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.

翻译：问题定义：传统上，纽约市顶尖的8所公立学校仅依据学生在特殊高中入学考试（SHSAT）中的成绩选拔候选人。已知这些成绩受学生社会经济地位及中学阶段备考情况影响，导致教育体系中存在显著的过滤效应。传统的学校分配机制无法自然解决学校隔离和班级多样性等问题，而这些问题近年来日益恶化。包括政策制定者在内的科学界通过引入群体配额和比例约束来应对，但效果参差不齐。如何找到有效且公平的方法来扩大优质教育资源的可及性，仍是未解难题。方法/结果：我们采用与多数现有文献不同的运筹学方法，旨在为高经济需求学生增加机会。利用纽约市教育局（DOE）的数据，我们发现被该局分类为"弱势群体"（主要基于经济因素的标准）的学生成绩分布存在偏移。我们将这种偏移建模为一种"偏差"，源于对弱势学生真实潜能的低估。我们分析了该偏差对分类匹配市场的影响，并表明：当中央规划的干预措施针对具有平均成绩水平的弱势学生群体时，可通过奖学金或培训显著降低偏差的影响。