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Simple adjustments of observed distributions for missing income and missing people

Author

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  • François Bourguignon

    (PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, PJSE - Paris Jourdan Sciences Economiques - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - INRA - Institut National de la Recherche Agronomique - EHESS - École des hautes études en sciences sociales - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique)

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.

Suggested Citation

  • François Bourguignon, 2018. "Simple adjustments of observed distributions for missing income and missing people," Post-Print halshs-01883896, HAL.
    • Handle: RePEc:hal:journl:halshs-01883896
    DOI: 10.1007/s10888-018-9388-8
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    Cited by:

    1. Ceriani, Lidia & Hlasny, Vladimir & Verme, Paolo, 2021. "Bottom Incomes and the Measurement of Poverty: A Brief Assessment of the Literature," GLO Discussion Paper Series 914, Global Labor Organization (GLO).
    2. Nicolas Frémeaux, 2023. "The More, the Better? Individual and Joint Interviewing in Surveys," Annals of Economics and Statistics, GENES, issue 149, pages 63-96.
    3. Katy Bergstrom & William Dodds & Nicholas Lacoste & Juan Rios, 2025. "Estimating the Welfare Cost of Labor Supply Frictions," Working Papers 2503, Tulane University, Department of Economics.
    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. Facundo Alvaredo & Mauricio de Rosa & Ignacio Flores & Marc Morgan, 2022. "The Inequality (or the Growth) we Measure: Data Gaps and the Distribution of Incomes," Working Papers halshs-03693223, HAL.
    6. Gonzalez Alvaredo, Facundo & De Rosa, Mauricio & Flores, Ignacio & Morgan, Marc, 2025. "The inequality (or the growth) we measure: data gaps and the distribution of incomes," LSE Research Online Documents on Economics 128509, London School of Economics and Political Science, LSE Library.
    7. Advani, Arun & Ooms, Tahnee & Summers, Andy, 2021. "Missing incomes in the UK: Evidence and policy implications," CAGE Online Working Paper Series 543, Competitive Advantage in the Global Economy (CAGE).
    8. 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.
    9. Mathias Silva, 2023. "Parametric models of income distributions integrating misreporting and non-response mechanisms," AMSE Working Papers 2311, Aix-Marseille School of Economics, France.
    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. 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.
    12. 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.
    13. 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.
    14. 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.
    15. Mathias Silva & Michel Lubrano, 2024. "Bayesian inference for income inequality using a Pareto II tail with an uncertain threshold: Combining EU-SILC and WID data," Working Papers hal-04759143, HAL.
    16. Fernando Antonio Ignacio González & Juan Antonio Dip, 2024. "Whom Is It Fair to Subsidise? Evidence from Argentina," Studies in Microeconomics, , vol. 12(3), pages 260-272, December.
    17. Ferreira, Francisco H. G. & Brunori, Paolo, 2024. "Inherited inequality, meritocracy, and the purpose of economic growth," LSE Research Online Documents on Economics 126263, London School of Economics and Political Science, LSE Library.
    18. 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.
    19. Frank Cowell & Emmanuel Flachaire, 2021. "Inequality Measurement: Methods and Data," Post-Print hal-03589066, HAL.

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