Built Into the System: How Wealth, Geography, and Segregation Shape Racial Achievement Gaps (2009–2018)

Abstract

This study analyzes racial achievement gaps across approximately 13,000 U.S. school districts using the Stanford Education Data Archive (SEDA), Version 5.0, covering 2009–2018. Through interactive data visualization, this paper examines Black–White and Hispanic–White achievement gaps in math and reading, investigating their geographic distribution and relationship to socioeconomic factors. Key findings reveal that: (1) achievement gaps are pervasive, averaging approximately 2 standard deviations nationally; (2) gaps are geographically concentrated, with Midwestern states and the District of Columbia showing the largest disparities; (3) gaps widen as students progress from 3rd to 8th grade, particularly in mathematics; (4) socioeconomic status is the strongest predictor of gap magnitude, with wealthier districts paradoxically exhibiting larger gaps; and (5) urban and suburban districts show larger gaps than rural areas. Multiple regression analysis confirms that SES, poverty rate, and racial composition significantly predict gap size. These findings suggest that educational inequality is structurally embedded in American schooling, with implications for targeted policy interventions.

1 Introduction

In the United States, a student’s race remains one of the strongest predictors of their academic outcomes. Black and Hispanic students consistently score lower than their White peers on standardized tests—a pattern known as the racial achievement gap. These gaps are not new; they have persisted for decades despite billions of dollars invested in educational reform, from No Child Left Behind to the Every Student Succeeds Act.

But here is what makes these gaps so troubling: they are not random. They follow patterns. They cluster in certain states, certain types of districts, and certain economic contexts. This suggests that achievement gaps are not simply the result of individual differences in ability or effort—they are built into the system itself.

This paper uses data visualization to make these structural patterns visible. By mapping achievement gaps across more than 13,000 U.S. school districts, I reveal where gaps are largest, how they have changed over time, and what factors predict their magnitude. The findings challenge conventional assumptions about educational inequality and point toward new directions for policy intervention.

2 Background and Context

Achievement gaps between White students and students of color have been documented since the 1960s, when the landmark Coleman Report first revealed stark racial disparities in educational outcomes. Since then, researchers have tracked these gaps using data from the National Assessment of Educational Progress (NAEP), state assessments, and international tests. The consistent finding is that while gaps have narrowed somewhat since the 1970s, they remain large—typically ranging from 0.5 to 1.0 standard deviations nationally.

What causes these gaps? Researchers have identified multiple contributing factors, including differences in family income, parental education, school funding, teacher quality, and exposure to environmental stressors like poverty and neighborhood violence. Critically, these factors are not equally distributed across racial groups. Centuries of discriminatory policies—from slavery to Jim Crow to redlining—have concentrated Black and Hispanic families in under-resourced neighborhoods with under-funded schools.

Understanding the geography of achievement gaps is therefore essential. Gaps are not uniform across the country; they vary dramatically from state to state and district to district. Some of this variation reflects differences in policy, funding, and school quality. But much of it reflects the legacy of segregation—the ways that housing policy, school district boundaries, and resource allocation have systematically disadvantaged students of color.

3 Research Questions

This study addresses three central research questions:

1. How large are racial achievement gaps in district-level academic performance across the United States?
2. How are these gaps geographically distributed across states and locale types (urban, suburban, town, rural)?
3. How do socioeconomic and demographic contexts help explain variation in gap magnitude?

4 Significance and Objectives

Why does this matter? Achievement gaps have consequences that extend far beyond test scores. Students who fall behind academically are less likely to graduate high school, attend college, or secure stable employment. Racial gaps in achievement contribute to racial gaps in income, wealth, and opportunity—perpetuating inequality across generations.

Understanding where gaps are largest and why is the first step toward closing them. This paper contributes to that understanding by:

1. Describing the distribution of Black–White and Hispanic–White achievement gaps across U.S. districts
2. Mapping the geographic patterns of gaps across states
3. Examining how gaps vary by grade level and over time
4. Identifying the socioeconomic and demographic predictors of gap magnitude
5. Demonstrating how interactive data visualization can illuminate patterns of educational inequity

The findings reveal a paradox: achievement gaps are often largest in wealthy, suburban districts—the very places we might expect to have the resources to close them. This “paradox of privilege” suggests that resources alone are not enough. Addressing achievement gaps requires confronting the structural forces—segregation, tracking, and opportunity hoarding—that produce them.

