Efficient Minimum Distance Estimation of Pareto Exponent from Top Income Shares
Statistics Theory
2020-07-23 v2 General Economics
Economics
Applications
Statistics Theory
Abstract
We propose an efficient estimation method for the income Pareto exponent when only certain top income shares are observable. Our estimator is based on the asymptotic theory of weighted sums of order statistics and the efficient minimum distance estimator. Simulations show that our estimator has excellent finite sample properties. We apply our estimation method to U.S. top income share data and find that the Pareto exponent has been stable at around 1.5 since 1985, suggesting that the rise in inequality during the last three decades is mainly driven by redistribution between the rich and poor, not among the rich.
Keywords
Cite
@article{arxiv.1901.02471,
title = {Efficient Minimum Distance Estimation of Pareto Exponent from Top Income Shares},
author = {Alexis Akira Toda and Yulong Wang},
journal= {arXiv preprint arXiv:1901.02471},
year = {2020}
}