Using advanced data analytics to assess the spatial causal effects of policing policies and practices

研究议程概述程序正义理论的基础是，当人们评估他们与警察的互动时，他们主要关注警察是否做出客观和中立的决定，并以公平和尊重的方式对待这些决定。当人们被以程序公正的方式对待时，他们往往认为当局在道德上是合适的，即使他们不同意他们的行为和要求，他们也会同意他们的行动和要求。反过来，当人们认为法律当局是适当和公正的时，他们就会感到遵守法律并与警方和刑事法院合作是一种正常的有根基的责任。合法性的概念是民主警务的核心，因为在民主社会中，警察必须通过公正行事、按比例使用胁迫并说服公民他们是有资格被遵守的制度来寻求和维持公众的支持。然而，在程序正义文献中，迄今收集的大多数经验证据本质上是观察性的，并依赖于对统计关联的解释。事实上，缺乏系统地评估该理论所提出的因果关系主张的研究。在最近的一次文献回顾中，Nagin和Telep(2017：18)表达了他们的担忧：“尚未确定的是，这些关联是否反映了程序上的公正待遇与人们所感知的合法性和合规性之间的因果关系。”然而，如果没有实证研究证明强大的因果关系，就很难设计出成功的政策倡议。因此，我建立这一奖学金的主要目的是测试和推进对警察文献中一些核心因果声明的理论理解。具体地说，我将仔细审查邻里级别和基于位置的警察效果。公民对警察的经验和看法有很大的不同，但尚不清楚这在多大程度上可以归因于不同社区的不同警务战略。通过使用地理编码的行政警察数据，并将其与公众态度调查合并，我的研究可以确定在特定社区最有效的警务做法，以向警察部队提供量身定做的建议。为了确定因果效应，我将使用最先进的因果推理技术、多水平匹配和基于位置的回归不连续设计。简而言之，当前的提案计划解决程序正义警务的一个关键方面：不同特征的社区之间，警务举措的效果如何不同？

Brief outline of the research agendaThe theory of procedural justice is built on the idea that when people evaluate their interactions with the police, they are primarily focussed on whether or not the officer makes objective and neutral decisions and treats them in a fair and respectful manner. When people are treated in a procedurally just manner, they tend to find the authorities morally appropriate and give consent to their actions and demands even when they disagree with them. In turn, when people view legal authorities as proper and just, they feel a normatively grounded duty to comply with the law and cooperate with the police and criminal courts. The concept of legitimacy lies at the heart of democratic policing, in that in a democratic society police must seek and maintain public support by acting impartially, using coercion proportionately and persuading the citizenry that they are an institution that is entitled to be obeyed.Yet, in the procedural justice literature, most of the empirical evidence gathered so far is observational in nature, and rely on the interpretation of statistical associations. In fact, there is a dearth of research systematically assessing the causal claims made by the theory. In a recent review of the literature, Nagin and Telep (2017: 18) voiced their concern: "What has not been established is whether these associations reflect a causal connection between procedurally just treatment and perceived legitimacy and compliance." However, without empirical research demonstrating robust causal relationships, it is difficult to devise successful policy initiatives.Thus, my principal aim with this fellowship is to test and advance theoretical understanding of some core causal claims of the policing literature. Specifically, I will scrutinise neighbourhood-level and location-based police effects. There is a substantial heterogeneity in the citizens' experiences and views regarding police officers but it is yet unclear to what extent this can be attributed to varying policing strategies in different neighbourhoods. By using geo-coded administrative police data, and merging it with public attitudes surveys, my research can identify policing practices that work best in particular neighbourhoods, to provide tailored recommendations to police forces. To identify causal effects, I will use state-of-the-art causal inference techniques, multilevel matching and location-based regression discontinuity designs. In a nushell, the current proposal plans to address one crucial aspect of procedural justice policing: how the effects of policing initiatives vary across neighbourhoods with different characteristics?