Talent is Everywhere, Mobility is Not: Mapping the Topological Anchors of Educational Pathways

TL;DR

This study uses machine learning on Chilean administrative data to map educational pathways, revealing how socioeconomic background influences student mobility, choices, and regional migration, highlighting structural inequalities.

Contribution

Introduces a novel framework combining machine learning and a two-dimensional educational space to analyze individual educational trajectories at scale.

Findings

High-achieving, low-income students tend to stay regional

Affluent students are more geographically mobile

Socioeconomic background significantly influences educational pathways

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.