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We study the role of information and access in capacity-constrained selectionproblems with fairness concerns. We develop a theoretical statisticaldiscrimination framework, where each applicant has multiple features and ispotentially 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 accessto this feature.  Our framework finds a natural application to recent policy debates ondropping standardized testing in college admissions. Our primary takeaway isthat the decision to drop a feature (such as test scores) cannot be madewithout the joint context of the information provided by other features and howthe requirement affects the applicant pool composition. Dropping a feature mayexacerbate disparities by decreasing the amount of information available foreach applicant, especially those from non-traditional backgrounds. However, inthe presence of access barriers to a feature, the interaction between theinformational environment and the effect of access barriers on the applicantpool size becomes highly complex. In this case, we provide a thresholdcharacterization regarding when removing a feature improves both academic meritand diversity. Finally, using calibrated simulations in both the strategic andnon-strategic settings, we demonstrate the presence of practical instanceswhere the decision to eliminate standardized testing improves or worsens allmetrics.

Dropping Standardized Testing for Admissions Trades Off Information and Access