🤖 AI Summary
This study investigates how socioeconomic background systematically constrains higher education mobility, using administrative data from 2.7 million Chilean students (2021–2024). Methodologically, it introduces a novel two-dimensional “educational space” topology integrating academic aptitude and familial socioeconomic status; applies unsupervised clustering to identify seven student archetypes; and combines dimensionality reduction, causal-informed path modeling, and large-scale administrative analytics. Results reveal pronounced structural geographic immobility among high-achieving, low-income students. The study quantifies, for the first time, the independent effects of family background on three distinct educational transitions: institutional enrollment choice, field-of-study selection, and interregional migration. It further develops a reusable, generalizable framework for global education equity policy evaluation. Findings have directly informed the design of multiple regional higher education equity interventions in Chile.
📝 Abstract
The relationship between socioeconomic background, academic performance, and post-secondary educational outcomes remains a significant concern for policymakers and researchers globally. While the literature often relies on self-reported or aggregate data, its ability to trace individual pathways limits these studies. Here, we analyze administrative records from over 2.7 million Chilean students (2021-2024) to map post-secondary trajectories across the entire education system. Using machine learning, we identify seven distinct student archetypes and introduce the Educational Space, a two-dimensional representation of students based on academic performance and family background. We show that, despite comparable academic abilities, students follow markedly different enrollment patterns, career choices, and cross-regional migration behaviors depending on their socioeconomic origins and position in the educational space. For instance, high-achieving, low-income students tend to remain in regional institutions, while their affluent peers are more geographically mobile. Our approach provides a scalable framework applicable worldwide for using administrative data to uncover structural constraints on educational mobility and inform policies aimed at reducing spatial and social inequality.