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The Weight of the Rich: Improving Surveys Using Tax Data

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  • Thomas Blanchet

    (PSE - Paris School of Economics - ENPC - École des Ponts ParisTech - ENS Paris - École normale supérieure - Paris - PSL - Université Paris sciences et lettres - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique - EHESS - École des hautes études en sciences sociales - 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 Paris - École normale supérieure - Paris - PSL - Université Paris sciences et lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, WIL - World Inequality Lab)

  • Ignacio Flores

    (WIL - World Inequality Lab)

  • Marc Morgan

    (PSE - Paris School of Economics - ENPC - École des Ponts ParisTech - ENS Paris - École normale supérieure - Paris - PSL - Université Paris sciences et lettres - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique - EHESS - École des hautes études en sciences sociales - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, WIL - World Inequality Lab)

Abstract

It is generally accepted that household surveys fail to accurately portray the top tail of the income distribution. Indeed, studies based on tax data challenge the credibility of surveys as a source to study inequality. To date, there is no broad consensus on how to best reconcile these two datasets. This paper presents a novel method that enables a consistent combination of these two sources of data, under the assumption that tax data sets a credible lower bound on the amount of people with given levels of income. The resulting micro-dataset preserves the consistency of other socio-demographic variables at both the individual and aggregate levels. It thus allows researchers to analyze dimensions of social inequality under a more representative distributive framework. Our procedure is illustrated by empirical applications to five countries, covering both developed and less-developed contexts over numerous years.

Suggested Citation

  • Thomas Blanchet & Ignacio Flores & Marc Morgan, 2018. "The Weight of the Rich: Improving Surveys Using Tax Data," Working Papers hal-02878315, HAL.
  • Handle: RePEc:hal:wpaper:hal-02878315
    Note: View the original document on HAL open archive server: https://hal-pse.archives-ouvertes.fr/hal-02878315
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    10. Rafael Carranza & Marc Morgan & Brian Nolan, 2021. "Top Income Adjustments and Inequality: An Investigation of the EU-SILC," Working Papers halshs-03321885, HAL.
    11. Bertrand Garbinti & Jonathan Goupille-Lebret & Thomas Piketty, 2021. "Accounting for Wealth-Inequality Dynamics: Methods, Estimates, and Simulations for France," Journal of the European Economic Association, European Economic Association, vol. 19(1), pages 620-663.
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    15. Milanovic, Branko, 2020. "After the financial crisis: the evolution of the global income distribution between 2008 and 2013," MPRA Paper 101560, University Library of Munich, Germany.
    16. Ranaldi, Marco, 2021. "Global Distributions of Capital and Labor Incomes: Capitalization of the Global Middle Class," SocArXiv 3g59r, Center for Open Science.
    17. Nora Lustig, 2020. "The ``missing rich'' in household surveys: causes and correction approaches," Working Papers 520, ECINEQ, Society for the Study of Economic Inequality.
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    19. Stefan Jestl & Emanuel List, 2020. "Distributional national accounts (DINA) for Austria 2004-2016," Working Paper Reihe der AK Wien - Materialien zu Wirtschaft und Gesellschaft 197, Kammer für Arbeiter und Angestellte für Wien, Abteilung Wirtschaftswissenschaft und Statistik.
    20. Marina Kunovac, 2020. "Distribution of household assets in Croatia," Public Sector Economics, Institute of Public Finance, vol. 44(3), pages 265-297.
    21. Khalid, Muhammed Abdul & Yang, Li, 2021. "Income inequality and ethnic cleavages in Malaysia: Evidence from distributional national accounts (1984–2014)," Journal of Asian Economics, Elsevier, vol. 72(C).
    22. Bartels, Charlotte & Waldenström, Daniel, 2021. "Inequality and top incomes," GLO Discussion Paper Series 959, Global Labor Organization (GLO).
    23. , Stone Center & Lustig, Nora, 2020. "The “Missing Rich” in Household Surveys: Causes and Correction Approaches," SocArXiv j23pn, Center for Open Science.
    24. 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.
    25. 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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