Is the wealth of the world’s billionaires not Paretian?

Capehart, Kevin W.

doi:10.1016/j.physa.2013.09.026

Cited by 10 publications

(8 citation statements)

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“…Note that combining survey with Forbes data raises the issue of measurement error in both datasets. Capehart () discusses measurement error problems in rich lists. In addition, a combination of both datasets in the regression method is only warranted under the assumption that both the sample and the rich list are consistent with the same Pareto distribution.…”

Section: A Pareto Law For the Tail Of The Wealth Distributionmentioning

confidence: 99%

How Fat is the Top Tail of the Wealth Distribution?

Vermeulen

2017

Review of Income and Wealth

153

163

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. The HFCN collects household-level data on households' finances and consumption in the euro area through a harmonised survey. The HFCN aims at studying in depth the micro-level structural information on euro area households' assets and liabilities. The objectives of the network are: Terms of use: Documents in1) understanding economic behaviour of individual households, developments in aggregate variables and the interactions between the two;2) evaluating the impact of shocks, policies and institutional changes on household portfolios and other variables;3) understanding the implications of heterogeneity for aggregate variables; 4) estimating choices of different households and their reaction to economic shocks; 5) building and calibrating realistic economic models incorporating heterogeneous agents; 6) gaining insights into issues such as monetary policy transmission and financial stability.The refereeing process of this paper has been co-ordinated by a team composed of Gabriel Fagan (ECB), Pirmin Fessler (Oesterreichische Nationalbank), Michalis Haliassos (Goethe University Frankfurt), Tullio Jappelli (University of Naples Federico II), Sébastien PérezDuarte (ECB), Jiri Slacalek (ECB), Federica Teppa (De Nederlandsche Bank), Peter Tufano (Oxford University) and Philip Vermeulen (ECB).The paper is released in order to make the results of HFCN research generally available, in preliminary form, to encourage comments and suggestions prior to final publication. The views expressed in the paper are the author's own and do not necessarily reflect those of the ESCB. AcknowledgementsI would like to thank the members of the HFCS network for many stimulating discussions. I also thank Jirka Slacalek and an anonymous referee for useful comments. Any remaining errors are solely mine. The views expressed in this paper only reflect those of the author. They do not necessarily reflect the views of the European Central Bank. Philip VermeulenEuropean Central Bank; e-mail: philip.vermeulen@ecb.europa.eu AbstractThe US Survey of Consumer Finances (SCF) and the Eurosystem's Household Finance and Consumption Survey (HFCS) provide evidence that wealth is heavily concentrated at the upper tail of the wealth distribution. A commonly cited number for the US is that 1 percent of the households hold 30 percent of total household wealth. I investigate the reliability of upper tail wealth estimates from household wealth surveys in the presence of survey differential non-response, i.e. ...

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Section: A Pareto Law For the Tail Of The Wealth Distributionmentioning

confidence: 99%

How Fat is the Top Tail of the Wealth Distribution?

Vermeulen

2017

Review of Income and Wealth

153

163

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“…Whether the Pareto Type 1 distribution provides satisfactory fit to wealth recorded in the Forbes rich lists is somewhat controversial; see Ogwang (), Brzezinski () and Capehart (). The debate revolves around the reliability of Kolmogorov–Smirnov type of goodness‐of‐fit tests when data are measured with error.…”

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confidence: 99%

Wealth Inequality: A Survey

Cowell

Kerm

2015

Journal of Economic Surveys

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“…Moreover, to make same basic induction about MLM estimates we used the same technique. Sampling was done from the original data (denoted as (1) in the tables) and then from the estimated models (denoted as (2) in the tables). Since data have finite variance and models do not, we were also looking for the difference in the estimated characteristics.…”

Section: Bootstrap Estimatesmentioning

confidence: 99%

“…95% C.I. (2) ζ Table 5 contains estimates of parameters obtained via MLM and calculated bootstrap confidence intervals both from the data and from the model. Shape parameter seems to be slightly overestimated, especially when compared to the bootstrap samples from the model, whereas opposite seems to be true for the scale parameterthe opposite of what have been seen in the MLE.…”

Section: Bootstrap Estimatesmentioning

confidence: 99%

“…With the one exception correlations are in the range from -0.55 to -0.62, that suggests low to moderate negative linear relationship between estimates of the parameters. (1) -0.5969 Inter-sample MLE bootstrap (1) -0.6184 Mean MLE bootstrap (2) -0.5553 Inter-sample MLE bootstrap (2) -0.5545 Inter-sample MLM bootstrap (1) -0.4390 Inter-sample MLM bootstrap (2) -0.6191 Source: the authors. Figure 8 contains estimated correlations from the bootstraps from the sample and from the model respectively.…”

Section: Bootstrap Estimatesmentioning

confidence: 99%

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Distribution of the wealth of the richest persons in the world

Čabla¹,

Habarta²

2019

Proceedings of the 22nd International Scientific Conference on Applications of Mathematics and Statistics in Economics (AMSE 20

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The aim of this paper is to examine the probability distribution of wealth of the richest persons in the world based on estimates from the CEOWORLD magazine's rich list for March 2019. Since one can safely assume that there are a tiny number of people out of the whole world's population in this list, we basically deal with the very right tail of the wealth distribution, which should according to the Pickands-Balkema-de Haan theorem follow a generalized Pareto distribution. We discuss in this paper not only different estimates of this distribution with an emphasis on the shape parameter per se but also their behavior throughout bootstrap samples. Among the main findings is the observation based on the maximum to sum plot and parametric estimates that there is high probability of infinite variance. This could have a serious impact on estimates of inequality measures. The whole distribution follows nearly a Pareto distribution, whereas the very right tail is closer to an exponential. The bootstrap technique confirms that maximum likelihood estimates are almost normally distributed, but they overestimate variance. Estimates via the method of L-moments diverge from the normal distribution. The correlation of the parameter estimates is moderately negative, which is demonstrated in a simultaneous confidence region.

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Is the wealth of the world’s billionaires not Paretian?

Cited by 10 publications

References 15 publications

How Fat is the Top Tail of the Wealth Distribution?

How Fat is the Top Tail of the Wealth Distribution?

Wealth Inequality: A Survey

Distribution of the wealth of the richest persons in the world

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