We study variation in policing outcomes attributable to differential policing practices in New York City (NYC) using geographic regression discontinuity designs. By focusing on small geographic windows near police precinct boundaries we can estimate local average treatment effects of precincts on arrest rates. The standard geographic regression discontinuity design relies 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. In particular, our test is robust to violations of the assumptions traditionally made in geographic regression discontinuity designs and is valid under weaker assumptions. We use a unique form of resampling to identify new geographic boundaries that are known to have no treatment effect, which provides a valid estimate of our test statistic's null distribution even under violations of standard assumptions. We find that this procedure gives substantially different results in the analysis of NYC arrest rates than those that rely on standard assumptions.

翻译：我们利用地理回归不连续设计研究纽约市不同警区执法实践差异所导致的警务结果变化。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。标准的地理回归不连续设计依赖于潜在结果曲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很容易被违反。我们开发了一种新颖且鲁棒的方法来检验纽约市不同警区是否因差异而导致警务结果变化。具体而言，我们的检验对传统地理回归不连续设计中的假设违反具有鲁棒性，并在更弱的假设下仍然有效。我们采用一种独特的重采样方法来识别已知无处理效应的新地理边界，即使在标准假设被违反的情况下，也能为检验统计量的零分布提供有效估计。我们发现，与依赖标准假设的方法相比，该程序在对纽约市逮捕率的分析中得出了显著不同的结果。