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A Comment on Fitting Pareto Tails to Complex Survey Data

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  • Wildauer, Rafael
  • Kapeller, Jakob

Abstract

Taking survey data on household wealth as our major example, this short paper discusses some of the issues applied researchers are facing when fitting (type I) Pareto distributions to complex survey data. The major contribution of this paper is twofold: First, we provide a novel take on key aspects of Pareto tail fitting and a new and easy way of implementing the latter. Second, we summarise key results on goodness of fit tests in the context of complex survey data. Taken together we think the paper provides a concise and useful presentation of the fundamentals of Pareto tail fitting with complex survey data.

Suggested Citation

  • Wildauer, Rafael & Kapeller, Jakob, 2019. "A Comment on Fitting Pareto Tails to Complex Survey Data," Greenwich Papers in Political Economy 26009, University of Greenwich, Greenwich Political Economy Research Centre.
    • Handle: RePEc:gpe:wpaper:26009
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    File URL: http://gala.gre.ac.uk/id/eprint/26009/7/26009%20WILDAUER_A_Comment_On_Fitting_Pareto_Tails_To_Complex_Survey_Data_%28Draft%29_2019.pdf
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    References listed on IDEAS

    as
    1. Wildauer, Rafael & Kapeller, Jakob, 2019. "Rank Correction: A New Approach to Differential Nonresponse in Wealth Survey Data," Greenwich Papers in Political Economy 26010, University of Greenwich, Greenwich Political Economy Research Centre.
    2. Xavier Gabaix & Rustam Ibragimov, 2011. "Rank - 1 / 2: A Simple Way to Improve the OLS Estimation of Tail Exponents," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(1), pages 24-39, January.
    3. Jayadev, Arjun, 2008. "A power law tail in India's wealth distribution: Evidence from survey data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(1), pages 270-276.
    4. Bach, Stefan & Thiemann, Andreas & Zucco, Aline, 2019. "Looking for the missing rich: tracing the top tail of the wealth distribution," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 26(6), pages 1234-1258.
    5. Jakob Kapeller & Rafael Wildauer, 2019. "Rank Correction: A New Approach to Differential Non-Response in Wealth Survey Data," ICAE Working Papers 101, Johannes Kepler University, Institute for Comprehensive Analysis of the Economy.
    6. Philip Vermeulen, 2018. "How Fat is the Top Tail of the Wealth Distribution?," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 64(2), pages 357-387, June.
    7. Gabaix, Xavier & Ibragimov, Rustam, 2011. "Rank − 1 / 2: A Simple Way to Improve the OLS Estimation of Tail Exponents," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 24-39.
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    Cited by:

    1. Rafael Wildauer & Jakob Kapeller, 2019. "Rank correction: a new approach to differential nonresponse in wealth survey data," Working Papers PKWP1921, Post Keynesian Economics Society (PKES).
    2. Jakob Kapeller & Rafael Wildauer, 2019. "Rank Correction: A New Approach to Differential Non-Response in Wealth Survey Data," ICAE Working Papers 101, Johannes Kepler University, Institute for Comprehensive Analysis of the Economy.
    3. Wildauer, Rafael & Kapeller, Jakob, 2022. "Tracing the invisible rich: A new approach to modelling Pareto tails in survey data," Labour Economics, Elsevier, vol. 75(C).

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    More about this item

    Keywords

    pareto distribution; complex survey data; wealth distribution;
    All these keywords.

    JEL classification:

    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution

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