5 Literature Review

5.1 Racial Achievement Gaps in American Education

The study of racial achievement gaps has a long history in American educational research. The 1966 Coleman Report first brought national attention to disparities in educational outcomes between White students and students of color, finding that family background—not school resources—was the primary predictor of student achievement. This finding sparked decades of debate about the relative importance of schools versus families in shaping educational outcomes.

Since then, researchers have consistently documented the persistence of racial achievement gaps. Fryer and Levitt (2004) analyzed data from the Early Childhood Longitudinal Study and found that Black-White test score gaps emerge before children enter kindergarten, suggesting that early childhood experiences play a critical role. In a follow-up study, Fryer and Levitt (2006) showed that these gaps widen during the first years of schooling, indicating that school experiences also contribute to disparities.

Reardon (2011) provided a comprehensive analysis of trends in achievement gaps over time, documenting that while Black-White gaps narrowed substantially between 1970 and 1990, progress has since stalled. More troubling, Reardon found that the income achievement gap—the gap between students from high-income and low-income families—has widened dramatically, now exceeding the racial gap in magnitude. Hanushek and colleagues (2019) confirmed that despite decades of reform efforts, racial achievement gaps remain stubbornly persistent, with Black students scoring roughly 0.5 to 0.7 standard deviations below White students nationally.

5.2 Socioeconomic Factors and Educational Inequality

A substantial body of research links achievement gaps to socioeconomic inequality. Duncan and Murnane (2011) documented the growing importance of family income in predicting children’s educational outcomes, arguing that rising income inequality has translated into rising educational inequality. Children from affluent families have access to better schools, more enrichment activities, and greater parental investment in their education.

Reardon and Portilla (2016) found some encouraging news: income-based gaps in school readiness narrowed between 1998 and 2010, potentially due to increased public investment in early childhood education. However, these gains have not fully translated into narrower gaps in later grades, suggesting that K-12 schooling may reinforce rather than reduce early disparities.

The relationship between race and socioeconomic status complicates the study of achievement gaps. As Rothstein (2017) documented in The Color of Law, contemporary racial segregation is not the result of private choices but of deliberate government policy—including redlining, restrictive covenants, and discriminatory lending practices that concentrated Black families in impoverished neighborhoods. These policies created the conditions for educational inequality by systematically excluding Black families from wealth-building opportunities and well-resourced schools.

Owens, Reardon, and Jencks (2016) showed that income segregation between school districts has increased substantially since 1990, with affluent families increasingly concentrated in separate districts from low-income families. This sorting has profound implications for educational equity, as school district boundaries determine access to resources, advanced coursework, and experienced teachers.

5.3 Geographic Dimensions of Educational Disparities

Recent research has turned attention to the geography of achievement gaps. Using data from the Stanford Education Data Archive (SEDA), Reardon and colleagues (2019) documented enormous variation in achievement gaps across states and districts. Some districts have virtually no gap, while others show disparities exceeding 3 standard deviations.

Fahle and Reardon (2018) found that geographic variation in achievement gaps is only partially explained by differences in socioeconomic composition. Even after controlling for poverty and income, substantial differences remain across districts, suggesting that local policies, school practices, and historical contexts also matter.

Perhaps most surprisingly, research has shown that achievement gaps are often largest in affluent, suburban districts. Card and Rothstein (2007) found that school segregation explains a substantial portion of the Black-White test score gap, but that segregation operates differently in different contexts. In suburban districts, Black students may attend the same schools as White students but experience very different opportunities within those schools—through tracking, discipline disparities, and differential access to advanced coursework.

Logan and Burdick-Will (2016) examined urban-suburban differences in educational opportunity, finding that suburban schools are not uniformly advantaged. Instead, the benefits of suburban schooling accrue primarily to White and affluent students, while students of color in suburban districts often face isolation and marginalization.

5.4 Gaps in the Literature

Despite this rich body of research, several gaps remain. First, most studies of achievement gaps rely on national or state-level data, obscuring the substantial variation that exists across districts. District-level analysis is essential for understanding how local contexts shape educational inequality.

Second, while researchers have documented the existence of geographic variation in achievement gaps, fewer studies have used interactive visualization to make these patterns accessible to policymakers and the public. Static tables and regression coefficients can obscure the human geography of inequality.

Third, the relationship between socioeconomic status and achievement gaps deserves further examination. While it is well established that poverty predicts lower achievement, the finding that wealthy districts often have larger gaps challenges conventional assumptions and requires explanation.

