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Segregation strongly reflects local demographics and housing patterns. For example, rural and suburban schools are more heterogeneous than ...
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SEGREGATION, RACE, AND CHARTER SCHOOLS: WHAT DO WE KNOW?
This report was supported by a grant from the Walton Family Foundation to study racial segregation in traditional and charter public schools. We would like to thank Elizabeth Roberto for commenting on our section concerning her divergence index, and to Jonathan Rothwell for his suggestions.
Figure 1. Dissimilarity index in Southern and non-Southern school districts Figure 2. Metropolitan-level black-white dissimilarity index, 1970- Figure 3. Public school enrollment by race, 1968- Figure 4. Falling exposure rates between 1988-2011: Perhaps not quite as bad as it first looks Figure 5. School racial segregation measures (within district) Figure 6. Exposure to low- and high-poverty schools by race, 2012- Figure 7. Public charter school students, by race/ethnicity: 2003-04 and 2013- Figure 8. Race and ethnicity of charter schools in Minneapolis-St. Paul, 2007- Figure 9. How different are charters and TPS from nearby schools? Figure 10. Variance in student achievement associated with differences among teachers/classrooms, schools, districts, and students Figure 11. Black-white student achievement and achievement gap, by black student density category, without and with accounting for student, teacher, and school characteristics, Grade 8 mathematics: 2011 Figure 12. Correlates of spatial variation in upward mobility Figure 13. Effect of low-poverty schools on the math scores of children in public housing Table 1. Coefficient estimates and hypothesis tests from multivariate regression models of the association between white-black achievement gap and segregation, 325 metropolitan areas, 2009- Table 2. Impacts of MTO on children’s income in adulthood W-2 earnings ($)
School segregation has returned to the front burner of public and political debate. Against the backdrop of police shootings and civic unrest in many U.S. cities, concerns about the role of public education in terms of race relations and segregation have grown. President Obama has also highlighted and put forward policies to address various dimensions of economic and racial inequality, including a proposal in his 2017 budget for a new $120 million grant program, “Stronger Together,” to support local efforts to integrate schools by income. In that context, this report compares various measures of school segregation and reviews research findings on the extent of school segregation, trends in school segregation over time, and the relationship between academic achievement and segregation by income and race. The role of school quality in mediating and moderating the associations between school segregation and academic achievement is examined through observational and experimental research findings. Research on charter schools receives particular attention. Findings include:
After Michael Brown was shot and killed by police in Ferguson, Missouri in August 2014, his grief-stricken mother addressed the media. “Do you know how hard it was for me to get him to stay in school and graduate?” she cried. “You know how many black men graduate? Not many. Because you bring them down to this type of level where they feel like they don’t got nothing to live for anyway." It was striking that, in a moment of such loss, Brown’s mother focused on her son’s education. He had been among the roughly 60 percent of students who managed to graduate from Normandy High School that year.^1 The proportion of the school’s students who are black is 98 percent. More than half a century ago, in Brown v. Board of Education , the U.S. Supreme Court invalidated state laws creating systems of separate and unequally resourced public schools for black and white students. While de jure segregation ended, de facto school segregation by race and class remained, and remains today. High levels of segregation are seen by many policymakers and educators as a serious barrier to economic opportunity for minority and low- income children, and to the wider benefits of a diverse and integrated society. School segregation, in particular, has returned to the forefront of public and political debate.^2 In part, this is because broader racial equity issues have been highlighted by recent police shootings, including Michael Brown in 2014 through to Terence Crutcher in Tulsa in September
patterns of human interaction, except perhaps a compulsory military draft. Further, there is a history of using these powers to positive effect for black students, through the court-ordered school desegregation plans put in place in many jurisdictions in the South following Brown v. Board.^4 Another reason public schools dominate discussions of segregation is the influence of a newly- available tool: large-scale longitudinal databases of education records, sometimes linked at the level of individual students to later outcomes such as employment and crime. Reardon, for example, uses test scores in from every school district in the U.S. to examine which forms of school segregation are most strongly associated with student achievement.^5 Chetty and his colleagues analyze IRS tax data on more than 40 million children and their parents, linked to Census and other administrative data, to identify features associated with the upward mobility of children, such as school quality and racial segregation.^6 These and other studies that take advantage of “big data” on schools and students provide increasingly precise descriptions of the extent and nature of school segregation. They have also provided the foundation for new research aimed at identifying the causal impacts of school segregation on student outcomes. Also important to the segregation discussion is the body of research on the impact of public charter schools. Whereas overall, charter schools across the nation perform only slightly better than regular public schools,^7 the story is different for a subset of charter schools serving overwhelmingly black and poor students in large cities with a so-called “no excuses” education model. Students in these schools have dramatically higher levels of achievement than comparable students attending regular public schools.^8 Studies providing the strongest evidence for the effectiveness of this particular type of charter school take advantage of the requirement that oversubscribed charters use a lottery to determine who among the applicants receives an offer of admission. Comparisons of state test scores, high school graduation rates, and college- going of students who win vs. lose their lottery for admission are, effectively, gold-standard randomized experiments on the impact of these charter schools on student outcomes. But these very same charter schools showing such results are often more segregated than traditional public schools serving the same general areas.^9 This creates a hot spot for public discussion and policy debate. Peter Cunningham, who served as assistant secretary of education for communication and outreach during the first term of the Obama administration, highlights the issue in a recent op-ed in U.S. News and World Report , entitled “Is School Integration Necessary?” He writes: Maybe the fight's not worth it. It's a good thing; we all think integration is good. But it's been a long fight, we've had middling success. At the same time, we have lots and lots of schools filled with kids of one race, one background, that are doing great. [Is school integration necessary?] It’s a good question.^10
and charter school networks, as long as it is a result of parental choice and not due to directly discriminatory policies or practices by the school? These are many of the questions being debated. We do not answer them, but we hope to improve the quality of the debate. Our principal findings, developed in the body of the report, are that:
of schools depend on local conditions for details of design and prospect for success. They are most likely to be feasible when the catchment area served by a school district has sufficient demographic diversity to afford the opportunity for more school diversity and where there is a political will to put policies in place to achieve those ends.
