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

Sep 15, 2026

Uploaded by Mack Lee

Problem definition and outcomes analysis address how New York City’s top public schools rely on SHSAT scores that reflect socioeconomic differences and middle-school test preparation, creating a substantial filtering effect across the education pipeline and worsening segregation and class diversity. The study introduces an operations-based, bias-aware modeling approach using NYC Department of Education data to quantify how misestimated potential affects an assortative matching market. It evaluates centralized, targeted scholarships or training and proposes a randomization-based allocation policy for incentive compatibility and individual fairness.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages43

Reading Time1 h 48 min

AccessFree to download

Abstract

1 Introduction

Methodology/results

Managerial implications

Keywords

arXiv :2004 . 10846v 5 [ cs .CY] 2 Jun 2026 Reducing the Filtering Effect in Public School Admissions: A Bias-aware Analysis for Targeted Interventions Yuri Faenza ∗, Swati Gupta †, Aapeli Vuorinen ‡, and Xuan Zhang § Abstract 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. Managerial implications: To make these interventions incentive compatible and individuallyfair, we propose a randomization-based policy for allocation of training resources to students, which is heavily targeted towards average performers. Our results challenge existing notions of scholarships in the current education system. We believe that these insights can guide policymakers in answering a critical question: how should one allocate limited funding across schools and students to maximally help disadvantaged students. Keywords: Bias, admissions, interventions, assortative matching, randomized policies. 1 Introduction The disparity in opportunities plays a major role in access to education at different levels of the educational pipeline (Quinn Capers et al. , 2017) . It is known that outcomes of middle school admissions dictate high school admissions, which in turn impact pathways to higher education (Corcoran and Baker-Smith, 2018; Boschma and Brownstein, 2016) . Selection starts much earlier however, with gifted and talented programs screening students as young as 4 years old. In particular, our work is motivated by high school admissions in New York City (NYC) . NYC has an extensive public school system with current enrollment of over one million students. ∗[yf2414@columbia.edu](yf2414@columbia.edu)[ ](yf2414@columbia.edu)†[swatig@mit.edu](swatig@mit.edu),‡[aapeli.vuorinen@columbia.edu](aapeli.vuorinen@columbia.edu) §[xz2569@columbia.edu](xz2569@columbia.edu) Every year roughly 80,000 students wish to join one of its 700 high school programs. The most sought after public schools are the so-called Specialized High Schools (SHSs) which, by law, select candidates solely based on their score on the Specialized High School Admissions Test (SHSAT) (NYC DOE, 2019) . Such scores are known to be impacted by socioeconomic status of students and test preparation received in middle schools (Corcoran and Baker-Smith, 2018; Shapiro, 2019a) . The results is a massive filtering effect in high school admissions: 50%(resp. 80%) of students admitted to the SHSs come from onl

FAQs

What problem does the document focus on in public school admissions?

It analyzes how SHSAT-score-based selection creates a filtering effect linked to socioeconomic status and unequal test preparation, contributing to lower representation and reduced diversity in top schools.

How does the document model the cause of the observed filtering effect?

It models a “bias” as arising from underestimation of disadvantaged students’ true potential, then studies the impact of this bias within an assortative matching market.

What interventions does the document recommend and how are they allocated fairly?

It shows that centralized scholarships or training can reduce the impact of bias when targeted to disadvantaged students with average performance, and it proposes a randomization-based policy to make resource allocation incentive compatible and individually fair.

Sep 15, 2026

Uploaded by Mack Lee