5.5 The Present Study

This study addresses these gaps by leveraging the Stanford Education Data Archive to visualize racial achievement gaps across approximately 13,000 U.S. school districts. Using interactive maps, scatterplots, and regression analysis, I examine the geographic distribution of gaps and their relationship to socioeconomic and demographic factors.

The study makes three contributions to the literature. First, it provides a comprehensive descriptive portrait of district-level achievement gaps, documenting their distribution and variation across the country. Second, it uses interactive visualization to make structural patterns of inequality visible and accessible. Third, it examines the “paradox of privilege”—the finding that wealthy districts often have larger gaps—and explores potential explanations for this counterintuitive pattern.

By making the structure of educational inequality visible, this study aims to inform policy interventions and public understanding of one of America’s most persistent challenges.

6 Methodology

6.1 Data Source

This study uses the Stanford Education Data Archive (SEDA), Version 5.0, developed by researchers at Stanford University’s Educational Opportunity Project (Reardon et al., 2021). SEDA provides the most comprehensive publicly available data on academic achievement in U.S. public schools, covering approximately 13,000 school districts, 50 states, and over 200 million test scores.

SEDA draws on state accountability assessments administered annually to students in grades 3–8 as required by federal law. Because each state uses different tests with different scales, SEDA uses statistical linking methods to place all scores on a common, nationally normed scale. Scores are expressed in grade-level standard deviation units, where 0 represents the national average and 1 represents one standard deviation above the mean. This allows for meaningful comparisons across states and over time.

The dataset covers the years 2009–2018, providing a decade of achievement data spanning the Obama administration’s Race to the Top initiative and the transition from No Child Left Behind to the Every Student Succeeds Act. This time period captures both the aftermath of the Great Recession and subsequent recovery, allowing for analysis of how economic conditions may have affected achievement patterns.

6.2 Variables and Measures

6.2.1 Outcome Variable: Achievement Gaps

The primary outcome variable is the racial achievement gap, calculated as the difference in mean achievement between White students and students of color:

\[Gap_{BW} = \bar{Y}_{White} - \bar{Y}_{Black}\]

\[Gap_{HW} = \bar{Y}_{White} - \bar{Y}_{Hispanic}\]

where \(\bar{Y}\) represents mean achievement in grade-level standard deviation units. Positive values indicate that White students outperform the comparison group; negative values would indicate the reverse. Gaps are calculated separately for math and reading (English Language Arts).

To create stable district-level estimates, I pooled data across all available years (2009–2018) and grades (3–8), calculating the median gap for each district. This approach reduces noise from year-to-year fluctuations and provides a more reliable estimate of each district’s typical achievement gap.

6.2.2 Predictor Variables

Key predictor variables were drawn from the SEDA covariates file, which contains district-level demographic and socioeconomic information:

Variable	Description	Measurement
Socioeconomic Status (SES)	District average socioeconomic status	Composite z-score based on median income, poverty rate, unemployment, and adult educational attainment
Poverty Rate	Proportion of students eligible for free/reduced-price lunch	Proportion (0–1)
Percent Black	Proportion of Black students in the district	Proportion (0–1)
Percent Hispanic	Proportion of Hispanic students in the district	Proportion (0–1)
Locale Type	Urban, suburban, town, or rural classification	Categorical (4 levels)

For regression analysis, continuous predictors (SES, poverty rate, percent Black) were standardized to z-scores to allow comparison of effect sizes across variables with different scales.

6.3 Analytical Approach

The analysis proceeds in four stages, moving from description to explanation:

6.3.1 Stage 1: Descriptive Visualization

I first describe the distribution of achievement gaps across U.S. districts using density plots. This establishes the overall magnitude of gaps and their variation across districts, answering the question: How large are racial achievement gaps?

6.3.2 Stage 2: Geographic Mapping

Next, I map achievement gaps at the state level using interactive choropleth maps. This reveals geographic patterns in gaps, answering the question: Where are gaps largest and smallest?

6.3.3 Stage 3: Bivariate Analysis

I then examine the relationship between gaps and potential predictors using scatterplots. This explores associations between SES, poverty, racial composition, and gap magnitude, answering the question: What factors are associated with larger or smaller gaps?

