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The Impact of Top Incomes Biases on the Measurement of Inequality in the United States

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  • Vladimir Hlasny
  • Paolo Verme

Abstract

The paper assesses how top incomes biases affect the estimation of income inequality in the United States using the Current Population Survey (the March Annual Social and Economic Supplement) and two alternative correction methods – a stochastic approach based on reweighting and a semi‐parametric approach based on replacing observations. Consistently with previous studies, both methods and their joint application show that income inequality in the United States between 1979 and 2014 has been consistently underestimated by several percentage points. The level of underestimation is positively and significantly associated with mean income, non‐response rates and the initial level of inequality. Reweighting is found to address top incomes biases – specifically those related to unit and item non‐response – consistently and more effectively than replacing, possibly because widely used parametric distributions do not represent US top incomes accurately.

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  • 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.
    • Handle: RePEc:bla:obuest:v:84:y:2022:i:4:p:749-788
    DOI: 10.1111/obes.12472
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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. Brunori, Paolo & Salas-Rojo, Pedro & Verme, Paolo, 2022. "Estimating Inequality with Missing Incomes," GLO Discussion Paper Series 1138, Global Labor Organization (GLO).
    3. Emmanuel Flachaire & Nora Lustig & Andrea Vigorito, 2023. "Underreporting of Top Incomes and Inequality: A Comparison of Correction Methods using Simulations and Linked Survey and Tax Data," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 69(4), pages 1033-1059, December.
    4. 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.
    5. Nora Lustig, 2020. "The ``missing rich'' in household surveys: causes and correction approaches," Working Papers 520, ECINEQ, Society for the Study of Economic Inequality.
    6. 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.
    7. Lidia Ceriani & Paolo Verme, 2019. "The inequality of extreme incomes," Working Papers 490, ECINEQ, Society for the Study of Economic Inequality.
    8. Nora Lustig, 2019. "The “Missing Rich” in Household Surveys: Causes and Correction Approaches," Commitment to Equity (CEQ) Working Paper Series 75, Tulane University, Department of Economics.
    9. João Nicolau & Pedro Raposo & Paulo M. M. Rodrigues, 2023. "Measuring wage inequality under right censoring," Economic Inquiry, Western Economic Association International, vol. 61(2), pages 377-401, April.
    10. Mathias Silva, 2023. "Parametric models of income distributions integrating misreporting and non-response mechanisms," Working Papers hal-04093646, HAL.

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    More about this item

    JEL classification:

    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • N35 - Economic History - - Labor and Consumers, Demography, Education, Health, Welfare, Income, Wealth, Religion, and Philanthropy - - - Asia including Middle East

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