Entropy based European income distributions and inequality measures

被引:4
|
作者
Villas-Boas, Sofia B. [1 ]
Fu, Qiuzi [2 ]
Judge, George [3 ,4 ]
机构
[1] Univ Calif Berkeley, ARE, Berkeley, CA 94720 USA
[2] Peking Univ, Natl Sch Dev, Beijing 100871, Peoples R China
[3] Univ Calif Berkeley, Grad Sch, 207 Giannini Hall, Berkeley, CA 94720 USA
[4] Univ Calif Berkeley, Giannini Fdn, 207 Giannini Hall, Berkeley, CA 94720 USA
关键词
Income probability distribution function; Micro income data; Information theoretic methods; Cressie-Read divergence; Entropy maximization; Pareto's law; (PDFs);
D O I
10.1016/j.physa.2018.09.121
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
In this paper, instead of likelihood based methods that are fragile under model uncertainty, we use entropy based methods on time-ordered household income data to recover income distribution information on European countries and obtain an inequality income measure. For information recovery, we use a family of information theoretic entropy divergence measures to obtain income probability density functions and the corresponding inequality measures, which reflect how European country based economic behavioral systems are performing, and in terms of dynamics have changed over time. (C) 2018 Elsevier B.V. All rights reserved.
引用
收藏
页码:686 / 698
页数:13
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