Reducing the Feeder Effect in Public School Admissions: A Bias-aware Analysis for Targeted Interventions

Traditionally, New York City's top 8 public schools select candidates solely based on their scores in the Specialized High School Admissions Test (SHSAT). These scores are known to be impacted by socioeconomic status of students and test preparation received in "feeder" middle schools, leading to a massive filtering effect in the education pipeline. For instance, around 50% (resp. 80%) of the students admitted to the top public high schools in New York City come from only 5% (resp. 15%) of the middle schools. The classical mechanisms for assigning students to schools do not naturally address problems like school segregation and class diversity, which have worsened over the years. The scientific community, including policy makers, have reacted by incorporating group-specific quotas, proportionality constraints, or summer school opportunities, but there is evidence that these can end up hurting minorities or even create legal challenges. We take a completely different approach to reduce this filtering effect of feeder middle schools, with the goal of increasing the opportunity of students with high economic needs. We model a two-sided market where a candidate may not be perceived at their true potential, and is therefore assigned to a lower level than the one (s)he deserves. We propose and study the effect of interventions such as additional training and scholarships towards disadvantaged students, and challenge existing mechanisms for scholarships. We validate these findings using SAT scores data from New York City high schools. We further show that our qualitative takeaways remain the same even when some of the modeling assumptions are relaxed.