A Generalized Outcome Test of Racial and Ethnic Discrimination in the Bail Decision Process

研究人员在刑事司法程序的不同阶段发现了种族差异的证据，例如交通拦截、保释水平、拘留决定和监禁。但是，如果决策者(法官、警官等)观察到研究人员不能观察到的特征，即使对种族待遇进行回归调整的比较也不能为区分种族差异和歧视提供适当的基础。最近，经济学家提出了一种替代方法，称为结果分析，在警察进行种族定性的背景下。我们打算在设定保释金的情况下检验这种“标准结果分析”(SOA)的表现。在驾车搜索环境中，SOA的关键预测是，当警察以理性、不偏不倚的方式行事时，所有被搜索群体的边缘成员必须表现出相同的命中率。但研究人员无法观察到一个特定的人在士兵决策时是边缘还是超边缘，因此他们必须使用平均命中率，而不是边缘命中率。在没有进一步信息的情况下，搜索者的平均命中率没有理由等同于边缘人的命中率。这就是所谓的基础设施边缘问题。Ayres(2001)提出，这个问题在保释设定的背景下消失了，因为法官被设定了连续的保释金额。这一建议的主要智力价值来自这样一个事实，即如果法官同时考虑被告保释的概率和他们在获释期间犯下不良行为的概率，那么这个猜想在保释案件中就不成立。我们展示了如何写出考虑这些问题的理性法官的一阶条件，以便它将FTA的概率与一个函数联系起来，该函数在任何组中的平均值以复杂的方式取决于该组中特征的分布。当法官比研究人员拥有更多的信息时，就存在遗漏变量偏差。我们表明，SOA测试通常是不合适的，产生的估计在统计上是有偏差的，方向是未知的。我们提供了一种替代的、通用的结果分析(GOA)，它是公正的，实施起来也很简单。然后，当通过工具变量获得关于保释水平的外部信息时，我们制定了一个关于种族歧视的有效测试。我们建议在华盛顿特区审前服务局的一个新的行政数据集上实施我们的GOA测试。该数据集有关于被捕者的丰富信息，包括药物测试结果，其详细程度至少与种族对保释决定影响的最佳研究中使用的行政数据集一样详细(Demuth 2003)。我们认为，被指派设定保释的法官的身份可以被视为外生的，我们将使用由于法官对自由贸易协定的感知成本的不同而导致的保释水平的变化来一致地估计我们的模型。我们提出的测试不仅允许我们测试辨别力，还允许我们测试行为模型是否被正确指定。为了说明我们方法的潜在优点和缺点，我们建议将这些结果与标准回归分析的结果进行比较。政府间司法机构的框架可以产生更广泛的影响，因为它可以在其他情况下用来测试种族歧视(例如，包括刑期)。结果测试需要对感兴趣的行为者的目标进行正式陈述，这可以基于实质性的证据进行评估，并且它对理性行为将包括哪些内容做出了强有力的可测试预测。这一正式的假设检验框架应会在犯罪学和刑事司法领域带来更多的理论发展。DC预审机构有“研究友好”的记录，工作人员表示愿意向马里兰州人口研究中心(MPRC)提供其整个被捕者数据库的定期数据传输。MPRC拥有一支强大的信息技术专业人员队伍，他们将把数据转化为安全、可用的形式。MPRC打算将这些数据提供给研究生和其他学者。

AbstractResearchers have found evidence of racial disparity in different stages of the criminal justice process for example, traffic stops, bail levels, detention decisions and incarceration. But, if the decision maker (judge, police officer, etc) observes characteristics that researchers cannot, even regression-adjusted comparisons of racial treatment do not provide an appropriate basis for distinguishing between racial disparity and discrimination. Recently, economists have proposed an alternative approach, known as outcome analysis, in the context of racial profiling by police. We intend to examine the performance of this "standard outcome analysis" (SOA) in the context of bail-setting.The key prediction of SOA in the motorist search context is that the marginal members of all searched groups must exhibit the same hit rate when police behave in a rational, unbiased fashion. But researchers cannot observe whether a given person was marginal or inframarginal at the trooper's decision time, so they must use average, rather than marginal, hit rates. Without further information, there is no reason why the average hit rate among those searched will equal the hit rate of the marginal person. This is known as the inframarginality problem. Ayres (2001) has suggested that this problem disappears in the bail-setting context, since judges are set continuous bail amounts.The main intellectual merit of this proposal comes from the fact that we can show that this conjecture does not hold in the bail case if judges take account of both the probability that defendants make bail and the probability that they commit bad acts while on release. We show how to write the first-order condition of a rational judge who takes these matters into account so that it relates the probability of FTA to a function whose average in any group depends in complicated ways on the distribution of characteristics in that group. Omitted variable bias exists whenever judges have more information than researchers. We show that SOA tests generally are not appropriate, yielding estimates that are statistically biased in an unknown direction. We offer an alternative, generalized outcome analysis (GOA), that is unbiased and straightforward to implement. We then formulate a valid test for racial discrimination when exogenous information on bail levels is available via instrumental variables. We propose to implement our GOA test on a new administrative dataset from the Washington DC Pretrial Services Agency. The dataset has rich information about arrestees, including drug test results, that is at least as detailed as the administrative datasets used in the best studies of the impact of race on the bail decision (Demuth 2003). We argue that the identity of the judge assigned to set bail can be treated as exogenous, and we will use variation in bail levels due to variation in judges' perceived costs of FTA to consistently estimate our model. The test we propose allows us not only to test for discrimination, but also to test whether the behavioral model is correctly specified. To illustrate the potential strengths and drawbacks of our approach, we propose to compare these results with those from standard regression analysis. The GOA framework can have a broader impact because it can be used in other settings to test for racial discrimination (e.g., including sentence length). Outcome testing requires a formal statement of the objectives of the actor of interest, which can be evaluated based on substantive evidence, and it makes a strong testable prediction about what rational behavior would include. This formal hypothesis testing framework should lead to more theory development in the field of criminology and criminal justice.The DC PreTrial Agency has a track record of being ``research-friendly'', and staff members have expressed a willingness to provide the Maryland Population Research Center (MPRC) with regular data transfers of their entire database of arrestees. MPRC has a strong staff of informational technology professionals who will transform the data into a secure, useable form. MPRC intends to make these data to both graduate students and other academics.