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Are We Underestimating the Crime Prevention Outcomes of Community Policing? The Importance of Crime Reporting Sensitivity Bias

One of the key policing innovations of the last three decades has been community-oriented policing. It is particularly important because it is one of the only proactive policing approaches that consistently improves citizen evaluations of the police. At the same time, a series of reviews have concluded that there is not persuasive evidence that community policing reduces crime. In this paper we argue that these conclusions are likely flawed because of what we term crime reporting sensitivity (CRS) bias. CRS bias occurs because community policing leads to more cooperation with the police and subsequently increased crime reporting. Such increased crime reporting bias adjusts crime prevention outcomes of community policing downward. We illustrate this process by reanalyzing data from the Brooklyn Park ACT Experiment (Weisburd et al., 2021). We begin by showing the specific crime categories that contribute most to CRS bias. We then use a difference-in-differences panel regression approach to assess whether the experimental intervention in Brooklyn Park led to significant CRS bias. Finally, we use bounded estimates from the Brooklyn Park Experiment to adjust meta-analytic results from prior community policing studies to examine whether the conclusion that community policing does not impact on crime would need to be revisited if CRS bias was accounted for. We find that adjusted estimates tell a very different, more positive, story about community policing, suggesting that future studies should recognize and adjust for CRS bias, or identify other measures not influenced by this mechanism.

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Do Parties Negotiate After Trespass Litigation?An Empirical Study of Coasean Bargaining

The allocative efficiency outcome predicted by the Coase theorem critically depends on the assumption that, barring high transaction costs, parties will bargain after litigation and misallocated entitlements by courts will be re-allocated through voluntary exchanges. Ward Farnsworth’s 1999 small-scale survey lent credence to the claim that parties do not bargain after litigation because of the endowment effect and the animosity created by litigation. Farnsworth’s sample is small and statistically biased. Yet no other article has tested whether parties in the real world would systematically fail to exchange for behavioral reasons. This paper combines seven different data sources to shed light on this issue. We survey 1511 practicing attorneys, who reported that a substantial minority of their clients settled with the other litigating party after courts had rendered decisions. We also examine over 300 hand-coded Taiwanese cases in which the landowner sued the illicit possessor for building a structure on the plaintiff’s property. Real estate transaction records of the land in dispute show that in 6% of the cases, the landowner registered a sale of property to the possessor after the litigation. Evidence from Google Street View and satellite pictures taken by the Taiwan government suggests that the exchange rate is higher than 6%. Logistic regressions suggest that post-litigation bargaining dynamics are at least partly rational — allocative efficiency and transaction costs (conventionally defined) still matter. To the extent that the pro se status proxies for animosity incurred during litigation, Farnsworth’s thesis is also supported.

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Reconciling Legal and Empirical Conceptions of Disparate Impact: An Analysis of Police Stops Across California

We evaluate the statistical and conceptual foundations of empirical tests for disparate impact. We begin by considering a recent, popular proposal in the economics literature that seeks to assess disparate impact via a comparison of error rates for the majority and the minority group. Building on past work, we show that this approach suffers from what is colloquially known as “the problem of inframarginality”, in turn putting it in direct conflict with legal understandings of discrimination. We then analyze two alternative proposals that quantify disparate impact either in terms of risk-adjusted disparities or by comparing existing disparities to those under a statistically optimized decision policy. Both approaches have differing, context-specific strengths and weaknesses, and we discuss how they relate to the individual elements in the legal test for disparate impact. We then turn towards assessing disparate impact of search decisions among approximately 1.5 million police stops recorded across California in 2022 pursuant to its Racial Identity and Profiling Act (RIPA). The results are suggestive of disparate impact against Black and Hispanic drivers for several large law enforcement agencies. We further propose alternative search strategies that more efficiently recover contraband while also exerting fewer racial disparities.

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