Systemic Discrimination: Theory and Measurement
提出测量系统性歧视的新工具，通过迭代审计实验识别间接歧视，并在两个田野实验中展示种族和性别歧视如何通过经验积累和推荐信语言等渠道累积。
Abstract Economists often measure discrimination as disparities arising from the direct effects of group identity. We develop new tools to model and measure systemic discrimination, capturing how discrimination in other decisions indirectly contributes to disparities. A novel experimental design, the iterated audit, identifies systemic discrimination. We illustrate these new tools in two field experiments. The first experiment shows how racial discrimination can accumulate across multiple rounds of hiring through the interaction of two forces: greater discrimination against inexperienced workers, which affects the opportunity to obtain experience, and high subsequent returns to experience. The second experiment shows how gender-based differences in the language of recommendation letters can translate into systemic gender discrimination in STEM hiring. We discuss how our findings qualify previous results on direct discrimination and how our tools can be used to target policy interventions.