류항국 교수

A bottom poor sensitive Gini coefficient (pgini) is defined by replacing income observations with their reciprocal values in the Gini coefficient. The underlying true income share function can be derived approximately using the maximum entropy method given the pgini coefficient.

Bentham (1789 Bentham, J. (1789). Utilitarianism, reproduced by Nabu Public Domain Reprints. Available at: http://www.publicdomainreprints.org. [Google Scholar]) introduced utility as the pursuit of happiness, with happiness defined in his philosophical view as existing if “pleasure” predominated over “pain.” This paper is the first to derive and compare the relative frequency distributions of income and utility in Bentham's classical sense. A utility-based Gini coefficient is formulated from a

The Gini coefficient is generally used to measure and summarize inequality over the entire income distribution function (IDF). Unfortunately, it is widely held that the Gini does not detect changes in the tails of the IDF particularly well. This paper introduces a new inequality measure that summarizes inequality well over the middle of the IDF and the tails simultaneously. We adopt an unconventional approach to measure inequality, as will be explained below, that better captures the level of in

Our friend and frequent collaborator Prof. Michael McAleer loved to enumerate lists and to give practical advice. Here, we present a review of 10 ways to derive the well-known Gini coefficient based on entropy measures. In fact, Mike was a collaborator on some of this work, as will be discussed in the paper. All are useful ways to combine two powerful tools, entropy measures and Gini coefficients to examine inequality in income distribution functions (IDFs) and can be applied to distributions of

Abstract Many well-known economic assumptions and desired restrictions can be readily established with the proper choice of a coordinate representation system. For example, the homotheticity assumption can be readily established with the polar coordinate system, the concavity restriction with the Muntz-Sartz series, the convexity restriction with the polynomial series, the orthogonality of regression functions with the orthonormal bases expansion, and the covariance stationary process with the F

Hang K. Ryu, Daniel J. Slottje, Coordinate Space versus Index Space Representations as Estimation Methods: An Application to How Macro Activity Affects the U.S. Income Distribution, Journal of Business & Economic Statistics, Vol. 12, No. 2 (Apr., 1994), pp. 243-251