scholarly journals Chasing the Tail: A Generalized Pareto Distribution Approach to Estimating Wealth Inequality

2021 ◽  
Author(s):  
Arthur B. Kennickell
Keyword(s):  
Pareto Distribution ◽  
Wealth Distribution ◽  
Estimation Method ◽  
Generalized Pareto Distribution ◽  
Wealth Inequality ◽  
Household Wealth ◽  
National Accounts ◽  
Working Paper ◽  
External Data ◽  
Generalized Pareto

Since the work reported in Vermeulen [2018], a literature has developed on using the simple Pareto distribution along with “rich list” information to make improved estimates of the upper tail of the wealth distribution measured in surveys. Because the construction of such external data is typically opaque and subject to potentially serious measurement error, it may be best not to depend exclusively on this approach. This paper develops an alternative approach, using the generalized Pareto distribution (GPD), of which the simple Pareto is a subset, extending an estimation strategy developed by Castillo and Hadi [1997]. The greater flexibility of the GPD allows the possibility of modeling the tail of the wealth distribution, using a larger set of data for support than is typically the case with the simple Pareto. Moreover, the elaboration of the estimation method presented here allows explicitly for the possibility that the extreme of the observed upper tail is measured with error or that it is not captured at all. The approach also allows the incorporation of external data on total wealth as a constraint on the estimation. For the applications considered here using Austrian and U.S. micro data, the model relies on an estimate of total household wealth from national accounts, rather than rich-list information. The results suggest that where sufficiently comparable and reliable estimates of aggregate wealth are available, this approach can provide a useful way of mitigating problems in comparing distributional estimates across surveys that differ meaningfully in their effective coverage of the upper tail of the wealth distribution. The approach may be particularly useful in the construction of distributional national accounts. (Stone Center on Socio-Economic Inequality Working Paper)

scholarly journals Intergenerational Transfers by Size and Wealth Inequality in Rich Countries

2020 ◽  
Author(s):  
Brian Nolan ◽  
Juan Palomino ◽  
Philippe Van Kerm ◽  
Salvatore Morelli
Keyword(s):  
Wealth Distribution ◽  
Intergenerational Transfers ◽  
Wealth Inequality ◽  
Household Wealth ◽  
Working Paper ◽  
Gender Education ◽  
The Us ◽  
The Uk ◽  
Rich Countries ◽  
The Impact

This paper uses household wealth surveys to compare patterns of intergenerational wealth transfers across six rich countries and assess the relationships between transfers, current levels of net wealth, and wealth inequality. The paper examines four Euro Area countries, France, Germany, Italy, and Spain and extends the systematic comparison to the US and the UK. It finds that many of those currently at the top of the wealth distribution did not benefit from intergenerational transfers, but those who did received particularly large amounts while those toward the bottom of the wealth distribution received very little. A substantial gap in net wealth is seen between those who received or did not receive some wealth transfer. Controlling for age, gender, education and household size reduces the size of that gap but it remains substantial, especially in the US. We further look at how a marginal increase in the proportion of recipients of transfers of differing sizes would contribute to the shape of the overall wealth distribution using influence function regressions. Crucially, we show that the impact depends not only on the locations in the wealth distributions of recipients versus non-recipients, but also on the size of the receipt, an aspect which has been overlooked to date. In most countries, increasing the proportion of recipients of large transfers generally increases overall wealth inequality. In contrast, having more recipients of small or medium- sized transfers would be expected to reduce wealth inequality modestly, as they are more concentrated around the middle of the wealth distribution than non-recipients. (Stone Center on Socio-Economic Inequality Working Paper)

scholarly journals Is the Top Tail of the Wealth Distribution the Missing Link between the Household Finance and Consumption Survey and National Accounts?

2019 ◽  
Vol 35 (1) ◽  
pp. 31-65 ◽  
Author(s):  
Robin Chakraborty ◽  
Ilja Kristian Kavonius ◽  
Sébastien Pérez-Duarte ◽  
Philip Vermeulen
Keyword(s):  
Pareto Distribution ◽  
Wealth Distribution ◽  
Household Finance ◽  
Household Wealth ◽  
National Accounts ◽  
Missing Link ◽  
System Of National Accounts ◽  
Household Sector ◽  
Consumption Survey ◽  
Asset Holdings

Abstract The financial accounts of the household sector within the system of national accounts report the aggregate asset holdings and liabilities of all households within a country. In principle, when household wealth surveys are explicitly designed to be representative of all households, aggregating these microdata should correspond to the macro-aggregates. In practice, however, differences are large. We first discuss conceptual and generic differences between those two sources of data. Thereafter, we investigate missing top tail observation from wealth surveys as a source of discrepancy. By fitting a Pareto distribution to the upper tail, we provide an estimate of how much of the gap between the micro- and macrodata is caused by the underestimation of the top tail of the wealth distribution. Conceptual and generic differences, as well as missing top tail observations, explain part of the gap between financial accounts and survey aggregates.

Inference on P(Y

2020 ◽  
Vol 72 (2) ◽  
pp. 89-110
Author(s):  
Manoj Chacko ◽  
Shiny Mathew
Keyword(s):  
Maximum Likelihood ◽  
Pareto Distribution ◽  
Generalized Pareto Distribution ◽  
Maximum Likelihood Estimators ◽  
Record Values ◽  
Asymptotic Distributions ◽  
Bayes Estimators ◽  
Generalized Pareto ◽  
Pareto Distributions ◽  
Generalized Pareto Distributions

In this article, the estimation of [Formula: see text] is considered when [Formula: see text] and [Formula: see text] are two independent generalized Pareto distributions. The maximum likelihood estimators and Bayes estimators of [Formula: see text] are obtained based on record values. The Asymptotic distributions are also obtained together with the corresponding confidence interval of [Formula: see text]. AMS 2000 subject classification: 90B25

scholarly journals The Gamma Generalized Pareto Distribution with Applications in Survival Analysis

2017 ◽  
Vol 6 (3) ◽  
pp. 141 ◽  
Author(s):  
Thiago A. N. De Andrade ◽  
Luz Milena Zea Fernandez ◽  
Frank Gomes-Silva ◽  
Gauss M. Cordeiro
Keyword(s):  
Monte Carlo Simulation ◽  
Pareto Distribution ◽  
Generalized Pareto Distribution ◽  
Real Data ◽  
Model Parameters ◽  
Monte Carlo Simulation Study ◽  
Lorenz Curves ◽  
Generalized Pareto ◽  
Mathematical Properties ◽  
Pareto Model

We study a three-parameter model named the gamma generalized Pareto distribution. This distribution extends the generalized Pareto model, which has many applications in areas such as insurance, reliability, finance and many others. We derive some of its characterizations and mathematical properties including explicit expressions for the density and quantile functions, ordinary and incomplete moments, mean deviations, Bonferroni and Lorenz curves, generating function, R\'enyi entropy and order statistics. We discuss the estimation of the model parameters by maximum likelihood. A small Monte Carlo simulation study and two applications to real data are presented. We hope that this distribution may be useful for modeling survival and reliability data.

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