Efficient minimum distance estimation of Pareto exponent from top income shares

Alexis Akira Toda, Yulong Wang

Research output: Contribution to journalArticle

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 US top income share data and find that the Pareto exponent has been ranging between 1.4 and 1.8 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.

Original languageEnglish (US)
JournalJournal of Applied Econometrics
DOIs
StateAccepted/In press - 2020

ASJC Scopus subject areas

  • Social Sciences (miscellaneous)
  • Economics and Econometrics

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