Pareto extrapolation: An analytical framework for studying tail inequality

B-Tier
Journal: Quantitative Economics
Year: 2023
Volume: 14
Issue: 1
Pages: 201-233

Authors (2)

Émilien Gouin‐Bonenfant (not in RePEc) Alexis Akira Toda (Emory University)

Score contribution per author:

1.005 = (α=2.01 / 2 authors) × 1.0x B-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

Abstract

We develop an analytical framework designed to solve and analyze heterogeneous‐agent models that endogenously 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 (e.g., 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.

Technical Details

RePEc Handle
repec:wly:quante:v:14:y:2023:i:1:p:201-233
Journal Field
General
Author Count
2
Added to Database
2026-01-29