Decomposing the Composition Effect: The Role of Covariates in Determining Between-Group Differences in Economic Outcomes
利用copula理论将构成效应分解为协变量的直接贡献、交互效应和依赖效应，并应用于1985-2005年美国工资数据，发现依赖效应可解释约五分之一工资不平等增幅。
In this article, we study the structure of the composition effect, which is the part of the observed between-group difference in the distribution of some economic outcome that can be explained by differences in the distribution of covariates. Using results from copula theory, we derive a new representation that contains three types of components: (i) the "direct contribution" of each covariate due to between-group differences in the respective marginal distributions, (ii) several "two-way" and "higher-order interaction effects" due to the interplay between two or more marginal distributions, and (iii) a "dependence effect" accounting for between-group differences in dependence patterns among the covariates. We show how these components can be estimated in practice, and use our method to study the evolution of the wage distribution in the United States between 1985 and 2005. We obtain some new and interesting empirical findings. For example, our estimates suggest that the dependence effect alone can explain about one-fifth of the increase in wage inequality over that period (as measured by the difference between the 90% and the 10% quantile).