Structuring College Access: The Market Segment Model and College Board Geomarkets

Title: Structuring College Access: The Market Segment Model and College Board Geomarkets

Abstract

The Structure of College Choice (Zemsky & Oedel, 1983) created “Geomarkets” and the “Market Segment Model.” Geomarkets carve states and metropolitan areas into smaller geographic units, meant to define local recruiting markets. The Market Segment Model predicts how student demand for a particular college varies by Geomarket, based on the socioeconomic characteristics of households. Geomarkets became an input for two College Board products that help colleges recruit students. First, the Enrollment Planning Service (EPS) software recommends specific Geo-markets and high schools from which colleges should recruit. Second, the Student Search Service sells the contact information of prospective students – referred to as “student lists” – and colleges can filter by Geomarket to determine which prospect profiles they purchase. We draw from scholarship on quantification, particularly the discussions of correlation and homophily by Chun (2021), to conceptualize how recruiting products incorporate Geomarkets. 

We address two research questions: What is the socioeconomic and racial variation between Geomarkets and how does this variation change over time? How does the socioeconomic and racial composition of included versus excluded prospects vary when student list purchases filter on particular Geomarkets? We answer RQ1 by analyzing Census data from 1980, 2000, and 2020. We answer RQ2 using data on student lists purchased by public universities, which we collected by issuing public records requests. We utilize a quantitative case study design. Metropolitan areas are cases. Analyses consist of descriptive statistics and interactive maps.

Bio: Ozan Jaquette

Ozan Jaquette is an associate professor of higher education in the UCLA School of Education and Information Studies. His research specializations are organizational behavior, enrollment management, higher education finance, and recruiting. His current research utilizes data science techniques (e.g., web-scraping) and other onerous methods (e.g., public records requests) to investigate marketing and recruiting behaviors in higher education. He teaches courses in programming, statistics, and organizational behavior. He is the associate director of curriculum for the UCLA DataX initiative and he has developed courses in programming and data analysis using R that are designed for students who are anxious about learning to code: https://anyone-can-cook.github.io/rclass1/ and https://anyone-can-cook.github.io/rclass2/.