Dropping Standardized Testing for Admissions Trades Off Information and Access
For policymakers and admissions committees, this work provides a formal model to understand when dropping standardized testing helps or harms equity and selection quality.
The paper develops a statistical discrimination framework to analyze the trade-off between the informational value and exclusionary effects of features like standardized test scores in admissions. It finds that dropping such features can either improve or worsen fairness and efficiency metrics depending on the informational context and access barriers, with calibrated simulations showing both possibilities.
We study the role of information and access in capacity-constrained selection problems with fairness concerns. We develop a 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 policy debates on dropping standardized testing in 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. Furthermore, we consider an extension with two schools and costly tests, where strategic students decide whether to take the test or not. Our theoretical results reveal that the students' test-taking behavior can be non-monotonic. We characterize the two-school policy equilibria and show that each school's optimal decision to drop the test critically depends on the other school's test policy. Finally, using calibrated simulations, we demonstrate the presence of practical instances where the decision to eliminate standardized testing improves or worsens all metrics.