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Correcting wealth survey data for the missing rich: The case of Austria

Author

Listed:
  • Paul Eckerstorfer
  • Johannes Halak

    (Government of the Federal State of Upper Austria)

  • Jakob Kapeller

    () (Department of Philosophy and Theory of Science, University of Linz, Linz, Austria)

  • Bernhard Schütz

    () (Department of Economics, University of Linz, Linz, Austria)

  • Florian Springholz

    (Department of Philosophy and Theory of Science, University of Linz, Linz, Austria)

  • Rafael Wildauer

    (Department of Economics, Kingston University London)

Abstract

It is a well-known criticism that due to its exponential distribution, survey data on wealth is hardly reliable when it comes to analyzing the richest parts of society. This paper addresses this criticism using Austrian data from the Household Finance and Consumption Survey (HFCS). In doing so we apply the assumption of a Pareto distribution to obtain estimates for the number of households possessing a net wealth greater than four million Euros as well as their aggregate wealth holdings. Thereby, we identify suitable parameter combinations by using a series of maximum-likelihood estimates and appropriate goodness-of-fit tests to avoid arbitrariness with respect to the fitting of the Pareto-Distribution. Our results suggest that the alleged non-observation bias is considerable, accounting for about one quarter of total net wealth. The method developed in this paper can easily be applied to other countries where survey data on wealth are available.

Suggested Citation

  • Paul Eckerstorfer & Johannes Halak & Jakob Kapeller & Bernhard Schütz & Florian Springholz & Rafael Wildauer, 2014. "Correcting wealth survey data for the missing rich: The case of Austria," Economics working papers 2014-01, Department of Economics, Johannes Kepler University Linz, Austria.
  • Handle: RePEc:jku:econwp:2014_01
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    File URL: http://www.econ.jku.at/papers/2014/wp1401.pdf
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    References listed on IDEAS

    as
    1. Camillo Dagum, 2006. "Wealth distribution models: analisys and applications," Statistica, Department of Statistics, University of Bologna, vol. 66(3), pages 235-268.
    2. Anthony B. Atkinson & Thomas Piketty & Emmanuel Saez, 2011. "Top Incomes in the Long Run of History," Journal of Economic Literature, American Economic Association, vol. 49(1), pages 3-71, March.
    3. Peter Hoeller & Isabelle Joumard & Mauro Pisu & Debra Bloch, 2012. "Less Income Inequality and More Growth – Are They Compatible? Part 1. Mapping Income Inequality Across the OECD," OECD Economics Department Working Papers 924, OECD Publishing.
    4. Robert Guttmann & Dominique Plihon, 2010. "Consumer debt and financial fragility," International Review of Applied Economics, Taylor & Francis Journals, vol. 24(3), pages 269-283.
    5. Stanislav Kolenikov, 2010. "Resampling variance estimation for complex survey data," Stata Journal, StataCorp LP, vol. 10(2), pages 165-199, June.
    6. Facundo Alvaredo & Emmanuel Saez, 2009. "Income and Wealth Concentration in Spain from a Historical and Fiscal Perspective," Journal of the European Economic Association, MIT Press, vol. 7(5), pages 1140-1167, September.
    7. Kopczuk, Wojciech & Saez, Emmanuel, 2004. "Top Wealth Shares in the United States, 1916-2000: Evidence From Estate Tax Returns," National Tax Journal, National Tax Association;National Tax Journal, vol. 57(2), pages 445-487, June.
    8. Wilkinson, Richard G. & Pickett, Kate E., 2007. "The problems of relative deprivation: Why some societies do better than others," Social Science & Medicine, Elsevier, vol. 65(9), pages 1965-1978, November.
    9. Facundo Alvaredo & Anthony B. Atkinson & Thomas Piketty & Emmanuel Saez, 2013. "The Top 1 Percent in International and Historical Perspective," Journal of Economic Perspectives, American Economic Association, vol. 27(3), pages 3-20, Summer.
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    More about this item

    Keywords

    wealth distribution; non-observation bias; Pareto distribution;

    JEL classification:

    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution

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