Efficient minimum distance estimation of Pareto exponent from top income shares
提出一种仅利用顶层收入份额数据估计收入帕累托指数的高效方法，模拟显示有限样本性质优良，应用于美国数据发现1985年以来帕累托指数在1.4至1.8之间波动，表明不平等加剧主要源于富人与穷人间的再分配。
Summary 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.