# Efficient Minimum Distance Estimation of Pareto Exponent from Top Income Shares

## TL;DR

This paper introduces an efficient method to estimate the Pareto exponent from limited top income share data, revealing stability in income inequality measures over decades and suggesting redistribution as the main driver.

## Contribution

It develops a novel minimum distance estimator based on order statistics for Pareto exponents using limited data, improving estimation efficiency.

## Key findings

- Pareto exponent estimated around 1.5 since 1985
- Income inequality rise driven by redistribution, not among the rich
- Estimator shows excellent finite sample properties

## 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.