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



Frank H.T. Rhodes Hall Room 253


Irene Lo, Stanford University

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.


Irene Lo is an assistant professor in the department of Management Science & Engineering at Stanford University. Her research builds on tools from algorithms and economics to design matching markets and assignment processes, with a focus on public sector and non-profit applications. She is especially interested in resource allocation in education, the environment, and the developing world. She was one of the program chairs for the 1st ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO '21), and was a co-organizer of the Mechanism Design for Social Good (MD4SG) research initiative.