Dropping Standardized Testing for Admissions Trades Off Information and Access

• URL: http://arxiv.org/abs/2010.04396v5
• Date: Tue, 5 Sep 2023 05:28:27 GMT
• Title: Dropping Standardized Testing for Admissions Trades Off Information and Access
• Authors: Nikhil Garg, Hannah Li, Faidra Monachou
• Abstract summary: 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. Our framework finds a natural application to recent policy debates on dropping standardized testing in college admissions.
• Score: 2.354619015221828
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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