Abstract
This article provides a rich longitudinal portrait of the financial and social resources available in the school districts of high- and low-income students in the United States from 1990 to 2014. Combining multiple publicly available data sources for most school districts in the United States, we document levels and gaps in school district financial resources—total per-pupil expenditures—and social resources—local rates of adult educational attainment, family structure, and adult unemployment—available to the average public school student at a variety of income levels over time. In addition to using eligibility for the National School Lunch Program as a blunt measure of student income, we estimate resource inequalities between income deciles to analyze resource gaps between affluent and poor children. We then examine the relationship between income segregation and resource gaps between the school districts of high- and low-income children. In previous work, the social context of schooling has been a theoretical but unmeasured mechanism through which income segregation may operate to create unequal opportunities for children. Our results show large and, in some cases, growing social resource gaps in the districts of high- and low-income students nationally and provide evidence that these gaps are exacerbated by income segregation. Conversely, per-pupil funding became more compensatory between high- and low-income students’ school districts over this period, especially in highly segregated states. However, there are early signs of reversal in this trend. The results provide evidence that school finance reforms have been somewhat effective in reducing the consequences of income segregation on funding inequities, while inequalities in the social context of schooling continue to grow.
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Notes
Other research did not find causal effects of school poverty on students’ outcomes (e.g., Lauen and Gaddis 2013).
Moreover, because of complex funding allocation, states with a greater share of Title I–eligible students actually receive less funding per student, on average, than smaller states with a smaller share of eligible students (Gordon 2016).
Some research has suggested that a more educated adult population in a school district may not benefit all children equally. School personnel may placate advantaged parents and provide more resources for white or higher-income children (Lewis and Diamond 2015; Lewis-McCoy 2014), and students may be placed in different academic tracks based on race and class (Oakes 1985).
Duncombe et al. (2015) described several methods for adjusting for geographic variation in costs. They noted that the ECWI does not take factors into account that may shape teachers’ job choices, such as student demographics and school conditions.
In some years, educational attainment is tabulated for adults with children enrolled in public schools. We do not use this measure because it is not available in all years, but the correlation between educational attainment for all adults and adults with children in public school is over .90 in the years that both tabulations are available, with similarly high correlations for unemployment rates between the two populations.
In more recent years, the census has also collected data on cohabitation, which may serve as a similar, alternative family arrangement to marriage in some cases. However, because information on cohabitation is not available in earlier years, we use married family households as a consistent measure of family structure across all years in our study, although we make no claims that marriage is normatively beneficial. Our measure may underestimate parental time and resources in communities where cohabitation is common.
Political processes, such as school district consolidation or fragmentation, are factors that affect income segregation between districts within states as well as levels of exposure to district resources. In our longitudinal descriptive framework, we acknowledge that changes in income segregation may be partly a function of changes in the underlying structure of school district boundaries.
Communications with officials at the U.S. Census Bureau and U.S. Department of Education suggest that these data are missing primarily because of data suppression practices. These practices primarily affect very small geographic units.
To do this, we assume that family incomes are spread evenly across each bin (income range).
We also estimate national cut points for income deciles for children in public school using IPUMS. One advantage of the IPUMS data is that income is presented as an exact dollar amount instead of in bins, and the number of children in a household is known. Estimates from the IPUMS and EDGE data are highly correlated. We use the EDGE cut-point estimates because they are derived from the same source as our social resource data.
The first decile corresponds to students in the 10th percentile and below, the 5th decile corresponds to students in the 40th to 50th percentiles, and the 10th decile corresponds to students in the 90th to 100th percentiles of the national income distribution of families with children in public school.
Income segregation among all households is highly correlated with income segregation among public school households, although income segregation levels are lower among all households compared with those among households with children in public school.
We implement the bias adjustment using the Stata program rankseg.
These national figures are population-weighted averages of the school districts in our sample.
The post-2000 figures deviate from national statistics in part because they are derived from five-year ACS averages.
When segregated school systems are also highly fragmented, this may result in higher overall educational costs because each district must replicate basic institutional functions and staffing. Although income-segregated states spend more on education, on average, it is unclear whether those extra dollars contribute directly to student outcomes.
Table A3 in the online appendix presents state-level descriptive statistics for education exposure and income segregation.
Official accounts of revenues generated through governmental channels may overlook fundraising efforts by parents or local nonprofit organizations, although to our knowledge, the extent of financial inequalities created by these sources is unknown. Inequalities created through extra-governmental means would likely erode the progressivity we observe because wealthier districts likely have greater capacity to supplement tax-based revenues as a result of both differential wealth as well as vast differences in the social resources we document.
