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

保释决定过程中种族和民族歧视的广义结果检验

• 批准号: 0718955
• 负责人: Peter Reuter
• 金额: $ 15.42万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-15 至 2011-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0718955&HistoricalAwards=false
• 关键词: Generalized; Outcome; Test; Racial; Ethnic

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.

摘要研究人员发现，在刑事司法程序的不同阶段，例如，交通站，保释水平，拘留决定和监禁，种族差异的证据。 但是，如果决策者（法官、警官等）观察到研究人员无法观察到的特征，即使是回归调整的种族待遇比较也不能为区分种族差异和歧视提供适当的基础。 最近，经济学家提出了另一种方法，称为结果分析，在警察种族貌相的背景下。 我们打算研究的性能，这种“标准结果分析”（SOA）的背景下保释setting.The关键的预测SOA在驾车搜索的上下文中是，所有搜索组的边缘成员必须表现出相同的命中率时，警察表现在一个理性的，公正的方式。但是研究人员无法观察到一个特定的人在骑警的决定时间是边缘还是超边缘，所以他们必须使用平均而不是边缘的命中率。在没有进一步信息的情况下，没有理由说被搜索者的平均命中率会等于边缘人的命中率。这就是所谓的超边缘性问题。 Ayres（2001）认为，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，这个建议的主要学术价值来自于这样一个事实，即我们可以证明，如果法官同时考虑被告保释的可能性和他们在释放期间犯下不良行为的可能性，那么这个猜想在保释案件中就不成立。我们将展示如何编写一阶条件的理性法官考虑到这些事项，使其涉及的概率FTA的功能，其平均值在任何一组依赖于复杂的方式在该组中的分布特征。 当法官比研究者拥有更多的信息时，就存在遗漏变量偏倚。 我们表明，SOA测试一般是不合适的，产生的估计是统计偏置在一个未知的方向。我们提供了一种替代的，广义的结果分析（果阿），这是公正的，直接实施。 然后，我们制定了一个有效的测试种族歧视时，通过工具变量的保释金水平的外生信息。我们建议在来自华盛顿特区审前服务机构的新管理数据集上实现我们的果阿测试。 该数据集有关于被捕者的丰富信息，包括药物测试结果，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，法官的身份被分配到设置保释可以被视为外源，我们将使用的变化，由于在法官的FTA感知成本的变化，以不断估计我们的模型保释水平。我们提出的测试不仅允许我们测试歧视，而且还可以测试行为模型是否被正确指定。 为了说明我们的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。果阿框架可以产生更广泛的影响，因为它可以用在其他环境中来测试种族歧视（例如，包括句子长度）。结果测试需要对感兴趣的行为者的目标进行正式陈述，可以根据实质性证据进行评估，并且它对理性行为将包括什么做出了强有力的可测试预测。 这一正式的假设检验框架应导致更多的理论发展领域的犯罪学和刑事司法。DC预审机构有一个“研究友好”的跟踪记录，工作人员表示愿意提供马里兰州人口研究中心（MPRC）与他们的整个被捕者数据库的定期数据传输。 MPRC拥有强大的信息技术专业人员，他们将数据转换为安全，可用的形式。 MPRC打算将这些数据提供给研究生和其他学者。