Systemic Discrimination: Theory and Measurement

Economists often measure discrimination as disparities arising from the direct effects of group identity. We develop new tools to model and measure systemic discrimination, which instead captures how discrimination in other decisions indirectly contributes to disparities. We propose an experimental design, the Iterated Audit, to identify systemic discrimination. We then illustrate these new tools in two field experiments. The first experiment shows how racial discrimination accumulates 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 outline how our tools can be used to target policy interventions.