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Information on IES-Funded Research
Grant Closed

The Effects of Racial School Segregation on the Black-White Achievement Gap

NCER
Program: Education Research Grants
Program topic(s): Improving Education Systems
Award amount: $500,000
Principal investigator: Sean Reardon
Awardee:
Stanford University
Year: 2007
Project type:
Exploration
Award number: R305A070377

Purpose

Over the last few decades, black students have scored on average from one-half to one standard deviation below white students on national tests of math and reading. The primary research question addressed in this project is whether school segregation patterns (both racial and economic) account for some of the variation in the racial achievement gap. More specifically, are policies and school assignment practices aimed at reducing racial segregation associated with reductions in racial achievement gaps?

Project Activities

The research team's first activities are to assemble racial achievement gap data from state assessment data systems for the years 1995-96 through 2006-07, compile data on the history of court-mandated desegregation, and draw data on segregation levels from the Common Core of Data (CCD), a publicly available dataset compiled annually since 1986-1987 by the National Center for Education Statistics. The CCD contains school- and district-level data for all public schools in the United States, including enrollment counts by grade, race, and free/reduced-price lunch eligibility status. The researchers use these counts to compute yearly segregation levels (both racial and economic segregation). With these data, the researchers will describe the patterns and trends in black-white achievement gaps on state assessments, estimate the effect of the release from court-ordered desegregation on achievement gaps, and estimate the effects of changes in levels of within-district segregation on within-district achievement gaps.

Structured Abstract

Setting

The research team is examining data on district-level achievement gaps using state assessment data systems for the years 1995-96 through 2006-07. Data on district-level segregation levels are drawn from the Common Core of Data (CCD), a publicly available dataset compiled annually since 1986-87 by the National Center for Education Statistics. The CCD contains school- and district-level data for all public schools in the United States, including enrollment counts by grade, race, and free/reduced-price lunch eligibility status. The researchers use these counts to compute yearly within-district, between-school segregation levels (both racial and economic segregation).

Sample

Participants are public school students in third through 12th grades in all 50 U.S. states. The data are aggregated by race for all districts in the United States.

Research design and methods

This is a secondary data analysis of longitudinal state assessment data by district. The release of many school districts from court-ordered desegregation during this time period provides a source of external variation in segregation levels that will be used to examine the relation between racial segregation and racial achievement gaps.

Key measures

The key measures are district-level black-white disparities on state assessments in math, reading, and other subjects as available.

Data analytic strategy

A variety of data analytic strategies (e.g., fixed effects regression models, instrumental variable regression models) are used to attempt to isolate the relationship between of levels of black-white segregation and student achievement.

People and institutions involved

IES program contact(s)

Katina Stapleton

Education Research Analyst
NCER

Products and publications

Products: Products from this project include published reports on the relations between patterns of racial school segregation and the black-white achievement gap.

Book chapter

Reardon, S.F., and Rhodes, L. (2011). The Effects of Socioeconomic School Integration Plans on Racial School Desegregation. In E. Frankenberg, and E. DeBray (Eds.), Integrating Schools in a Changing Society: New Policies and Legal Options for a Multiracial Generation (pp. 187-208). Chapel Hill, NC: University of North Carolina Press.

Reardon, S.F., Yun, J.T., Chmielewski, A.K. (2012). Suburbanization and School Segregation. In W.F. Tate (Ed.), Research on Schools, Neighborhoods, and Communities: Toward Civic Responsibility (pp. 85-103). Washington, DC: American Educational Research Association.

Journal article, monograph, or newsletter

Ho, A.D., and Reardon, S.F. (2012). Estimating Achievement Gaps From Test Scores Reported in Ordinal Proficiency Categories. Journal of Educational and Behavioral Statistics, 37 (4): 489-517.

Reardon, S.F., Grewal, E., Kalogrides, D., and Greenberg, E. (2012). Brown Fades: The End of Court-Ordered School Desegregation and the Resegregation of American Public Schools. Journal of Policy Analysis and Management, 31 (4): 876-904.

Working paper

Reardon, S.F. (2008). Differential Growth in the Black-White Achievement Gap During Elementary School Among Initially High- and Low-Scoring Students (2008-07). Stanford University: Institute for Research on Education Policy and Practice Working Paper.

Reardon, S.F. (2008). Thirteen Ways of Looking at the Black-White Test Score Gap (2008-08). Stanford University: Institute for Research on Education Policy and Practice Working Paper.

** This project was submitted to and funded under Education Policy, Finance, and Systems in FY 2007.

Related projects

Addressing Practical Problems in Achievement Gap Estimation: Nonparametric Methods for Censored Data

R305D110018

Supplemental information

Purpose: The primary research question addressed in this project is whether school segregation patterns (both racial and economic segregation) account for some of the variation in the racial achievement gap. More specifically, are policies and school assignment practices aimed at reducing racial segregation associated with reductions in racial achievement gaps?

Questions about this project?

To answer additional questions about this project or provide feedback, please contact the program officer.

 

Tags

Policies and StandardsMathematicsData and Assessments

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Questions about this project?

To answer additional questions about this project or provide feedback, please contact the program officer.

 

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