The Kitagawa–Oaxaca–Blinder decomposition approach has been widely used to attribute group-level differences in an outcome to differences in endowment, coefficients, and their interactions. The method has been implemented for Stata in the popular oaxaca command for cross-sectional analyses. In recent decades, however, research questions have been more often focused on the decomposition of group-based differences in change over time, for example, diverging income trajectories, as well as decomposition of change in differences between groups, for example, change in the gender pay gap over time. We review five existing methods for the decomposition of changes in group means and contribute an extension that takes an interventionist perspective suitable for applications with a clear before–after comparison.

These decompositions of levels and changes over time can be implemented using the xtoaxaca command, which works as a postestimation command for different regression commands in Stata. It is built to maximize flexibility in modeling and implements all decomposition techniques presented in this article.

Kitagawa-Oaxaca-Blinder 分解方法已被广泛用于将结果中的组级差异归因于禀赋、系数及其相互作用的差异。该方法已在用于横截面分析的流行oaxaca命令中为Stata 实施。然而，近几十年来，研究问题更多地集中在基于群体的差异随时间变化的分解上，例如不同的收入轨迹，以及群体间差异的分解，例如，随着时间的推移，性别工资差距。我们回顾了用于分解群体均值变化的五种现有方法，并提供了一种扩展，该方法采用适合于具有明确前后比较的应用的干预主义视角。

这些级别和随时间变化的分解可以使用xtoaxaca命令来实现，该命令用作 Stata 中不同回归命令的后估计命令。它旨在最大限度地提高建模的灵活性，并实现本文中介绍的所有分解技术。