Abstract
Using detailed administrative data for public schools, we document racial and ethnic segregation at the classroom level in North Carolina, a state that has experienced a sharp increase in Hispanic enrollment. We decompose classroom-level segregation in counties into within-school and between-school components. We find that the within-school component accounted for a sizable share of total segregation in middle schools and high schools. Recognizing its importance could temper the praise for school assignment policies that reduce racial disparities between schools but allow large disparities within them. More generally, we observe between the two components a complementary relationship, with one component tending to be large when the other one is small. Comparing the degree of segregation for the state’s two largest racial/ethnic minority groups, we find that white/Hispanic segregation was more severe than white/black segregation, particularly within schools. Finally, we examine enrollment patterns by course and show that school segregation brings with it differences by race and ethnicity in the courses that students take, with white students more likely to be enrolled in advanced classes.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
Throughout, we follow convention in using the term white to refer to non-Hispanic whites or European Americans and black to refer to non-Hispanic African Americans. We use the term Hispanic interchangeably with Latino/Latina/Latinx. We refer to these groups interchangeably as racial/ethnic, ethnoracial, or racial.
For an analysis of segregation in rural schools more generally, see Logan and Burdick-Will (2017).
Between 1990 and 2010, while the foreign-born population in the U.S. doubled, it increased sixfold in North Carolina (Portes and Rumbaut 2014, Table 9).
The Supreme Court codified this prohibition in the 2007 decision Parents Involved in Community Schools v. Seattle School District No. 1, a decision in which Chief Justice John Roberts declared sardonically, “The way to stop discrimination on the basis of race is to stop discriminating on the basis of race” (551 U.S. 701, 748 (2007). Schools in North Carolina fell under this new color-blind judicial approach earlier than 2007, owing to decisions made by the Fourth Circuit Court of Appeals. For discussion of this approach, see Boger (2000) or King and Smith (2011, p. 194).
Another reason why between-school segregation for high schools might be consistently smaller than that for elementary schools is a form of mechanical bias (sometimes called the “scale effect”) wherein measured segregation tends to be higher when enumeration units are smaller. Wong (2003) describes the scale effect as a manifestation of the more general “modified areal unit problem.”.
Middle schools showed two peaks and segregation in elementary schools was mostly flat across all racial compositions, rising only in schools 80 to 90% non-white (Clotfelter et al. 2003, p. 1494).
Although the initial legislation called for charter schools to “reasonably reflect” the racial and ethnic composition of their surrounding areas, the state softened the language in 2013 by requiring only that charter schools “shall make efforts foe the population of the school to reasonably reflect” the surrounding areas (Ladd et al. 2017, p. 538). Recent research shows that charter schools have contributed to racial segregation in the state (Clotfelter et al. 2020; Ladd and Turaeva 2020).
We exclude them because of their very small shares relative to the three main groups of interest for this study: blacks, whites and Hispanics. As we explain in the text, measures of segregation are suspect when the proportion of one group is very small; indices for these other groups would be especially susceptible to such problems. The only use we make of data on students in other racial and ethnic categories is to include them in total enrollment, which we use in calculating percentage black and Hispanic and in our calculations to identify the specific courses in each school, where we employ data for students in all racial and ethnic categories, as explained in the following subsection.
Up to 2013, the state’s detailed census of classrooms was recorded in School Activity Reports, which reported the number of students by grade and race/ethnicity in every section of every course, by school, but not the identities of those students. For each school, we employed an algorithm that selected the course or courses whose total enrollment across all sections in the school most nearly matched that school’s enrollment for the grade. After 2013, classroom census information was reported only in a dataset called course membership, which provides information on every course taken by every student. With this more detailed data, we could form our classes by assigning every student to exactly one section of a course. From the possible courses (7th grade English courses allowed by the state, for example), we looked for the course most commonly taken by students (in the 7th grade) in that school. For all the students who took this course, we defined our classes in the school based on enrollments in that course. For any students who did not enroll in that most commonly taken course, if there were any, we selected the next most commonly taken course and defined classes based on that course, too. For any students who took neither of those courses, we repeated the process until all students had been assigned to one section of a course in the relevant grade and subject.
In 2017, only 11 counties contained more than one school district. Historically, there were many more school districts, but consolidation over time has reduced the number for this state of over 10 million people to 115 districts. The decision of a county to continue to have more than one school district is in part a decision about school segregation. For that reason, it makes sense to use the county, not its individual school districts, as the unit for measuring segregation.
This inequality is a direct manifestation of the scale effect, noted above, which causes calculated segregation indices to be large when enumeration units are small and small when enumeration units are large.
Wong (2003) discusses a similar decomposition of the dissimilarity index applied in a geographical context to segregation in local areas that are contained in larger regional areas.
