Contextual Stochastic Optimization for School Desegregation Policymaking

• URL: http://arxiv.org/abs/2408.12572v1
• Date: Thu, 22 Aug 2024 17:40:06 GMT
• Title: Contextual Stochastic Optimization for School Desegregation Policymaking
• Authors: Hongzhao Guan, Nabeel Gillani, Tyler Simko, Jasmine Mangat, Pascal Van Hentenryck,
• Abstract summary: This paper develops a joint redistricting and choice modeling framework, called redistricting with choices (RWC) The main methodological contribution of the RWC is a contextual optimization model that minimizes district-wide dissimilarity. The results also reveal that predicting school choice is a challenging machine learning problem.
• Score: 13.670408636443831
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Most US school districts draw geographic "attendance zones" to assign children to schools based on their home address, a process that can codify existing neighborhood racial/ethnic and socioeconomic status (SES) segregation in schools. Redrawing boundaries can reduce segregation, but estimating the rezoning impact is challenging as families can opt-out of their assigned schools. This paper is an attempt to address this societal problem: it develops a joint redistricting and choice modeling framework, called redistricting with choices (RWC). The RWC framework is applied to a large US public school district for estimating how redrawing elementary school boundaries in the district might realistically impact levels of socioeconomic segregation. The main methodological contribution of the RWC is a contextual stochastic optimization model that minimizes district-wide dissimilarity, and integrates the rezoning constraints and a school choice model for the students obtained through machine learning. The key finding of the study is the observation that RWC yields boundary changes that might reduce segregation by a substantial amount (23%) -- but doing so might require the re-assignment of a large number of students, likely to mitigate re-segregation that choice patterns could exacerbate. The results also reveal that predicting school choice is a challenging machine learning problem. Overall, this study offers a novel practical framework that both academics and policymakers might use to foster more diverse and integrated schools.

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