Pareto Extrapolation: An Analytical Framework for Studying Tail Inequality

78 Pages Posted: 22 Oct 2018 Last revised: 11 Jun 2020

See all articles by Emilien Gouin-Bonenfant

Emilien Gouin-Bonenfant

Columbia University

Alexis Akira Toda

University of California, San Diego (UCSD) - Department of Economics

Date Written: May 13, 2019

Abstract

We develop an analytical framework designed to solve and analyze heterogeneous-agent models that generate fat-tailed wealth distributions. We exploit the asymptotic linearity of policy functions and the analytical characterization of the Pareto exponent to augment the conventional solution algorithm with a theory of the tail. Our framework allows for a precise understanding of the very top of the wealth distribution (i.e., analytical expressions for top wealth shares, type distribution in the tail, and transition probabilities in and out of the tail) in addition to delivering improved accuracy and speed. We apply our framework to quantify the effect of a wealth tax on large fortunes in a general equilibrium model with borrowing constraint, portfolio choice, earnings risk, and return heterogeneity.

Keywords: Bewley-Huggett-Aiyagari model, Pareto exponent, power law, solution accuracy, wealth tax

JEL Classification: C63, D31, D58, E21

Suggested Citation

Gouin-Bonenfant, Emilien and Toda, Alexis Akira, Pareto Extrapolation: An Analytical Framework for Studying Tail Inequality (May 13, 2019). Available at SSRN: https://ssrn.com/abstract=3260899 or http://dx.doi.org/10.2139/ssrn.3260899

Emilien Gouin-Bonenfant

Columbia University ( email )

3022 Broadway
New York, NY 10027
United States

Alexis Akira Toda (Contact Author)

University of California, San Diego (UCSD) - Department of Economics ( email )

9500 Gilman Drive
Mail Code 0508
La Jolla, CA 92093-0508
United States

HOME PAGE: http://https://sites.google.com/site/aatoda111/

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
267
Abstract Views
1,511
rank
144,448
PlumX Metrics