background, social capital, housing, discrimination, and so on. Even on the specific question of school integration, success is frequently dependent on efforts that are outside of the education system’s control, including housing policies, public transportation, and employment opportunities. Absent more integration in the nation’s housing markets, for example, there is only so much a school district can do to create integrated classrooms. When it comes to the role of public schools in combating inequality, there is a pragmatic middle course between fatalism and utopianism, which is that schools matter a lot but cannot be expected to carry the full load.
“How segregated are schools?” It seems a simple question, and one that ought to be easily answered using empirical yardsticks. But there are, in fact, a variety of measures in common use by scholars in this field, each aiming to illuminate a different aspect of segregation. The choice of measure is not trivial. Particular indices can often yield not just different trends, but opposing ones. Before selecting one measure over another, it is important to be clear which particular aspect of segregation is most pertinent to the investigation (and why). Used in isolation, any of the measures can provide only one piece of the picture. The choice of measure is often based on a normative sense of why segregation is important in the first place, even if that choice is not made explicit. Policymakers operating with different levers at different levels—schools vs. city, education vs. housing—will want to use the index and approach that is most appropriate to their specific circumstances. The first decision that must be made before segregation can be measured is which groups should be considered, i.e., who is segregated from whom? Again, this sounds like a simple choice. But there are many different options, each potentially generating different results and conclusions. For instance, segregation can be measured between different economic groups, such as those with low incomes or those with high incomes vs. everyone else. Segregation of students who are English language learners (ELL) and non-ELL, or those with disabilities, or immigrant status, and so on could also be examined.^23 Last but not least is segregation by race—which is the focus of this paper. In each case, decisions also have to be made about how exactly to define each group. Our focus is on racial segregation in schools, but it is worth noting the related and substantial research literature on economic segregation.^24 Here, too, there are methodological challenges, including the waning usefulness of eligibility for federal free and reduced price lunch program as a marker of poverty, especially in terms of measuring trends.^25 DEFINITIONS The most common focus of research on segregation is race. “How racially segregated are schools?” This seems like yet another straightforward question. But there are a number of complexities here, even before we turn to the choice of specific index. First, categories based on race have a social as well as biological dimension.^26 Second, there is a rapidly growing minority of Americans who define themselves as mixed race. There are, for
it is 30 percent.^29 Averaging the experiences of the black students across the two schools, weighting by their population in each school, we find that the average black student attends a school that is 34 percent white. This, then, is the black-to-white exposure index for the district as a whole. Meanwhile, the white-to-black exposure index is 21 percent—the average white student in this district attends a school that is 21 percent black.
The isolation index is the inverse of the exposure index, measuring how clustered students from one group are among people like themselves. The question being posed here is “how white is the average white student’s school?” Or, “how black is the average black student’s school?”^30 In PresidentTown, the average white student attends a school that is 79 percent white, despite the fact that the district is only 63 percent white. (Note that this is simply 100 minus 21 percent, where 21 percent was the white to black exposure index.) The average black student attends a school that is 66 percent black. The exposure index and the isolation index shed light on a particular aspect of school segregation: the extent to which students in a particular demographic subgroup are exposed to, or isolated from, students in other demographic subgroups when attending a particular school or category of schools. These two indices can only be meaningfully interpreted with reference to the racial composition of the areas in which the schools are located. In themselves, these indices are effectively “blind” to the question of how the composition of a specific school, or group of schools, is shaped by the composition of the surrounding area. (How to define “surrounding area” is a hugely important question, as we discuss below). Students are clearly more likely to attend majority- white schools in majority-white areas. The next three indices aim to address this issue, by comparing the population of the schools to the population they serve.
The dissimilarity index provides a numerical answer to the question, “Do the students in the schools in a particular place look like the population of that place?” It is a measure of how closely schools reflect their community (i.e., how similar or dissimilar they are). Unlike the exposure and isolation indices, the dissimilarity index therefore attempts to take into account the proportions of each group in the larger geographic area when judging the extent of segregation in schools. The dissimilarity index ranges between 0 and 1, where lower numbers denote less segregation.
