We study variation in policing outcomes attributable to differential policing practices in New York City (NYC) using geographic regression discontinuity designs (GeoRDDs). By focusing on small geographic windows near police precinct boundaries we can estimate local average treatment effects of police precincts on arrest rates. We propose estimands and develop estimators for the GeoRDD when the data come from a spatial point process. Additionally, standard GeoRDDs rely on continuity assumptions of the potential outcome surface or a local randomization assumption within a window around the boundary. These assumptions, however, can easily be violated in realistic applications. We develop a novel and robust approach to testing whether there are differences in policing outcomes that are caused by differences in police precincts across NYC. Importantly, this approach is applicable to standard regression discontinuity designs with both numeric and point process data. This approach is robust to violations of traditional assumptions made, and is valid under weaker assumptions. We use a unique form of resampling to provide a valid estimate of our test statistic's null distribution even under violations of standard assumptions. This procedure gives substantially different results in the analysis of NYC arrest rates than those that rely on standard assumptions.

翻译：我们利用地理断点回归设计（GeoRDDs）研究纽约市因不同执法实践导致的警务结果差异。通过聚焦警区边界附近的小范围地理窗口，我们能够估计警区对逮捕率的局部平均处理效应。我们针对空间点过程数据提出参数定义并开发了GeoRDD的估计方法。此外，标准GeoRDD依赖于潜在结果表面的连续性假设或边界附近窗口内的局部随机化假设，然而这些假设在实际应用中极易被违反。我们开发了一种新颖且稳健的方法，用于检验纽约市各警区间执法差异是否由警区差异导致。值得注意的是，该方法适用于数值型和点过程数据的标准断点回归设计，且对传统假设的违反具有稳健性，在更弱假设下依然有效。我们采用独特的重抽样方式，即使在标准假设被违反的情况下也能提供检验统计量零分布的有效估计。与依赖标准假设的分析相比，本方法在纽约市逮捕率分析中得出了显著不同的结果。