Data-Driven Decision Processes Seminar

Title: Designing School Choice for Diversity in the San Francisco Unified School District

Abstract: More than 65 years after the "Brown v. Board of Education" ruling that school segregation is unconstitutional, public schools across the U.S. are resegregating. In attempts to disentangle school segregation from neighborhood segregation, many cities have adopted policies for district-wide choice. However, these policies have largely not improved patterns of segregation. From 2018-2020, we worked with the San Francisco Unified School District (SFUSD) to design a new policy for student assignment system that meets the district's goals of diversity, predictability, and proximity. To develop potential policies, we used optimization techniques to augment and operationalize the district's proposal of restricting choice to zones. We compared these to approaches typically suggested by the school choice literature. Using predictive choice models developed using historical choice data, we find that appropriately-designed zones with minority reserves can achieve all the district's goals, at the expense of choice, and choice can resegregate diverse zones. A zone-based policy can decrease the percentage of racial minorities in high-poverty schools from 29% to 11%, decrease the average travel distance from 1.39 miles to 1.29 miles, and improve predictability, but reduce the percentage of students assigned to one of their top 3 programs from 80% to 59%. Existing approaches in the school choice literature can improve diversity at lesser expense to choice, and present a trade-off between improvements to diversity, proximity, and equity of access. Our work informed the design and approval of a zone-based policy for use starting the 2025-26 school year.