Disparate Treatment: Theories and Evidence

半个多世纪以来，种族和性别差异一直是美国社会和文化话语的前沿。这项研究有两个不同的项目，有助于更深入地了解差别待遇的来源以及如何补救。第一个项目研究执法背景下的差别待遇。以往关于执法的研究侧重于根据可观察到的特征来解释歧视，但本项目表明，待遇的差异不一定与可观察到的差异有关。该研究开发了一个理论模型，在这个模型中，最小化犯罪可能需要在一群明显相同的个体中对一个子群体进行更严格的监管。分析表明，受到更严格监管的个人身份可能会随着时间的推移而轮换，因此随着时间的推移，监管的总体影响是平均分配的。这一理论有助于更全面地理解公平与效率之间的取舍关系。该分析也适用于其他设置，如税务审计，并提供比较静态结果。该理论被用于分析高速公路超速的数据，并计算警察资源边际增加所能挽救的生命的收益。作为该项目的一部分而开发的建模方法和估算结果将有助于分析减少不安全驾驶和事故的政策有效性。第二个项目涉及匹配互补、指导和榜样作用。这些影响常常被用来为平权行动（平权行动本身就是一种差别待遇）提供理由。虽然在教育、社会学、经济学和心理学中对师徒关系和榜样效应进行了很多讨论，但事实证明，这些效应很难定量地证明，部分原因是缺乏详细的数据来控制匹配过程的内生性质。本项目将研究高等教育背景下的配对效应。构建了一个数据集，可以根据种族、民族和性别来估计学生和教师配对的好处。估计方法在学生被分配给教师的过程中利用了随机化。通过收集有关学生和教师之间互动类型的综合数据，该项目还提供了对指导和榜样效应发生的渠道的见解。第二个项目的目标是在坚实的实验基础上建立指导和角色榜样效应的概念。这项研究将有助于更好地理解差别待遇的影响。

Racial and gender disparities have been at the forefront of social and cultural discourse in the US for over half a century. This research has two distinct projects that contribute to a deeper understanding of the sources of disparate treatment and how to remedy it. The first project studies disparate treatment in the context of law enforcement. Previous research on law enforcement focuses on explaining discrimination based on observable characteristics, but this project shows that disparities in treatment need not be related to observable differences. The research develops a theoretical model in which minimizing crime may require a subgroup be policed more intensely within a group of observably identical individuals. The analysis suggests that the identity of individuals subjected to more intense policing may be rotated over time, so that over time the overall impact of policing is distributed equally. This theory can help achieve a more comprehensive understanding of the trade-off between fairness and efficiency. The analysis is also applicable to other settings such as tax auditing, and it affords comparative-statics results. The theory is used to analyze data on highway speeding and to compute the benefit, in terms of lives saved, of a marginal increase in police resources. The modeling methods developed as part of this project and the estimates produce will be useful for analyzing the effectiveness of policies that reduce unsafe driving and therefore accidents.The second project concerns match complementarities, mentoring and role model effects. These effects are often used to provide a rationale for affirmative action (itself a form of disparate treatment). While there has been much discussion in education, sociology, economics and psychology about mentoring and role model effects, these effects have proved difficult to demonstrate quantitatively partly to lack of detailed data to control for the endogenous nature of the matching process. This project will study matching effects in the context of higher education. A data set is constructed that allows estimation of the benefit of pairing students and instructors by race, ethnicity and gender. The estimation approach takes advantage of randomization in the process by which students are assigned to instructors. By gathering comprehensive data on the types of interactions that take place between students and instructors, the project also provides insights into the channels through which mentoring and role model effects take place. The goal of this second project is to establish the notion of mentoring and role model effects on a firm experimental basis. This research will provide a better understanding of the effects of disparate treatment.