6.3.4 Stage 4: Regression Analysis

Finally, I estimate a multiple regression model to assess the independent contribution of each predictor:

\[Gap_i = \beta_0 + \beta_1 SES_i + \beta_2 Poverty_i + \beta_3 PercentBlack_i + \epsilon_i\]

where \(Gap_i\) is the median Black–White math gap for district \(i\), and the \(\beta\) coefficients represent the change in the gap associated with a one standard deviation increase in each predictor, holding other variables constant.

This model answers the question: Which factors most strongly predict achievement gap magnitude when controlling for other variables?

6.4 Tools and Software

All analyses were conducted in R (version 4.3). Data manipulation and visualization used the tidyverse suite of packages (Wickham et al., 2019). Interactive visualizations were created using plotly (Sievert, 2020), which enables hover tooltips, zooming, and dynamic exploration of the data. Maps were generated using plotly’s choropleth functionality with Albers USA projection.

The choice to use interactive visualization is deliberate. Static charts can display patterns, but interactive visualization allows users to explore the data—hovering over individual districts to see their names and characteristics, zooming into regions of interest, and engaging with the data as active participants rather than passive observers. This approach aligns with the study’s goal of making structural inequality visible and accessible.

6.5 Limitations

Several limitations should be noted. First, SEDA data are based on state accountability tests, which vary in difficulty and content across states. Although SEDA uses linking methods to create comparable scores, some measurement error may remain.

Second, the analysis is cross-sectional. When examining gaps by grade level, I compare different students at different grades in the same year, not the same students as they progress through school. Longitudinal analysis would provide stronger evidence about how gaps change over time within cohorts.

Third, the analysis cannot establish causality. The associations between SES, poverty, and achievement gaps may reflect omitted variables such as school funding, teacher quality, curriculum, or neighborhood characteristics not captured in the SEDA covariates.

Despite these limitations, SEDA remains the best available data source for examining district-level achievement gaps at national scale, and the patterns documented here provide important insights into the structure of educational inequality in America.

7 Findings

This section presents the results of my analysis, organized around the three research questions: (1) the magnitude and distribution of achievement gaps, (2) geographic patterns, and (3) the relationship between socioeconomic factors and gap size.

7.1 Distribution of Achievement Gaps

I begin by examining how racial achievement gaps are distributed across U.S. school districts. Figure 1 displays density plots showing the distribution of Black–White and Hispanic–White gaps in both math and reading.

Key findings from Figure 1:

• Achievement gaps are pervasive. The vast majority of districts show positive gaps, meaning White students outperform Black and Hispanic students.
• The median Black–White gap is approximately 1.8 SD in both math and reading—equivalent to nearly two years of learning.
• Hispanic–White gaps are slightly smaller but follow a similar distribution.
• There is substantial variation across districts. Some districts have gaps exceeding 4 SD, while a small number show negative gaps.

7.2 Math vs. Reading Achievement Gaps

Figure 2 examines the relationship between math and reading gaps within districts.

Key findings: Math and reading gaps are strongly correlated within districts, suggesting that the factors driving gaps operate across subjects.

7.3 Geographic Patterns: State-Level Achievement Gaps

Achievement gaps are not randomly distributed but follow clear geographic patterns. The following maps display state-level median gaps.

7.3.1 Black–White Math Gaps by State

7.3.2 Black–White Reading Gaps by State

Key findings from the maps:

• The District of Columbia has the largest achievement gaps in the nation (~4.4 SD in math).
• Midwestern states—including Minnesota, Wisconsin, Nebraska, and Iowa—show surprisingly large gaps.
• Southern states generally show smaller gaps, though this reflects lower overall achievement rather than greater equity.

7.3.3 State Rankings

7.4 Animated Maps: How Have Gaps Changed Over Time?

The following animated maps show how achievement gaps have evolved from 2009 to 2018.

7.4.1 Black–White Gaps Over Time

7.4.2 Hispanic–White Gaps Over Time

7.5 Achievement Gaps Across Grade Levels

Do achievement gaps widen as students progress through school? The following figures examine how gaps vary from 3rd to 8th grade.

7.5.1 Black–White Gaps by Grade

Key finding: Math gaps widen substantially from Grade 3 (~1.7 SD) to Grade 8 (~2.2 SD), representing a 35% increase. Reading gaps remain relatively flat.

7.5.2 Hispanic–White Gaps by Grade

7.6 Urban-Rural Differences in Achievement Gaps

How do achievement gaps vary by locale type?

7.6.1 Math Gaps by Locale

7.6.2 Reading Gaps by Locale

Key findings: Urban and suburban districts have the largest achievement gaps (~1.9-2.0 SD), while rural districts have the smallest (~1.5 SD). This challenges the assumption that gaps are primarily a problem of under-resourced urban schools.