Current data initiatives, such as the Stanford Education Data Archive (SEDA), may permit future links between the mechanisms we document and student outcomes, although the SEDA data are available for only recent school years.
References
Autor, D. H. (2014). Skills, education, and the rise of earnings inequality among “the other 99 percent.” Science, 23 , 843–851.
Autor, D. H., Katz, L. F., & Kearney, M. S. (2006). The polarization of the U.S. labor market. American Economic Review: Papers & Proceedings, 96, 189–194.
Autor, D. H., Katz, L. F., & Kearney, M. S. (2008). Trends in U.S. wage inequality: Revising the revisionists. Review of Economics and Statistics, 90, 300–323.
Bailey, M. J., & Dynarski, S. M. (2011). Inequality in postsecondary education. In G. J. Duncan & R. J. Murnane (Eds.), Whither opportunity? Rising inequality, schools, and children’s life chances (pp. 117–132). New York, NY: Russell Sage Foundation.
Baker, B. D., & Corcoran, S. P. (2012). The stealth inequities of school funding: How state and local school finance systems perpetuate inequitable student spending (Education, K-12, Report). Washington, DC: Center for American Progress.
Baker, B. D., Farrie, D., Johnson, M., Luhm, T., & Sciarra, D. G. (2017). Is school funding fair? A national report card (6th ed.). New Brunswick and Newark, NJ: Rutgers Graduate School of Education, and Education Law Center.
Baker, B. D., & Green, P. C. (2015). Conceptions of equity and adequacy in school finance. In H. F. Ladd & M. E. Goertz (Eds.), Handbook of research in education finance and policy (2nd ed., pp. 231–243). New York, NY: Routledge.
Baker, B. D., Schiarra, D. G., & Farrie, D. (2010). Is school funding fair? A national report card. New Brunswick and Newark, NJ: Rutgers Graduate School of Education, and Education Law Center.
Bischoff, K. (2008). School district fragmentation and racial residential segregation: How do boundaries matter? Urban Affairs Review, 44, 182–217.
Blau, P. M., & Duncan, O. D. (1967). The American occupational structure. New York, NY: Wiley.
Boser, U., & Baffour, P. (2017). Isolated and segregated: A new look at the income divide in our nation’s schooling system (Education, K-12, Report). Washington, DC: Center for American Progress.
Bozick, R., Alexander, K., Entwistle, D., Dauberand, S., & Kerr, K. (2010). Framing the future: Revisiting the place of educational expectations in status attainment. Social Forces, 88, 2027–2052.
Card, D., & Payne, A. (2002). School finance reform, the distribution of school spending, and the distribution of student test scores. Journal of Public Economics, 83, 49–82.
Chingos, M. M., & Blagg, K. (2017). Do poor kids get their fair share of school funding? Washington, DC: Urban Institute.
Coleman, J. S. (1988). Social capital in the creation of human capital. American Journal of Sociology, 94(Suppl.), S95–S120.
Coleman, J. S., Campbell, E., Hobson, C., McPartland, J., Mood, A., Weinfeld, F., & York, R. (1966). Equality of educational opportunity. Washington, DC: U.S. Government Printing Office.
Corcoran, S. P., & Evans, W. N. (2015). Equity, adequacy, and the evolving state role in education finance. In H. F. Ladd & M. E. Goertz (Eds.), Handbook of research in education finance and policy (2nd ed., pp. 353–375). New York, NY: Routledge.
Domina, T., Pharris-Ciurej, N., Penner, A. M., Penner, E. K., Brummet, Q., Porter, S. R., & Sanabria, T. (2018). Is free and reduced-price lunch a valid measure of educational disadvantage? Educational Researcher, 47, 539–555.
Downes, T. A., & Figlio, D. N. (1997). School finance reforms, tax limits, and student performance: Do reforms level up or dumb down? (Discussion Paper No. 1142-97). Madison: Institute for Research on Poverty, University of Wisconsin–Madison.
Duncombe, W., Nguyen-Hoang, P., & Yinger, J. (2015). Measurement of cost differentials. In H. F. Ladd & M. E. Goertz (Eds.), Handbook of research in education finance and policy (2nd ed., pp. 260–278). New York, NY: Routledge.
Evans, W. N., Schwab, R. M., & Wagner, K. L. (2019). The Great Recession and public education. Education Finance and Policy, 14, 298–326.
Gordon, N. (2016). Increasing targeting, flexibility, and transparency in Title I of the Elementary and Secondary Education Act to help disadvantaged students (Policy Brief 2016-01). Washington, DC: Hamilton Project, Brookings Institution.
