Wang paper on minimum distance estimation of Pareto exponent published in J of Applied Econometrics
Jun 12, 2020
Alexis Akira Toda & Yulong Wang
Journal of Applied Econometrics, June 2020
The authors propose an efficient estimation method for the income Pareto exponent when only certain top income shares are observable. Their estimator is based on the asymptotic theory of weighted sums of order statistics and the efficient minimum distance estimator. Simulations show that their estimator has excellent finite-sample properties.
The authors apply their estimation method to U.S. 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.