7.7 Socioeconomic Predictors of Achievement Gaps

7.7.1 SES and Black–White Gaps

Key finding: There is a strong positive relationship between district SES and achievement gaps—wealthier districts have larger gaps. This “paradox of privilege” challenges conventional assumptions.

7.7.2 SES and Hispanic–White Gaps

7.7.3 The Segregation Story

Key finding: The color gradient reveals structural segregation—as percent Black increases, district SES decreases (colors shift from teal to brown). Black students are systematically concentrated in lower-SES districts.

7.7.4 The Poverty Double-Bind

7.8 Regression Analysis: What Predicts Achievement Gaps?

To assess the independent contribution of each factor, I estimated a multiple regression model.

7.8.1 Coefficient Plot

Regression findings: SES is the dominant predictor (β ≈ 0.87), followed by poverty rate (β ≈ 0.62) and percent Black students (β ≈ 0.11). All predictors are statistically significant.

7.8.2 3D Regression Surface

The 3D surface visually confirms that SES is the dominant predictor—the surface rises most steeply along the SES axis.

8 Discussion and Conclusion

8.1 Summary of Findings

This study examined racial achievement gaps across approximately 13,000 U.S. school districts using the Stanford Education Data Archive (SEDA). The analysis revealed five key findings:

1. Achievement gaps are pervasive. The median Black–White gap is approximately 1.8 standard deviations—equivalent to nearly two years of learning. Virtually every district in America shows gaps favoring White students.

2. Gaps are geographically concentrated. The District of Columbia, Idaho, Minnesota, Wisconsin, and other Midwestern states show the largest gaps. Southern states generally show smaller gaps, though this reflects lower overall achievement rather than greater equity.

3. Math gaps widen across grade levels. Black–White gaps in mathematics increase by 35% from 3rd to 8th grade, while reading gaps remain relatively flat. This suggests cumulative disadvantage in mathematics instruction.

4. Wealthy districts have the largest gaps. Contrary to conventional assumptions, high-SES districts show larger achievement gaps than low-SES districts—a finding I term the “paradox of privilege.”

5. SES is the dominant predictor. Regression analysis confirms that socioeconomic status is by far the strongest predictor of gap magnitude (β = 0.87), followed by poverty rate (β = 0.62) and percent Black students (β = 0.11).

8.2 Interpretation: The Paradox of Privilege

The finding that wealthy districts have larger achievement gaps demands explanation. How can districts with the most resources show the greatest disparities?

Research by Reardon, Kalogrides, and Shores (2019) offers insight. Using SEDA data, they found that racial achievement gaps are largest in districts where White families have high socioeconomic status and Black families have relatively lower status—that is, where within-district inequality is greatest. In affluent suburbs, White students benefit from extensive resources: experienced teachers, advanced coursework, private tutoring, and enrichment activities. Black students in those same districts often do not have equal access to these opportunities.

This pattern reflects what scholars call “opportunity hoarding”—the tendency of affluent families to secure advantages for their children in ways that exclude others (Lewis & Diamond, 2015). In suburban schools, this manifests through tracking systems that sort students into different academic pathways, with Black students disproportionately placed in lower tracks (Tyson, 2011). It also operates through discipline disparities, with Black students facing harsher punishments for similar behaviors (Skiba et al., 2011), and through differential access to gifted programs and Advanced Placement courses (Ford, 2014).

The paradox of privilege thus reveals that resources alone do not close gaps. What matters is how resources are distributed—and in American schools, they are distributed unequally along racial lines even within the same districts.

8.3 The Compounding of Math Gaps

The finding that math gaps widen from 3rd to 8th grade while reading gaps remain flat has important implications. This pattern is consistent with research on the cumulative nature of mathematics learning. As Claessens and Engel (2013) demonstrated, early math skills are the strongest predictor of later academic achievement—stronger even than early reading skills or socio-emotional development.

Mathematics is hierarchical: fractions build on multiplication, algebra builds on fractions, and calculus builds on algebra. Students who fall behind early face compounding disadvantage, as each new concept depends on mastery of previous ones (Morgan, Farkas, & Wu, 2009). For Black students who enter school with fewer opportunities for early math exposure—due to disparities in preschool quality, parental resources, and neighborhood conditions—the trajectory is difficult to reverse.

