Data to replicate: The Geography of Mathematical (Dis)Opportunity

Published: 18 July 2023| Version 1 | DOI: 10.17632/gyjsdxdgc9.1
Manuel Gonzalez Canche


Abstract Research has shown that mathematical proficiency gaps are related to students’ and schools’ indicators of poverty, with fewer studies on neighborhood effects on achievement gaps. Although this literature has accounted for students’ nesting within schools, so far methodological constraints have not allowed researchers to formally account for both multilevel and spatial effects. We contribute to this discussion by simultaneously considering test-takers own socioeconomic standing and the impact of their nesting school and neighborhood structures. Multilevel simultaneous autoregressive (MSAR) models and population-level data of 2.09 million test-takers, whose standardized performances were measured at grades 3 to 8 in New York State, revealed the presence of geography of mathematical (dis)opportunity. Since mathematical performance is spatially dependent across schools and neighborhoods, moving forward, applied researchers should rely on MSAR to account for sources of spatially driven bias that cannot be handled with multilevel models alone. Full replication code and data is provided To be featured at AERA OPEN


Steps to reproduce

Full replication code is provided here


University of Pennsylvania


Mathematics, Data Science, Applied Geography, Social Stratification, Spatial Database, Spatial Analysis, Spatial Auto-Correlation, Academic Achievement, Spatial Dependence, Elementary School, Socio-Economic Stratification, Analysis of Poverty


Spencer Foundation

National Academy of Education