We describe and apply three empirical approaches to identify superfluous police activity, unjustified racially disparate impacts, and limits to regulatory interventions. First, using cost-benefit analysis, we show that traffic and pedestrian stops in Nashville and New York City disproportionately impacted communities of color without achieving their stated public-safety goals. Second, we address a longstanding problem in discrimination research by presenting an empirical approach for identifying “similarly situated” individuals and, in so doing, quantify potentially unjustified disparities in stop policies in New York City and Chicago. Finally, taking a holistic view of police contact in Chicago and Philadelphia, we show that settlement agreements curbed pedestrian stops but that a concomitant rise in traffic stops maintained aggregate racial disparities, illustrating the challenges facing regulatory efforts. These case studies highlight the promise and value of viewing legal principles and policy goals through the lens of modern data analysis—both in police reform and in reform efforts more broadly.

我们描述并应用三种实证方法来识别多余的警察活动、不合理的种族差异影响以及监管干预的限制。首先，使用成本效益分析，我们表明纳什维尔和纽约市的交通和行人停靠点对有色人种社区的影响不成比例，但并未实现其既定的公共安全目标。其次，我们通过提出一种用于识别“处境相似”的个体的经验方法来解决歧视研究中长期存在的问题，并在此过程中量化纽约市和芝加哥停止政策中潜在的不合理差异。最后，对芝加哥和费城的警方接触情况进行整体观察，我们表明，和解协议限制了行人停车，但随之而来的交通停车增加维持了总体种族差异，说明了监管工作面临的挑战。这些案例研究强调了通过现代数据分析的视角看待法律原则和政策目标的前景和价值——无论是在警察改革中还是在更广泛的改革努力中。