Abstract
Educational disparities remain a key contributor to increasing social and wealth inequalities. To address this, researchers and policymakers have focused on average differences between racial groups or differences among students who are falling behind1. This focus potentially leads to educational triage, diverting resources away from high-achieving students, including those from racial minorities2,3. Here we focus on the ‘racial excellence gap’—the difference in the likelihood that students from racial minorities (Black and Hispanic) reach the highest levels of academic achievement compared with their non-minority (white and Asian) peers. There is a shortage of evidence that systematically measures the magnitude of the excellence gap and how it evolves4,5. Using longitudinal, statewide, administrative data, we document eight facts regarding the excellence gap from third grade (typically ages 8–9) to high school (typically ages 14–18), link the stability of excellence gaps and student backgrounds, and assess the efficacy of public policies. We show that excellence gaps in maths and reading are evident by the third grade and grow slightly over time, especially for female students. About one third of the gap is explained by a student’s socioeconomic status, and about one tenth is explained by the school environment. Top-achieving racial minority students are also less likely to persist in excellence as they progress through school. Moreover, state accountability policies that direct additional resources to reduce non-race-based inequality6 had minimal effects on the racial excellence gaps. Documenting these patterns is an important step towards eliminating excellence gaps and removing the ‘racial glass ceiling’.
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Fig. 1: Racial excellence gaps from third to eighth grade.
Fig. 2: Excellence gaps across schools.
Fig. 3: Effect of Focus designations on excellence gaps.
Fig. 4: Eighth grade excellence by third grade rank.
Fig. 5: Excellence in college readiness.
Fig. 6: Explaining racial differences in persistence and entry of excellence.
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Data availability
This research used data structured and maintained by the MERI–Michigan Education Data Center (MEDC). MEDC data are modified for analysis purposes using rules governed by MEDC and are not identical to those data collected and maintained by the Michigan Department of Education (MDE) and/or Michigan’s Center for Educational Performance and Information (CEPI). Results, information and opinions solely represent the analysis, information and opinions of the authors and are not endorsed by, or reflect the views or positions of, grantors, MDE and CEPI or any employee thereof. Researchers interested in accessing these data can submit a request to MEDC through https://medc.miedresearch.org. There are no constraints to submit a proposal.
Code availability
Code used in this paper will be publicly available at https://www.openicpsr.org/openicpsr/project/215681/version/V1/view.
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Acknowledgements
The authors thank D. Neal and P. Peña for helpful comments; S. Hemelt and B. Jacob for sharing their code; G. Barcelo, C. Brobst, J. Cipriano, Z. El-Kaissi, B. Hammer, A. Listo, A. Mryan, F. Pagnotta, L. Ramirez, S. Shi, R. Sharma, C. Smith and J. Wang for research assistance with the literature review.
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Authors and Affiliations
Department of Economics, Columbia University, New York, NY, USA
Uditi Karna
Department of Economics, University of Chicago, Chicago, IL, USA
Min Sok Lee, John A. List & Haruka Uchida
Research School of Economics, Australian National University, Canberra, Australian Capital Territory, Australia
John A. List
NBER, Cambridge, MA, USA
John A. List
Frank Batten School of Leadership and Public Policy, University of Virgina, Charlottesville, VA, USA
Andrew Simon
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Uditi Karna
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Contributions
J.A.L., M.S.L. and H.U. conceived the research questions and acquired the data. U.K., J.A.L., A.S. and H.U. formulated the methodological approach, and U.K., A.S. and H.U. analysed the data. U.K., A.S., J.A.L. and H.U. wrote the manuscript. The authors are listed in alphabetical order.
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Extended data figures and tables
Extended Data Fig. 1 Percentage change in excellence gap from 3rd grade.
This figure displays the percentage change in this difference between minority status in reaching excellence, relative to the difference in the 3rd grade. Excellence is defined as reaching the top decile on the math statewide standardized exam, MEAP for all years until 2014 and M-STEP for all years after 2014. Analysis uses our main sample, as defined in Methods, which consists of 534,122 students in total: 136,137 classified as a racial minority and 397,985 as a non-minority. Error bars reflect 95% confidence intervals around the mean.
Extended Data Fig. 2 Excellence gaps by gender.
These figures show for female and male students the proportion of each minority group that reaches excellence in (a) math and (b) reading. Analysis uses our main sample, as defined in Methods, which consists of 534,122 students in total: 136,137 classified as a racial minority and 397,985 as a non-minority. Error bars reflect 95% confidence intervals around the mean.
Extended Data Fig. 3 Gaps in college readiness (AP coursework).
This figure displays (a) the percentage of each race/ethnicity group that takes at least one AP course or takes at least one STEM AP Course and (b) the Gelbach decomposition of each minority non-minority difference. “Economically disadvantaged” is an indicator for whether a student is classified as economically disadvantaged by Michigan which is defined by whether the student is determined to be eligible for free or reduced-price meals, are in households receiving food or cash assistance, are homeless, are migrant, or are in foster care. Analysis uses our high school sample, as defined in Methods, which consists of 244,750 students in total: 58,370 classified as a racial minority and 186,380 as a non-minority. Error bars reflect 95% confidence intervals around the mean.
Extended Data Fig. 4 Excellence in the SAT math section by 8th grade rank.
This figure shows the proportion of students who score in the top decile of the SAT math section nationally, and the distribution of 8th grade achievement in math for each percentile in the top 10 percent. Shaded lines around the two lines correspond to the 95% confidence intervals and shaded areas reflect the density of students at each achievement level. Analysis uses our high school sample, as defined in Methods, which consists of 244,750 students in total: 58,370 classified as a racial minority and 186,380 as a non-minority.
Extended Data Fig. 5 Explaining racial differences in persistence and entry in college readiness (AP coursework).
This figure displays the Gelbach decomposition of each minority non-minority difference in whether a student takes at least one AP course or takes at least one STEM AP Course, among students who achieved excellence in 8th grade math (for Persistence) and students who did not achieve excellence in 8th grade math (for Entry). “Economically disadvantaged” is an indicator for whether a student is classified as economically disadvantaged by Michigan which is defined by whether the student is determined to be eligible for free or reduced-price meals, are in households receiving food or cash assistance, are homeless, are migrant, or are in foster care. Analysis uses our high school sample, as defined in Methods, which consists of 244,750 students in total: 58,370 classified as a racial minority and 186,380 as a non-minority.
Extended Data Table 1 Population characteristics
Full size table
Extended Data Table 2 Predicting excellence gaps across schools
Full size table
Supplementary information
Supplementary Information
This file contains 35 display items and descriptions of supplementary methods. These items analyse the gap along various dimensions (such as socioeconomic status, grade, school, individual race groups and gender), further decompose main results across the distribution of achievement, and provide additional analyses of the accountability policy and the gap across schools.
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Karna, U., Lee, M.S., List, J.A. et al. Measuring racial educational disparities over time amongst top achievers. Nature (2025). https://doi.org/10.1038/s41586-024-08536-0
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Received:25 March 2024
Accepted:17 December 2024
Published:12 February 2025
DOI:https://doi.org/10.1038/s41586-024-08536-0
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