Pareto Distribution of the Forbes Billionaires

Eugene Pinsky (), Weiqi Zhang () and Zibo Wang ()
Additional contact information
Eugene Pinsky: Metropolitan College
Weiqi Zhang: Metropolitan College
Zibo Wang: Metropolitan College

Computational Economics, 2025, vol. 66, issue 1, No 24, 809-834

Abstract: Abstract The Pareto distribution is commonly used to represent situations where a small portion of the population controls a disproportionately large share of resources, such as income or wealth distribution. Our study analyzed the Forbes Billionaire List from 2001 to 2023 by fitting it to a Pareto distribution using the Maximum Likelihood Estimation (MLE). Our results showed that the distribution parameter $$\alpha$$ α consistently ranged from 1.0 to 1.5. When the distribution parameter $$\alpha$$ α is less than 2, the underlying Pareto distribution has infinite variance, complicating the comparisons of deviations. To address this, we used Mean Absolute Deviation MAD (about median) as an alternative approach to estimate $$\alpha$$ α . Using MAD resulted in a three times lower root-mean-square error than using MLE. We considered MAD-based kurtosis and skewness by analogy with quantile statistics. We derived new interpretations for these measures in terms of areas of appropriately folded cumulative distribution functions. We applied this innovative approach to the Forbes Billionaire dataset, focusing on various segments, including continents, gender, and industries. We examined historical trends and considered future predictions. Our findings suggest that MAD is more effective for analyzing datasets that follow Pareto distributions.

Keywords: Forbes dataset; 80–20 rule; Predictive models; Statistical distribution analysis (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10614-024-10730-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:66:y:2025:i:1:d:10.1007_s10614-024-10730-1

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-024-10730-1

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().