Although, by construction, the Coleman index is designed to be independent of the racial mix of the population being studied, our calculations, as well as those of Becker (1978) suggest that the Coleman index, over at least some ranges, in fact tends to be correlated with the percent minority. Becker (1978, p. 14) reports that calculated values of the Coleman index in applications to employment and higher education both showed a positive correlation with percent black. Likewise, in unpublished simulations we found strong associations between the Coleman index and county racial compositions, a correlation we did not observe with the dissimilarity index.
Previous research has established that the dissimilarity index is subject to upward bias when the proportion of racial minority individuals is very low or when the units of grouping are small, and this bias applies as well to other widely used measures of imbalance. As explained in studies such as Allen et al. (2015) and Mazza (2017), the problem arises because small enumeration units will simply by chance tend to differ in composition, a tendency that will be more pronounced with a very small racial minority group. Among the methods, Monte Carlo simulations are proposed to correct the bias, which allow actual distributions to be compared to those generated randomly. According to Mazza (2017, p. 31), “Most of the methods proposed use computation-intensive techniques that have the drawback of introducing complexity and substantial computational burdens.” As an alternative, many studies have resorted to various rule-of-thumb remedies, such as excluding cities or districts with tiny proportions of the racial minority group of interest, an approach we adopt here. Another form of bias in measuring segregation in residential patterns, due to reliance on samples of the population discussed in Reardon et al. (2018), for example, is not relevant in the present case, since all student counts cover 100% of the relevant population.
Weighted averages of segregation indices for the state and the two urban and rural categories using this sample of 62 counties yielded identical or very similar values to those using slightly larger samples of 86 counties with at least 4% black enrollments in 1998 for white/black calculations and 67 counties with at least 4% Hispanic enrollments in 2006 for white/Hispanic calculations.
Urban counties are those where more than half of the population in 2000 lived in urban areas, according to the 2000 Census of Population and Housing.
Belk v. Charlotte-Mecklenburg Bd of Educ., 211 F. 3d 853 (4th Cir 2000). See also Clotfelter et al. (2008a, p. 50).
The proportion was 24%; authors’ calculations using unpublished data from the North Carolina Education Research Center.
For a description of North Carolina’s courses, see North Carolina Department of Public Instruction, “Course Code Guidance,” https://files.nc.gov/dpi/documents/course_information/Course_Code_Guidance.pdf 4–7-20.
References
Alexander, K. L., & McDill, E. L. (1976). Selection and allocation within schools: Some causes and consequences of curriculum placement. American Sociological Review, 41, 963–980.
Allen, R., et al. (2015). More reliable inference for the dissimilarity index of segregation. Econometrics Journal, 18(February), 40–66.
Allport, G. (1954). The nature of prejudice. New York: Addison-Wesley.
Becker, H. J. (1978, November). The measurement of segregation: The dissimilarity index and Coleman’s segregation index compared. Report #265. Johns Hopkins Center for Social Organization of Schools.
Card, D., & Giuliano, L. (2014, September). Does gifted education work? For which students? NBER Working Paper 20453.
Clotfelter, C. T., Hemelt, S. W., Ladd, H. F., & Turaeva, M. (2020, June). Undoing desegregation: School segregation in the era of school choice and color-blind jurisprudence. Unpublished Paper.
Clotfelter, C. T., Ladd, H. F., & Vigdor, J. L. (2003). Segregation and resegregation in North Carolina’s public school classrooms. North Carolina Law Review, 81, 1463–1511.
Clotfelter, C. T., Ladd, H. F., & Vigdor, J. L. (2008a). School segregation under color-blind jurisprudence: The case of North Carolina. Virginia Journal of Social Policy and the Law, 16(Fall), 46–86.
Clotfelter, C. T., Ladd, H. F., & Vigdor, J. L. (2008b). New destinations, New trajectories? The educational attainment and persistence of hispanic youth in North Carolina. Child Development, 83(September/October), 1608–1622.
Clotfelter, C. T., Ladd, H. F., & Vigdor, J. L. (2005). Who teaches whom? Race and the distribution of novice teachers. Economics of Education Review, 24, 377–392.
Conger, D. (2005). Within-school segregation in an urban school district. Educational Evaluation and Policy Analysis, 27(3), 225–244.
Domina, T., et al. (2020, February). The kids on the bus: The academic consequences of diversity-driven school reassignments. Unpublished Paper.
Dondero, M., & Muller, C. (2012). School stratification in new and established Latino destinations. Social Forces, 91(December), 477–502.
Gamoran, A. (1992). Access to excellence: Assignment to honors English classes in the transition from middle to high school. Educational Evaluation and Policy Analysis, 14, 185–204.