If the schools in a particular community have roughly the same proportion of black and white students as the community (typically defined as the school district), the black-white dissimilarity index will be low (i.e., the schools are not too dissimilar to the broader population). The dissimilarity index has an intuitive interpretation: it shows the proportion of students who would have to move schools in order for the schools to perfectly match the surrounding community. In the PresidentTown school district the dissimilarity index is 0.69, or 69 percent. So we would need to move 69 percent of black or white students between Hamilton and Jefferson to ensure that both schools matched the district proportions of black and white students (about 62 percent white and 38 percent black). To provide a real example, St. Paul, Minnesota, had a white-black dissimilarity index of 0.30 in 2004. This means 30 percent of either black or white students would need to move schools in order to eliminate the discrepancy between the race-group proportions within each school and their proportions within St. Paul district as a whole.^31 Note that when a student in the numerical minority group moves, this has a bigger downward impact on the dissimilarity index than when one from the numerical majority does. It is almost always more “efficient” to move students in the minority group, if the goal is to bring down the dissimilarity index while moving the lowest possible number of students.
The divergence index was developed by Roberto as a measure of inequality and segregation.^32 We use the divergence index to measure how much the school-level proportions of each student group differ from their proportions in the larger area as a whole.^33 Like the dissimilarity index, the divergence index describes the level of segregation in the schools given the proportions of each group in the larger area. But while the classic dissimilarity index can only be calculated for two groups,^34 the divergence index can be calculated for three or more groups. The divergence index can also be decomposed for geographic sub-units.^35 This means, for example, that we could calculate the share of total segregation within a metro that is due to segregation between the metro’s school districts, as opposed to segregation within each district.^36 The maximum value of the divergence index depends on the proportions of each group in the overall population being considered, but its minimum value is always zero. The lower the index value, the more closely the school-level proportions of each group mirror the proportions in the entire population. In PresidentTown, the district divergence index is 0.26.
The Theil index is a technically complex segregation measure, which is better thought of as
of Richmond is Hanover County. The overall Richmond-Petersburg metro area (which includes both Hanover and Richmond City) is 50 percent white. But Richmond City and Hanover have separate school districts, with very different demographic characteristics. In 2010, Hanover’s schools were 84 percent white. Richmond’s schools were just 10 percent white.^42 The white-black dissimilarity index in Richmond City is 0.69, high compared to districts around the country.^43 But the index in Hanover is only 0.21, because Hanover’s relatively small black population is evenly distributed throughout the county.^44 Hanover, on this measure and at this geographical scale, looks like a well-integrated district; it is Richmond that seems to have the problem. But zooming out, it becomes clear that the real segregation is between the two districts, rather than within them. At the metro level, the schools are highly segregated, with a dissimilarity index of 0.57.^45 In The Future of School Integration , Mantil, Perkins, and Aberger compare the potential for desegregation (in this case on economic grounds) via within -district changes, compared to between -district changes, using data from Virginia, Nebraska, Colorado, Massachusetts, Missouri, and Florida.^46 They find that within-district desegregation policies could reduce the prevalence of high-poverty schools modestly. Between-district strategies would be more effective: in four of the six states, they could reduce the prevalence of high-poverty schools by more than one-third. In many cases, the population of the metro area will be an appropriate one against which to compare schools. In others, the city may cover too large a geographical area to be a useful point of comparison. For example, computing a dissimilarity index for schools in Staten Island or the Bronx based on the school-aged population for all of New York City might be of limited use. Perhaps someone might consider using the dissimilarity index as a basis for a policy to move students among schools—say, a plan to transport students between Staten Island and the Bronx to achieve more school integration. But that would be impractical in terms of transit times, much less politics. An appeal of metro-based measures is that, at this level, housing policy and zoning laws influence residential segregation and therefore school segregation, too. These are issues that might be the province of a metropolitan planning council, or the state that includes a metropolitan area, or businesses that can use decisions on where to locate or relocate to leverage government action. A metro-level dissimilarity index might help city policymakers think about larger reforms to address residential segregation (and by extension, school segregation), involving zoning and affordable housing in places like Staten Island. For principals, or school administrators, or school boards, or mayors (as well as the parents and voters that inform their actions), on the other hand, measures based on the district or neighborhood might be more useful than metro measures of segregation, because this is where
they have some control over decisionmaking and are held accountable.^47 The point here is that there is not one “correct” geographical area to use in calculating indices, simply that that there is a need to be sensitive to local political and economic geographies, as well as to the level at which potential policy interventions might be carried out. There are, then, many different ways to measure segregation, between different groups of people, and using different comparisons. None is perfect or comprehensive. Each gets at a different aspect or result of segregation. Being clear about what aspects of segregation are of interest—and why—will help policymakers select the best available measure, and use it correctly. A final cautionary note, aimed at those attempting to interpret research findings in this field. Researchers examining segregation might use different indices to look at different groups compared to different geographical areas over different periods of time. Again, this is not to say that one approach is wrong and another right; merely that we should be keenly aware that apparently technical methodological choices often reflect a specific kind of concern with segregation, and can weigh heavily on results.