Standardized Tests and Affirmative Action: The Role of Bias and Variance

by   Nikhil Garg, et al.

The University of California recently suspended through 2024 the requirement that California applicants submit SAT scores, upending the major role standardized testing has played in college admissions. We study the impact of this decision and its interplay with other policies (such as affirmative action) on admitted class composition. We develop a market model with schools and students. Students have an unobserved true skill level, a potentially observed demographic group membership, and an observed application with both test scores and other features. Bayesian schools optimize the dual-objectives of admitting (1) the "most qualified" and (2) a "diverse" cohort. They estimate each applicant's true skill level using the observed features and potentially their group membership, and then admit students with or without affirmative action. We show that dropping test scores may exacerbate disparities by decreasing the amount of information available for each applicant. However, if there are substantial barriers to testing, removing the test improves both academic merit and diversity by increasing the size of the applicant pool. We also find that affirmative action alongside using group membership in skill estimation is an effective strategy with respect to the dual-objective. Findings are validated with calibrated simulations using cross-national testing data.



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