Haller, A. O., & Portes, A. (1973). Status attainment processes. Sociology of Education, 46, 51–91.
Hanushek, E. A. (2003). The failure of input-based schooling policies. Economic Journal, 113, F64–F98.
Heise, M. (1995). State constitutions, school finance litigation, and the “Third Wave”: From equity to adequacy. Temple Law Review, 68, 1151–1176.
Hoxby, C. (2000). Peer effects in the classroom: Learning from gender and race variation (NBER Working Paper No. 7867). Cambridge, MA: National Bureau of Economic Research.
Jackson, C. K., Johnson, R. C., & Persico, C. (2015). The effect of school finance reforms on the distribution of spending, academic achievement, and adult outcomes (NBER Working Paper Series No. 20118). Cambridge, MA: National Bureau of Economic Research.
Jackson, C. K., Johnson, R. C., & Persico, C. (2016). The effects of school spending on educational and economic outcomes: Evidence from school finance reforms. Quarterly Journal of Economics, 131, 157–218.
Johnson, M. K., & Reynolds, J. R. (2013). Educational expectation trajectories and attainment in the transition to adulthood. Social Science Research, 42, 818–835.
Kessler, R. C., Turner, J. B., & House, J. S. (1989). Unemployment, reemployment, and emotional functioning in a community sample. American Sociological Review, 54, 648–657.
Lafortune, J., Rothstein, J., & Schanzenbach, D. W. (2018). School finance reform and the distribution of student achievement. American Economic Journal: Applied Economics, 10(2), 1–26.
Lamont, M. (2002). The dignity of working men: Morality and the boundaries of race, class, and immigration. Cambridge, MA: Harvard University Press.
Lareau, A. (2003). Unequal childhoods: Class, race, and family life. Berkeley: University of California Press.
Lauen, D. L., & Gaddis, S. M. (2013). Exposure to classroom poverty and test score achievement: Contextual effects or selection? American Journal of Sociology, 118, 973–979.
Legewie, J., & DiPrete, T. A. (2012). School context and the gender gap in educational achievement. American Sociological Review, 77, 463–485.
Lewis, A. E., & Diamond, J. B. (2015). Despite the best intentions: How racial inequality thrives in good schools. New York, NY: Oxford University Press.
Lewis-McCoy, R. L. (2014). Inequality in the promised land: Race, resources, and suburban schooling. Stanford, CA: Stanford University Press.
Logan, J. R., Stowell, J., & Oakley, D. (2002). Choosing segregation: Racial imbalance in American public schools, 1990–2000 (Report). Albany, NY: Lewis Mumford Center for Comparative Urban and Regional Research, University at Albany.
Logan, J. R., Zhang, W., & Oakley, D. (2017). Court orders, white flight, and school district segregation, 1970–2010. Social Forces, 95, 1049–1075.
McLanahan, S., & Sandefur, G. (1994). Growing up with a single parent: What hurts, what helps. Cambridge, MA: Harvard University Press.
McNeal, R. B., Jr. (2015). Parent involvement and student performance: The influence of school context. Educational Research for Policy and Practice, 14, 153–167.
Murray, S., Evans, W., & Schwab, R. (1998). Education-finance reform and the distribution of education resources. American Economic Review, 88, 789–812.
Oakes, J. (1985). Keeping track: How schools structure inequality. New Haven, CT: Yale University Press.
Orfield, G., & Lee, C. (2005). Why segregation matters: Poverty and educational inequality. Cambridge, MA: Harvard University Civil Rights Project.
Owens, A. (2016). Inequality in children’s contexts: The economic segregation of households with and without children. American Sociological Review, 81, 549–574.
Owens, A. (2018). Income segregation between school districts and inequality in students’ achievement. Sociology of Education, 91, 1–27.
Owens, A., Reardon, S. F., & Jencks, C. (2016). Income segregation between schools and school districts. American Educational Research Journal, 53, 1159–1197.
Phi Delta Kappan. (2017). The 49th annual PDK poll of the public’s attitudes toward the public schools: Academic achievement isn’t the only mission. Phi Delta Kappan, 99(1), NP1–NP32.
Piketty, T., & Saez, E. (2003). Income inequality in the United States, 1913–1998. Quarterly Journal of Economics, 118, 1–39.
Potter, H., Quick, K., & Davies, E. (2016). A new wave of school integration: Districts and charters pursuing socioeconomic diversity (Century Foundation brief). Washington, DC: Century Foundation.
