Disparate Treatment: Theories and Evidence

种族和性别不平等一直处于美国社会和文化话语的前沿超过半个世纪。这项研究有两个不同的项目，有助于更深入地了解不同的待遇的来源，以及如何补救它。第一个项目研究执法背景下的不同待遇。以前关于执法的研究侧重于根据可观察到的特征解释歧视，但本项目表明，待遇上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，在该模型中，最小化犯罪可能需要在一组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，受到更密集警务的个人的身份可能会随着时间的推移而轮换，因此，随着时间的推移，警务的总体影响是平均分布的。这一理论有助于更全面地理解公平与效率之间的权衡。该分析也适用于税务审计等其他背景，并提供了比较静态的结果。这一理论被用来分析高速公路超速数据，并计算警察资源边际增加的效益，即挽救生命。作为该项目的一部分开发的建模方法和产生的估计将有助于分析减少不安全驾驶和事故的政策的有效性。第二个项目涉及匹配互补性，指导和榜样效应。这些影响常常被用来为扶持行动提供理由（扶持行动本身就是一种区别对待的形式）。虽然在教育学、社会学、经济学和心理学中有很多关于指导和榜样效应的讨论，但这些效应已证明难以定量证明，部分原因是缺乏详细的数据来控制匹配过程的内在性质。本项目将研究高等教育背景下的匹配效应。一个数据集的构建，可以估计配对的学生和教师的种族，民族和性别的好处。估计方法利用随机化的过程中，学生被分配给教师。通过收集关于学生和教师之间互动类型的全面数据，该项目还提供了对指导和榜样效应发生渠道的见解。第二个项目的目标是在坚实的实验基础上建立指导和榜样效应的概念。 这项研究将提供一个更好的理解不同的治疗效果。

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.