About the Segregation Index | The Educational Opportunity Project at Stanford University

About the Segregation Explorer

The Segregation Explorer provides data on segregation in the United States

The Segregation Explorer 1.0 provides data on segregation between schools and school districts in the United States.

In the interactive map, users can visualize school segregation between racial/ethnic and economic groups in states, counties, metropolitan areas, and school districts from 1991 to 2022 (years represent the fall of the school year; e.g., 1991 refers to the 1991-1992 school year). School district data include all schools located in the district's geographic boundaries. Email segxsupport@stanford.edu with questions.

Measuring Segregation

Segregation estimates draw on school-level data from the Longitudinal Imputed School Dataset (LISD) 1.0, created by the Segregation Explorer team. The LISD combines, cleans, and imputes missing and erroneous data from the National Center for Education Statistics Common Core of Data (CCD). More information about the LISD is available at the Get the Data page.

Segregation estimates in the interactive map represent the two-group normalized exposure index, which measures the difference between two groups’ exposure to one of the groups. For example, the White-Hispanic normalized exposure index compares the proportions of White (or Hispanic, equivalently) students in the average White and Hispanic students’ schools. A White-Hispanic normalized exposure value of 0.5 would indicate that the proportion of White students in the average White student’s school is 50 percentage points higher than in the average Hispanic student’s school. (The two-group normalized exposure measure ignores the presence of other groups aside from the racial dyad of interest.) The normalized exposure index ranges from 0 to 1. A value of 0 implies no segregation — the two groups have equal exposure to one group (all schools have identical proportions of the two groups). A value of 1 implies complete segregation— the two groups have no exposure to one another (no Hispanic student attends a school with any White students, and vice versa). For more information on this measure, see our brief.

Racial/Ethnic and Economic Groups

Racial/ethnic categories are defined by the CCD. White, Black, Asian, Native American, and Multiracial refer to non-Hispanic students. Asian includes Asian, Hawaiian, and Pacific Islander students. When estimating White-non-White segregation, non-White refers to Black, Native American, Asian, and Hispanic students. Free lunch or reduced-price lunch eligible students are those whose families earn less than 130% and 185%, respectively, of the poverty threshold for their family size.

Data Download

Visit the Get the Data page to download the LISD, a Geographic Crosswalk linking schools to larger geographies, and segregation data for multiple geographies, including several not presented in the interactive map. Additional segregation metrics, including exposure indices and the dissimilarity index, are provided in the segregation data files. Documentation and codebooks are also available for download.

Future Releases

The Segregation Explorer will release additional segregation data over the coming months, including data on residential segregation, racial/ethnic school segregation dating back to 1967, grade-level school segregation, segregation among other racial/ethnic dyads, and private school segregation. Please subscribe to the Educational Opportunity Project newsletter newsletter here to stay up to date on our future plans!

Conference: The Unfinished Legacy of Brown v Board of Education at 70

On May 6, 2024, the Educational Opportunity Project at Stanford and The Stanford Institute for Advancing Just Societies hosted a conference to reflect on the legacy of the Brown v. Board of Education Supreme Court decision and chart the unfinished business of school integration. The conference, led by sean f. reardon (Stanford) and Ann Owens (USC), brought together educators, policymakers, and leading scholars and legal experts to distill the lessons of recent research on segregation and craft a new agenda for addressing racial and economic segregation in American schools. View media from the conference here.

Who We Are

Segregation Explorer Team

Ann Owens

Professor Sociology and Spatial Sciences, University of Southern California

Ann Owens is Professor of Sociology and, by courtesy, Public Policy and Spatial Sciences at the University of Southern California. Her research centers on the causes and consequences of social inequality, with a focus on urban neighborhoods, housing, education, and geographic and social mobility. Ann has particular expertise on neighborhood and school segregation, and her research also examines how housing and educational policies cause or alleviate social inequalities. Ann is a recipient of the William T. Grant Foundation Scholar award, Spencer Foundation/National Academy of Education Postdoctoral Fellowship, and the 2022 William Julius Wilson Early Career Award from the American Sociological Association. With sean f. reardon, Ann co-leads the Segregation Index, a project aimed at generating comprehensive residential and school segregation data (www.segindex.org). Ann received her PhD in Sociology and Social Policy from Harvard University and was a postdoctoral fellow at the Stanford Center on Poverty and Inequality.

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Sean Reardon

Faculty Director

Sean Reardon is the endowed Professor of Poverty and Inequality in Education and is Professor (by courtesy) of Sociology at Stanford University. His research focuses on the causes, patterns, trends, and consequences of social and educational inequality, the effects of educational policy on educational and social inequality, and in applied statistical methods for educational research. Reardon is the developer of the Stanford Education Data Archive (SEDA). Professor Reardon received his doctorate in education in 1997 from Harvard University. He is a member of the National Academy of Education and the American Academy of Arts and Sciences.

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Demetra Kalogrides

PhD, Research Associate

Demetra Kalogrides is a research associate at the Center for Education Policy Analysis where she collaborates on research with professor Sean Reardon. Currently, she works on the creation and analysis of the data in the Stanford Education Data Archive (SEDA - seda.stanford.edu). SEDA is a publicly available series of data files that include test performance and other demographic and economic information about every public school district in the US. She is also a part-time research associate at the Annenberg Institute at Brown University where she studies teachers and school leadership with Professor Susanna Loeb. Kalogrides received a bachelor's degree in sociology from Santa Clara University and a Masters and PhD in sociology from the University of California at Davis. She has worked at CEPA since 2008.

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Heewon Jang

Assistant Professor, University of Alabama

Heewon Jang was a doctoral student in educational policy at the Stanford Graduate School of Education. She is a recipient of the fellowship from the Karr Family Scholarship and the Korea Foundation for Advanced Studies. Prior to her doctoral training, she received a bachelor’s degree in Education and Statistics (2013) and a master’s in the Sociology of Education (2016) from Korea University. Her research focuses on the patterns and consequences of residential and school segregation in relation to racial and socioeconomic achievement gaps. She is also interested in how policy interventions can increase educational opportunities for the disadvantaged. She uses a variety of quantitative methods to study how school and neighborhood contexts affect academic performance of students from different racial and economic backgrounds and whether the impact of these contexts can be moderated by educational policy.

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Thalia Tom

PhD Candidate

Thalia Tom is a Ph.D. Candidate in Sociology at the University of Southern California. Her research draws on quantitative and spatial approaches to document inequality in neighborhood and school contexts. Her work has examined links between differential exposure to school socioeconomic status and disparities in math achievement, residential segregation by educational attainment, and the role of neighborhood racial/ethnic and socioeconomic composition in shaping access to health resources. Thalia’s dissertation, which investigates the consequences of neighborhood disadvantage for educational outcomes in early childhood, has been supported by grants from the American Sociological Association/National Science Foundation and the Russell Sage Foundation.

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“The American Dream will remain out of reach until all of our children have equal educational opportunities. My hope is that this data will help in the hard work of bringing the dream closer to reality.”

Sean Reardon

Funders

The Segregation Explorer’s data and website are generously supported by the following funders:

The construction of the Segregation Explorer data and website has been supported by grants from the Russell Sage Foundation (1911-19449), the Robert Wood Johnson Foundation (81086), and the Bill and Melinda Gates Foundation. Any opinions expressed are those of the principal investigators alone and should not be construed as representing the opinions of the Russell Sage Foundation, Robert Wood Johnson Foundation, or Bill and Melinda Gates Foundation.