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Significant Statistical Uncertainty over Share of High Net Worth Households

Author

Listed:
  • Christian Westermeier
  • Markus M. Grabka

Abstract

The analyses of wealth inequality based on survey data usually suffer from undercoverage of the upper percentiles of the very wealthy. Yet given this group’s substantial share of total net worth, it is of particular relevance. As no tax data are available in Germany, the largest fortunes can only be simulated using “rich lists.” For example, combining the Forbes list, with its approximately 50 German US dollar billionaires, with survey data results in an increased aggregate total net worth for all households in Germany in 2012 of between one-third and 50 percent, depending on the scenario. Moreover, the share of the richest one percent of the population (about 400,000 households) rises from approximately one-fifth to one-third. After reassessment, the richest ten percent of the population’s share of total net worth is estimated to be between 64 and 74 percent, depending on the scenario. These reassessments are characterized by a high degree of uncertainty which eventually can only be reduced by improving the base data.

Suggested Citation

  • Christian Westermeier & Markus M. Grabka, 2015. "Significant Statistical Uncertainty over Share of High Net Worth Households," DIW Economic Bulletin, DIW Berlin, German Institute for Economic Research, vol. 5(14/15), pages 210-219.
    • Handle: RePEc:diw:diwdeb:2015-14-3
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    File URL: https://www.diw.de/documents/publikationen/73/diw_01.c.500047.de/diw_econ_bull_2015-14-3.pdf
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    Citations

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    Cited by:

    1. Sierminska, Eva & Piazzalunga, Daniela & Grabka, Markus M., 2018. "Transitioning towards more equality? Wealth gender differences and the changing role of explanatory factors over time," GLO Discussion Paper Series 252, Global Labor Organization (GLO).
    2. Facundo Alvaredo & Anthony Atkinson & Lucas Chancel & Thomas Piketty & Emmanuel Saez & Gabriel Zucman, 2016. "Distributional National Accounts (DINA) Guidelines : Concepts and Methods used in WID.world," Working Papers halshs-02794308, HAL.
    3. Späth Jochen & Schmid Kai Daniel, 2018. "The Distribution of Household Savings in Germany," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 238(1), pages 3-32, February.
    4. Karla Cordova & Markus M. Grabka & Eva Sierminska, 2022. "Pension Wealth and the Gender Wealth Gap," European Journal of Population, Springer;European Association for Population Studies, vol. 38(4), pages 755-810, October.
    5. Thilo N. H. Albers & Charlotte Bartels & Moritz Schularick, 2020. "The Distribution of Wealth in Germany, 1895-2018," ECONtribute Policy Brief Series 001, University of Bonn and University of Cologne, Germany.
    6. Korom, Philipp, 2017. "Ungleiche Mittelschichten: Über Unterschiede im Immobilienvermögen und im Erbe innerhalb der Mitte Deutschlands," MPIfG Discussion Paper 17/14, Max Planck Institute for the Study of Societies.

    More about this item

    Keywords

    Wealth Inequality; pareto distribution; SOEP;
    All these keywords.

    JEL classification:

    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being

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