We study the role of information and access in capacity-constrained selection problems with fairness concerns. We develop a theoretical framework with testable implications that formalizes the trade-off between the (potentially positive) informational role of a feature and its (negative) exclusionary nature when members of different social groups have unequal access to this feature. Our framework finds a natural application to recent policy debates on dropping standardized testing in college admissions. Our primary takeaway is that the decision to drop a feature (such as test scores) cannot be made without the joint context of the information provided by other features and how the requirement affects the applicant pool composition. Dropping a feature may exacerbate disparities by decreasing the amount of information available for each applicant, especially those from non-traditional backgrounds. However, in the presence of access barriers to a feature, the interaction between the informational environment and the effect of access barriers on the applicant pool size becomes highly complex. In this case, we provide a threshold characterization regarding when removing a feature improves both academic merit and diversity. Finally, using application and transcript data from the University of Texas at Austin, we illustrate that there exist practical settings where dropping standardized testing improves or worsens all metrics.
翻译:我们研究信息和准入在受能力限制的甄选问题中的作用,并关注公平问题。我们开发了一个具有可测试影响的理论框架,在不同的社会群体成员无法平等地获得某一特征时,将某一特征的(潜在积极的)信息作用与其(消极的)排斥性质之间的权衡正式确定下来。我们的框架发现,最近关于大学入学时放弃标准化测试的政策辩论自然适用。我们的主要取舍是,如果没有其他特征提供的信息的共同背景以及要求如何影响申请人集合构成,就不能作出放弃某一特征的决定(例如考试分数)。通过减少每个申请者,特别是非传统背景的申请者可获得的信息数量,放弃某一特征可能会加剧差异。然而,在存在某种特征的准入障碍的情况下,信息环境与准入障碍对申请人群规模的影响之间的相互作用变得非常复杂。在本案中,我们提供了在消除某一特征时提高学术功绩和多样性的门槛定性。最后,我们利用奥斯丁德克萨斯大学的应用和笔录数据,说明存在实际环境,在这种环境中,放弃标准化测试会改善或恶化所有计量标准。