方法对比

	Theil Inequality Decomposition	Shapley Decomposition of Inequality
领域	经济学	经济学
方法族	Process / pipeline	Process / pipeline
起源年份	1967	2013
提出者	Henri Theil (1967); decomposition class by Anthony Shorrocks (1980)	Anthony Shorrocks (working paper 1999; published 2013)
类型	Decomposable inequality measure	Axiomatic decomposition procedure
开创性文献	Theil, H. (1967). Economics and Information Theory. Amsterdam: North-Holland. ISBN: 9780444814630	Shorrocks, A. F. (2013). Decomposition procedures for distributional analysis: a unified framework based on the Shapley value. Journal of Economic Inequality, 11(1), 99–126.
别名	Theil Index, Theil's T and L, Generalized Entropy Decomposition, Within-Between Inequality Decomposition	Shapley Decomposition, Shorrocks Shapley Decomposition, Factor Decomposition of Inequality, Shapley Value Distributional Decomposition
相关	3	4
摘要	The Theil index, introduced by Henri Theil in 1967 by importing Shannon's information theory into economics, measures income inequality as the divergence between each unit's income share and its population share. Its defining advantage is exact additive decomposability: total inequality splits cleanly into a within-group component (inequality inside each subgroup) and a between-group component (inequality between subgroup means). Theil's T and its companion L (mean log deviation) are the two best-known members of the generalized-entropy class, which Anthony Shorrocks showed in 1980 to be the only inequality measures that are additively decomposable in this way.	The Shapley decomposition, formalized for distributional analysis by Anthony Shorrocks (in a widely circulated 1999 working paper, published in 2013), is a general procedure for attributing an inequality or poverty statistic to its contributing factors — income sources, population subgroups, or determinants. It borrows the Shapley value from cooperative game theory: each factor's contribution is its average marginal effect on the indicator across all possible orders in which factors could be eliminated. The result is an exact, symmetric, residual-free decomposition that applies to any indicator, even those (like the Gini) that have no natural analytic decomposition of their own.