This finding aligns with research by Reardon and Galindo (2009), who documented that Hispanic–White math gaps also widen during elementary school, particularly for students from non-English-speaking households. The pattern suggests that schools are not effectively remediating early disparities in mathematics—and may in fact be exacerbating them.

8.4 Geographic Patterns and the Legacy of Segregation

The geographic concentration of achievement gaps—particularly the large gaps in Midwestern states—reflects the legacy of racial segregation. Although the Midwest is often perceived as racially homogeneous, cities like Minneapolis, Milwaukee, Chicago, and Detroit have among the highest levels of residential segregation in the nation (Logan & Stults, 2011).

Rothstein (2017) documented how federal housing policy deliberately created and maintained segregation in northern cities through redlining, restrictive covenants, and discriminatory lending. The consequences persist today: Black families remain concentrated in neighborhoods with under-resourced schools, while White families have access to well-funded suburban districts.

Card and Rothstein (2007) found that school segregation explains a substantial portion of the Black–White test score gap, with the effect strongest in metropolitan areas with high levels of residential segregation. The Midwest’s large achievement gaps likely reflect this history: highly segregated metropolitan areas with stark boundaries between affluent White suburbs and impoverished Black urban cores.

8.5 Policy Implications

These findings suggest several directions for policy intervention:

First, target interventions to high-SES districts. Conventional wisdom holds that achievement gaps are a problem of under-resourced urban schools. This study shows that gaps are equally large—or larger—in affluent suburbs. Policies should address opportunity hoarding, tracking, and within-school segregation in these contexts.

Second, prioritize early mathematics instruction. The widening of math gaps across grade levels underscores the importance of early intervention. High-quality early childhood education with strong mathematics components may help close gaps before they compound (Duncan et al., 2007).

Third, address structural segregation. Achievement gaps are fundamentally a product of segregation—both between and within districts. As Reardon and Owens (2014) argued, school integration remains one of the most effective tools for reducing achievement gaps, yet it has largely disappeared from the policy agenda.

Fourth, examine within-school practices. Even in integrated schools, Black students often experience different opportunities than White students through tracking, discipline, and access to advanced coursework. Detracking reforms and culturally responsive pedagogy may help address these within-school disparities (Oakes, 2005; Ladson-Billings, 1995).

8.6 Limitations

Several limitations should be acknowledged. First, this analysis is cross-sectional: I compare students at different grade levels in the same year, not the same students over time. Longitudinal data would provide stronger evidence about how gaps change within cohorts.

Second, the analysis cannot establish causality. The association between SES and achievement gaps may reflect omitted variables such as school funding formulas, teacher quality, curriculum, or neighborhood characteristics not captured in the SEDA covariates.

Third, district-level analysis masks substantial within-district variation. Some schools within high-gap districts may have successfully closed gaps through effective practices. Future research should examine school-level data to identify positive outliers.

Fourth, the analysis focuses on test scores, which capture only one dimension of educational inequality. Gaps in graduation rates, college enrollment, and long-term outcomes may show different patterns.

8.7 Conclusion

This study set out to answer three questions: How large are racial achievement gaps? Where are they concentrated? And what factors predict their magnitude?

The answers are sobering. Achievement gaps are large, pervasive, and structurally embedded in American education. They are concentrated not in the places we might expect—not primarily in poor rural areas or struggling urban schools—but in wealthy suburban districts where resources are abundant but unequally distributed.

The title of this paper—“Built Into the System”—reflects the central finding. Achievement gaps are not accidents or anomalies. They are the predictable product of a system designed to sort and stratify students by race and class. They follow geographic patterns that mirror the history of segregation. They compound over time as students progress through schools that fail to remediate early disparities. And they persist in wealthy districts where opportunity hoarding ensures that advantages flow to those who already have the most.

Making these patterns visible is the first step toward changing them. The interactive visualizations presented in this paper reveal the structure of educational inequality in ways that tables and regression coefficients cannot. They show that every state, every district, every school operates within a system that produces racial disparities.

Closing achievement gaps will require more than additional resources or better teaching. It will require confronting the structural forces—segregation, tracking, opportunity hoarding—that produce gaps in the first place. As Ladson-Billings (2006) argued, we should speak not of an “achievement gap” but of an “education debt”—the accumulated deficit of educational opportunity owed to Black students after centuries of exclusion.

The data presented here document the size of that debt. Paying it will require transforming not just schools, but the systems of inequality in which they are embedded.

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