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Heavy-tailed distributions and the distribution of wealth: Evidence from rich lists in Canada, 1999–2017

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  • Campolieti, Michele

Abstract

I examine whether a power law distribution fits the top wealth distribution, i.e., the “Rich 100”, from Canada. I use a maximum likelihood framework that considers the fit of the estimated distribution as well as allowing tests against alternative heavy-tailed and skewed distributions. I find that while a power law distribution does fit the data well it is not possible to distinguish it from a lognormal distribution truncated to the upper tail.

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  • Campolieti, Michele, 2018. "Heavy-tailed distributions and the distribution of wealth: Evidence from rich lists in Canada, 1999–2017," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 263-272.
    • Handle: RePEc:eee:phsmap:v:503:y:2018:i:c:p:263-272
    DOI: 10.1016/j.physa.2018.02.057
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    References listed on IDEAS

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    Cited by:

    1. Safari, Muhammad Aslam Mohd & Masseran, Nurulkamal & Ibrahim, Kamarulzaman & AL-Dhurafi, Nasr Ahmed, 2020. "The power-law distribution for the income of poor households," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    2. Tomaschitz, Roman, 2020. "Multiply broken power-law densities as survival functions: An alternative to Pareto and lognormal fits," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    3. Campolieti, Michele & Ramos, Arturo, 2021. "The distribution of strike size: Empirical evidence from Europe and North America in the 19th and 20th centuries," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    4. Safari, Muhammad Aslam Mohd & Masseran, Nurulkamal & Ibrahim, Kamarulzaman & Hussain, Saiful Izzuan, 2021. "Measuring income inequality: A robust semi-parametric approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 562(C).

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