Dropping Standardized Testing for Admissions Trades Off Information and Access

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 tradeoff 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 nontraditional 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 nonmonotonic. 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.

Funding: This work was supported by the National Science Foundation [Grant 2339427]. This research uses public data from the Texas Higher Education Opportunity Project (THEOP) and acknowledges the following agencies that made THEOP data available through grants and support: the Ford Foundation, the Andrew W. Mellon Foundation, the William and Flora Hewlett Foundation, the Spencer Foundation, the National Science Foundation [Grant SES-0350990], the National Institute of Child Health & Human Development [Grant R24 H0047879], and the Office of Population Research at Princeton University. NG was also supported by Cornell Tech Urban Tech Hub, Google, Meta, and Amazon research awards.

Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.02573.