Maximum entropy estimation of income distributions from Basmann’s weighted geometric mean measure
2017
Ryu, Hang K. | Slottje, Daniel J.
This paper introduces a new Maximum Entropy based inequality measure that is related to Basmann’s class of weighted geometric mean (WGM) measures, but with the added feature that the new measure is flexible enough to describe other characteristics of an observed income distribution function (IDF), a feature that other well-known measures do not possess. As an application, using Current Population Survey (CPS) data, we apply the new measure to Blinder and Esaki’s (1978) aggregate macro-modeling approach to examine US income inequality trends from 1947 to 2014. Increases in the unemployment rate and decreases in inflation rates and in the growth rate in gross domestic product (GDP) were found to deepen income inequality; rising inequality is a recent trend many policymakers have been watching with concern.
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