方法对比

	Datt-Ravallion Decomposition	Shapley Decomposition of Inequality
领域	经济学	经济学
方法族	Process / pipeline	Process / pipeline
起源年份	1992	2013
提出者	Gaurav Datt & Martin Ravallion	Anthony Shorrocks (working paper 1999; published 2013)
类型	Poverty-change decomposition	Axiomatic decomposition procedure
开创性文献	Datt, G., & Ravallion, M. (1992). Growth and redistribution components of changes in poverty measures: a decomposition with applications to Brazil and India in the 1980s. Journal of Development Economics, 38(2), 275–295.	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	Growth-Redistribution Decomposition, Datt-Ravallion Method, Growth and Redistribution Components, Poverty Change Decomposition	Shapley Decomposition, Shorrocks Shapley Decomposition, Factor Decomposition of Inequality, Shapley Value Distributional Decomposition
相关	3	4
摘要	The Datt-Ravallion decomposition, introduced by Gaurav Datt and Martin Ravallion in 1992, separates the observed change in a poverty measure between two dates into a growth component — the change attributable to a shift in mean income holding the relative distribution fixed — and a redistribution component — the change attributable to a shift in the Lorenz curve holding mean income fixed. A residual captures the interaction between the two. It became the standard way to ask whether falling poverty was driven by rising average incomes or by changes in inequality, and underlies the empirical literature on pro-poor growth.	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.