**Title: Mitigating Filtering Effects in Public School Admissions: A Bias-Aware Framework for Targeted Interventions

Abstract:

Problem Statement: Historically, admissions to New York City’s elite top 8 public schools relied exclusively on Specialized High School Admissions Test (SHSAT) results. However, these scores are heavily influenced by students’ socioeconomic backgrounds and the quality of middle school test preparation, creating a significant filtering effect within the educational pipeline. Conventional student assignment mechanisms fail to adequately address issues such as school segregation and class diversity, which have deteriorated over time. In response, policymakers and researchers have attempted to incorporate group-specific quotas and proportionality constraints, yet these efforts have yielded inconsistent outcomes. Consequently, the challenge of identifying equitable and effective strategies to expand access to high-quality education remains unresolved.

Methodology and Findings: Departing from traditional literature, this study employs an operations research perspective aimed at expanding opportunities for students with high economic needs. By analyzing New York City Department of Education (DOE) data, we identify a distributional shift in the test scores of students categorized as "disadvantaged" by the DOE—a classification primarily driven by economic factors. We conceptualize this shift as a "bias" stemming from the systematic underestimation of the true academic potential of disadvantaged students. Our analysis examines how this bias affects assortative matching markets. The results demonstrate that centrally coordinated interventions, such as providing scholarships or training programs, can substantially mitigate the impact of this bias, particularly when directed toward disadvantaged students exhibiting average performance levels.

Source: arXiv Generated at: 2026-06-04 00:00:00 UTC