Gamoran, A., & Mare, R. (1992). Secondary school tracking and educational inequality: compensation, reinforcement, or neutrality. American Journal of Sociology, 94(March), 1146–1183.
Gándara, P. (2019). The crisis in the education of Latino students. National Education Association, Issues and Action. http://www.nea.org/home/17404.htm. 7/30/2019.
Gándara, P., Rumberger, R., Maxwell-Jolly, J., & Callahan, R. (2003). English learners in California schools: Unequal resources, unequal outcomes. Educational Policy Analysis Archives, 11(October 7), 1–52.
Grissom, J. A., & Redding, C. (2016). Discretion and disproportionality: Explaining the underrepresentation of high-achieving students of color in gifted programs. AERA Open, 2(January–March), 1–25.
Kahlenberg, R. D. (Ed.). (2012). The future of school integration: Socioeconomic diversity as an educational reform strategy. New York: Century Foundation.
Kalogrides, D., & Loeb, S. (2013). Different teachers, different peers: The magnitude of student sorting within schools. Educational Researcher, 42, 304–316.
Ladd, H. F., Clotfelter, C. T., & Holbein, J. (2017). The growing segmentation of the charter school sector in North Carolina. Education Finance and Policy, 12(4), 536–563.
Ladd, H. F., & Turaeva, M. (2020, September). Parental preferences for charter schools in North Carolina: Implications for racial segregation and isolation. Working paper of the National Center for the Study of Privatization in Education. Teachers College, Columbia University.
Logan, J. R., & Burdick-Will, J. (2017). School segregation and disparities in urban, suburban, and rural areas. Annals of the American Academy, 674(November), 199–216.
Lucas, S. R., & Berends, M. (2007). Race and track location in U.S. public schools. Research in Social Stratification and Mobility, 25, 169–187.
Mazza, A. (2017). Dealing with the bias of the dissimilarity index of segregation. RevistaIntaliana di EconomiaDemografia e Statistica, 71, 25–34.
Mickelson, R. A. (2001). Subverting Swann: First- and second-generation segregation in Charlotte North Carolina. American Educational Research Journal, 38(2), 215–252.
Mickelson, R. A. (2015). The cumulative disadvantages of first- and second-generation segregation for middle school achievement. American Educational Research Journal, 52(4), 657–692.
Morgan, P.R., & McPartland, J. M. (1981, August). The extent of classroom segregation within desegregated schools. Unpublished Paper. National Institute of Education.
Oakes, J. (1985). Keeping track: How schools structure inequality. New Haven, CT: Yale University Press.
Parsons, T. (1959). The school class as a social system: Some of its functions in American society. Harvard Education Review, 29, 297–313.
Portes, A., & Rumbaut, R. G. (2014). Immigrant America: A portrait. Oakland, CA: University of California Press.
Reardon, S. F., Bishoff, K., Owens, A., & Townsend, J. B. (2018). Has income segregation really increased? Bias and bias correction in sample-based segregation estimates. Demography, 55, 2129–2160.
Rist, R. C. (1970). Student social class and teacher expectations: The self-fulfilling prophecy in Ghetto education. Harvard Educational Review, 40(3), 411–450.
Rosenbaum, J. E. (1976). Making inequality: The hidden curriculum of high school tracking. New York: Wiley.
Tippett, R. (2019, September). North Carolina’s Hispanic community: 2019 Snapshot. Carolina Demography. https://www.ncdemography.org/2019/09/26/north-carolinas-hispanic-community-2019-snapshot/. 10/6/2020.
Tyson, K. (2011). Integration interrupted: Tracking, black students, and acting white after Brown. New York: Oxford University Press.
Useem, E. (1992). Middle schools and math groups: Parents’ involvement in children’s placement”. Sociology of Education, 65, 263–279.
Wong, D. W. S. (2003). Spatial decomposition of segregation indices: A framework toward measuring segregation at multiple levels. Geographical Analysis, 35(July), 179–1294.
Acknowledgements
We are grateful to Dan Goldhaber, John Logan, Sean Reardon, Tim Sass, and participants at the Sanford School Social Policy Workshop for helpful comments and discussions and to Jessica Wilkinson for valuable research assistance. We gratefully acknowledge the financial support of the National Center for Analysis of Longitudinal Data in Education Research (CALDER).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendices
Appendix 1
Appendix 2
Appendix 3
Rights and permissions
About this article
Cite this article
Clotfelter, C.T., Ladd, H.F., Clifton, C.R. et al. School Segregation at the Classroom Level in a Southern ‘New Destination’ State. Race Soc Probl 13, 131–160 (2021). https://doi.org/10.1007/s12552-020-09309-w
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12552-020-09309-w