Nudges to High-Performing High Schools: Reducing Information Gaps in NYC’s High School Application Process
Written by Erica Chiang
The Challenge: Disparities in High School Application Behavior
In New York City, eighth-grade students apply to high school by submitting a ranked list of programs they are interested in attending. A centralized matching process then assigns them to a single program where they receive an offer. This system was designed to expand access to schools by allowing students to apply to any high school in the city and to rank programs based on their true preferences without needing to strategize based on their chances of admission.
But in practice, the scale and complexity of the system makes this difficult. NYC has over 900 high school programs, with a wide range of academic focuses, extracurricular activities, and performance metrics, meaning that forming informed preferences over schools requires significant time, information, and support. NYC Public Schools (NYCPS) believes that this complexity may be a driving force behind disparities in high school application behavior—for instance, we observed that students from some middle schools consistently apply to high-performing high schools at much lower rates, even to schools where they are likely to be admitted.
This raises a key question: To what extent do observed application patterns reflect students’ true preferences, versus gaps in information access?
The Project: “Nudges” to High-Performing High Schools
As a Siegel PiTech PhD Impact Fellow with NYCPS, I designed an informational intervention to target students from middle schools that have not historically sent many students to the highest performing high schools, helping them discover high-performing, nearby high schools where they have a strong chance of admission. Our goals were to (1) expand opportunity and increase awareness of high-quality school options for students who might not otherwise have known of them, and (2) understand the extent to which current behavior reflects true preferences versus lack of information.
Designing the intervention required close collaboration across multiple NYCPS offices. Together, we:
Defined a set of criteria for “high-performing” high schools
Identified “target middle schools” that historically sent very few students to these schools
Identified “nudge-eligible” students—those attending target middle schools who live near a high-performing high school where they have a high chance of acceptance
Each of these steps involved careful discussion and tradeoffs. For example, we wanted to focus on a small fraction of high schools in order to effectively address disparities, while still ensuring broad geographic coverage across the city. Defining target middle schools required discussing how many past years of data to consider and what threshold constituted “very few” students. We also conducted focus groups, which informed practical design choices such as how far a recommended school should be from a student’s home, how many schools to recommend to each student, and how to communicate recommendations clearly to families.
Safely deploying the intervention also required anticipating how our nudges would change overall application patterns. So a major focus of my work was ensuring that our nudges did not substantively affect admissions cutoffs at any programs. Using data from past admissions cycles, I simulated match outcomes given varying numbers of students nudged to each program and under various assumptions on how many students actually changed their application behavior after receiving nudges. This allowed us to make informed decisions about the number of nudges for each program, such that our intervention could expand access at scale without meaningfully affecting admissions cutoffs.
Erica Chiang
Ph.D. Student, Computer Science
Impact & Path Forward
This fall, we successfully deployed the system to about 500 eighth-grade students currently applying to high school, as part of a randomized controlled trial. We are now working toward evaluating the causal effects of the intervention and building off of the insights from this year to design a larger-scale, more personalized recommendation system for future years.