Unmasking Intersectional Inequities: Analyzing Brazilian Education Data Through an Intersectional Lens

• In Event: Virtual Poster Session
  In Virtual iPresentation Only: Division D - Section 1: Educational Measurement, Psychometrics, and Assessment Virtual Poster Session

Abstract

The lack of critical analysis can mislead data-driven policymaking and exacerbate structural unfairness. This study presents an intersectional analysis of nearly 1 million Brazilian 5th-graders' SAEB Mathematics and Portuguese grades. By incorporating intersecting filters such as gender, geographic location, and race/ethnicity, the study reveals educational outcome disparities across social groupings. Intersectionality uncovered latent exclusion patterns overlooked by traditional analyses. The data showed that Black girls received lower Portuguese grades than white boys despite higher overall scores, and Black boys had worse Math grades than white girls, despite boys generally scoring higher. These findings challenge single-axis assumptions and emphasize the need for improved public data analysis to address multidimensional educational inequality.

Authors

• Gabriel Fortes, Universidad Alberto Hurtado

• Angelina Nunes de Vasconcelos, Universidade Federal de Alagoas

• Leonardo Soares, Universidade Federal de Alagoas

• Carine Mendes, Federal University of Alagoas

• Leonardo Brandao Marques, Harvard University