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Traditionally, quantitative research on discrimination has focused on the measurement of direct bias, net of factors that may themselves be shaped by prior discrimination. This approach assumes that biases are additive and linearly separable, suggesting that intervening on the most biased decision makers is the most effective path toward equity. However, emerging economic research reconceptualizes “systemic discrimination” as an outcome of interdependent systems, where unjust effects compound through complex interactions (McMillon, 2023).
Under this conception, a key mechanism of systemic discrimination involves multi-stage decision-making, in which later decisions are influenced by earlier ones. This paper presents theoretical findings showing how sequential decision-making can amplify the long-term impact of initial discrimination. It shows how the total inequity faced by disadvantaged groups can be underestimated by traditional measurement methods that fail to account for the interdependence of sequential biased decision making. It also derives measurable conditions under which it is optimal to intervene on a less biased actor in an earlier stage, even when the later stage actor is far more biased. In effect, this research explores how to counteract systemic discrimination by leveraging its own structural dynamics. It extends the results to the general N-stage case, formalizing how the conclusions depend on the strength of the interdependence of the sequential decisions. Finally, the paper leverages empirical evidence using restricted-use criminal justice data on prosecutors and judges. However the results generalize to any multi-stage decision process, including those in health and education applications as well.