  Problem definition: Traditionally, New York City's top 8 public schools have
selected 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 middle schools, leading to
a massive filtering effect in the education pipeline. 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 policymakers, have reacted by incorporating
group-specific quotas and proportionality constraints, with mixed results. The
problem of finding effective and fair methods for broadening access to
top-notch education is still unsolved.
  Methodology/results: We take an operations approach to the problem different
from most established literature, with the goal of increasing opportunities for
students with high economic needs. Using data from the Department of Education
(DOE) in New York City, we show that there is a shift in the distribution of
scores obtained by students that the DOE classifies as "disadvantaged"
(following criteria mostly based on economic factors). We model this shift as a
"bias" that results from an underestimation of the true potential of
disadvantaged students. We analyze the impact this bias has on an assortative
matching market. We show that centrally planned interventions can significantly
reduce the impact of bias through scholarships or training, when they target
the segment of disadvantaged students with average performance.
