Working Paper: NBER ID: w16045
Authors: Nicole Fortin; Thomas Lemieux; Sergio Firpo
Abstract: This chapter provides a comprehensive overview of decomposition methods that have been developed since the seminal work of Oaxaca and Blinder in the early 1970s. These methods are used to decompose the difference in a distributional statistic between two groups, or its change over time, into various explanatory factors. While the original work of Oaxaca and Blinder considered the case of the mean, our main focus is on other distributional statistics besides the mean such as quantiles, the Gini coefficient or the variance. We discuss the assumptions required for identifying the different elements of the decomposition, as well as various estimation methods proposed in the literature. We also illustrate how these methods work in practice by discussing existing applications and working through a set of empirical examples throughout the paper.
Keywords: decomposition methods; wage inequality; economic growth; labor economics
JEL Codes: C14; C21; J31; J71
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| observable characteristics (C90) | wage outcomes (J31) |
| wage structure (J31) | wage outcomes (J31) |
| wage gap (J31) | composition effect (A30) |
| wage gap (J31) | wage structure effect (J31) |
| wage structure effect + composition effect (J31) | wage gap (J31) |
| observed covariates (C20) | treatment assignment (C90) |
| treatment assignment (C90) | potential outcomes (D79) |