We study the role of information and access in capacity-constrained selection problems with fairness concerns. We develop a theoretical statistical discrimination framework, where each applicant has multiple features and is potentially strategic. The model 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 calibrated simulations in both the strategic and non-strategic settings, we demonstrate the presence of practical instances where the decision to eliminate standardized testing improves or worsens all metrics.

翻译：我们研究了在资源有限且关注公平性的选拔问题中，信息与可及性所扮演的角色。我们构建了一个理论性的统计歧视框架，在该框架中，每位申请者拥有多个特征并可能具有策略性。该模型形式化了某个特征在信息层面（可能存在的积极作用）与其因不同社会群体成员对该特征获取机会不平等而导致的（负面）排斥性之间的权衡。我们的框架自然适用于近期关于大学招生中是否取消标准化考试的政策辩论。我们的核心结论是，放弃某一特征（如考试成绩）的决定，不能脱离其他特征所提供的信息环境以及该要求对申请者群体构成的影响。放弃一个特征可能通过减少每位申请者（尤其是来自非传统背景的申请者）可获得的信息量，从而加剧不公平现象。然而，当特定特征存在获取障碍时，信息环境与获取障碍对申请者群体规模的影响之间的相互作用变得极其复杂。在此情况下，我们给出了一个阈值特征刻画，用以说明何时取消某项特征能同时提升学术水平与多样性。最后，通过在策略性与非策略性设定下进行校准模拟，我们证明了存在实际案例，其中取消标准化考试的决定可能改善或恶化所有衡量指标。