Quillian, L. (2014). Does segregation create winners and losers? Residential segregation and inequality in educational attainment. Social Problems, 61, 402–426.
Reardon, S. F. (2011a). Measures of income segregation (Working paper). Stanford, CA: Stanford Center for Education Policy Analysis.
Reardon, S. F. (2011b). The widening academic achievement gap between the rich and the poor: New evidence and possible explanations. In G. J. Duncan & R. J. Murnane (Eds.), Whither opportunity? Rising inequality, schools, and children’s life chances (pp. 91–117). New York, NY: Russell Sage Foundation.
Reardon, S. F. (2016). School segregation and racial academic achievement gaps. RSF: Russell Sage Foundation Journal of the Social Sciences, 2(5), 34–57. https://doi.org/10.7758/RSF.2016.2.5.03
Reardon, S. F., & Bischoff, K. (2011). Income inequality and income segregation. American Journal of Sociology, 116, 1092–1153.
Reardon, S. F., Bischoff, K., Townsend, J. B., & Owens, A. (2018). Has income segregation really increased? Bias and bias correction in sample-based segregation estimates. Demography, 55, 2129–2160.
Reardon, S. F., Fox, L., & Townsend, J. (2015). Neighborhood income composition by household race and income, 1990–2009. Annals of the American Academy of Political and Social Science, 660, 78–97.
Reardon, S. F., & Owens, A. (2014). 60 years after Brown: Trends and consequences of school segregation. Annual Review of Sociology, 40, 199–218.
Ruggles, S., Genadek, K., Goeken, R., Grover, J., & Sobek, M. (2015). Integrated public use microdata series: Verion 6.0 [Machine-readable database]. Minneapolis: University of Minnesota.
Sacerdote, B. (2001). Peer effects with random assignment: Results for Dartmouth roommates. Quarterly Journal of Economics, 116, 681–704.
Schwartz, H. (2012). Housing policy is school policy: Economically integrative housing promotes academic success in Montgomery County, Maryland. In R. D. Kahlenberg (Ed.), Future of school integration: Socioeconomic diversity as an education reform strategy (pp. 27–65). New York, NY: Century Foundation.
Sewell, W. H., Haller, A. O., & Portes, A. (1969). The educational and early occupational attainment process. American Sociological Review, 34, 82–92.
Stroub, K. J., & Richards, M. P. (2013). From resegregation to reintegration: Trends in the racial/ethnic segregation of metropolitan public schools, 1993–2009. American Educational Research Journal, 50, 497–531.
Sui-Chu, E. H., & Willms, J. D. (1996). Effects of parental involvement on eighth-grade achievement. Sociology of Education, 69, 126–141.
Sun, Y. (1999). The contextual effects of community social capital on academic performance. Social Science Research, 28, 403–426.
Taylor, L. (n.d.). The National Center for Education Statistics comparable wage index. College Station: Bush School of Government and Public Service, Texas A&M University.
Taylor, L. L., & Fowler, W. J., Jr. (2006). A comparable wage approach to geographic cost adjustment (NCES Research and Development Report No. 2006-321). Washington, DC: U.S. Department of Education, National Center for Education Statistics.
U.S. Government Accountability Office (GAO). (2016). Better use of information could help agencies identify disparities and address racial discrimination (Report to Congregational Requesters No. GAO-16-345). Washington, DC: U.S. GAO.
Verba, S., Schlozman, K. L., & Brady, H. E. (1995). Voice and equality: Civic voluntarism in American politics. Cambridge, MA: Harvard University Press.
Weber, M., Srikanth, A., & Baker, B. (2016). School funding fairness data system codebook. New Brunswick and Newark, NJ: Rutgers Graduate School of Education and Education Law Center. Retrieved from http://www.schoolfundingfairness.org/data-download
Wilson, W. J. (1996). When work disappears: The world of the new urban poor. New York, NY: Vintage Books.
Acknowledgments
An earlier version of this article was presented at the annual meeting of the American Sociological Association (Montreal, August 2017). Research for this article was supported by the Stanford University Center for Poverty and Inequality (New Scholars Grant to Bischoff and Owens) and from National Academy of Education/Spencer Foundation Fellowships to both authors. We thank the editors and four anonymous reviewers for their constructive comments.
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Bischoff, K., Owens, A. The Segregation of Opportunity: Social and Financial Resources in the Educational Contexts of Lower- and Higher-Income Children, 1990–2014. Demography 56, 1635–1664 (2019). https://doi.org/10.1007/s13524-019-00817-y
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DOI: https://doi.org/10.1007/s13524-019-00817-y