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A fascinating, age-old U.S. public policy question surrounds the relationship between educational outcomes and segregation (e.g., income, racial). Over the years, growing economic literature continues to add nuanced perspectives to the issue of the interaction between school finances and the makeup of local communities. Social science researchers emphasize the importance of studying residential segregation, whether by income or race-ethnic groups, because of potential neighborhood effects on the long run educational outlook for young students. Since public education in the U.S. is largely financed by local property taxes, there is a large disparity between funding for schools in different communities.
Thomas Nechyba, “School finance, spatial income segregation, and the nature of communities”:
Although many education policy workers focus almost solely on the impacts of disparate per pupil expenditures across schools, a large body of economic research shows strong evidence that there are broader factors affecting educational inequities, and vice versa. For instance, since public school systems are based in local districts, residential segregation by income is more likely to occur—and over time this greater residential segregation feeds even larger inequalities, thus leading to a relentless cycle. Differences in education quality are capitalized into housing prices (i.e., property values often further decline in poor areas with inadequate schools).
Nechyba (2003) questions why the contemporary educational inequalities discussion is so restricted to per pupil spending gaps; rather, he calls for a more general examination of different school finance institutions and their various equilibrium effects. Thus, the paper sets up a structural model representing a decentralized economy in which households select their place of residence, where to send kids for education, and the degree of support to provide public schools; the model includes the most causal potential factors leading to income segregation. Using observations from New Jersey districts, Nechyba (2003) adjusts the underlying structural parameters until his model simulates realistic features from the data. Then, holding these parameters constant, policy simulations can be conducted. The framework accounts for a couple main sources of residential segregation by income: 1) different neighborhoods are historically endowed with disparate housing quality and neighborhood characteristics; 2) places of residence affect a child’s school quality since public education systems require households to live within exogenously outlined boundaries. However, the inclusion of private schools into this model complicates matters because households that send their children to private educations care less about public school quality near their homes; alternatively, they would be even motivated to live in a subpar public school district with lower housing costs. Since private school households are often wealthier, this counterintuitive phenomenon pushes the local economy toward income desegregation.
Subsequently, Nechyba (2003) use the structural model to find that state financing of school districts indeed dampens residential income segregation in an area.  This effect is expected because school systems that are purely locally financed give wealthier households motivation to segregate by income and thus shape better schools. However, two less intuitive findings are that the existence of a private school market leads to considerable residential income desegregation, and that in the presence of private schools, the existence of public schools actually tends to lower residential segregation—even though, by themselves, public school systems are associated with higher spatial segregation. However, a notable caveat to this study is that the model equilibrium overestimates the movement of private school households into poorer neighborhoods simply because the structural model does not account for non-school characteristics of such communities.
Raquel Fernandez and Richard Rogerson, “Keeping people out: income distribution, zoning, and the quality of public education”:
Meanwhile, Fernandez and Rogerson (1993) investigate the effects of community zoning regulations on per pupil spending, particularly with respect to property taxes and the formation of communities with average income differences. The analysis relies on simulations using a two-community model, where each community determines tax rates by majority vote, and households can choose their place of residence. Without zoning, the equilibrium of this model results in a poor neighborhood with a low tax rate (i.e., low per pupil expenditure) and a wealthier community with a higher tax rate. The study found that the existence of zoning regulations, meaning that households have to purchase a minimum level of housing in order to live in a predetermined area, is associated with the wealthy community decreasing in size and becoming richer, while the poorer neighborhood grows larger. The increased exclusivity of the rich neighborhood causes the lowest income households of that neighborhood to enter a poorer one, thus raising average income in both areas. As a result, the poorer community sees greater per pupil spending, but the change in education quality across the two areas is ambiguous.
Sarah Reber, “School desegregation and educational attainment for blacks”:
Although the first two studies mentioned did not delve into racial segregation, it is important to gauge the relationship between segregation and educational attainment for certain race-ethnic groups. Reber (2007) looks into the effects of the desegregation process after the Supreme Court’s 1954 Brown v. Board of Education decision, specifically focusing on schools in Louisiana. Previous studies showed that the desegregation policy had two effects: 1) increasing black students’ exposure to white peers in school and 2) raising the level of federal and state funding such that the average spending in predominantly black schools increased. Given that desegregation essentially eliminated disparities in student-teacher ratios for black and white students within previously segregated districts, Reber (2007) sought to determine whether greater educational attainment by blacks following desegregation resulted more from greater exposure to white peers or increased funding.
The simplest specification in this paper is a univariate regression that looks for an association between a county’s initial share of black enrollment and change in educational attainment from before and after desegregation; in this model, the dependent variable of interest is mean attainment for 1970-1975, minus the average attainment for 1960-1965. Later, the paper considers metrics such as the 12th grade continuation rate and adds controls for other characteristics such as change in county’s employment. These regressions on high school grade continuation and graduation rates indicate that greater educational attainment increased more for black students in districts with higher rates of black enrollment following desegregation—thus implying that, following desegregation, increased funding played a larger role than higher exposure to white students in raising attainment.
Growing literature contribute to U.S. policies designed to match educational opportunities of students coming from vastly different racial and socio-economic backgrounds. Whether through investigating the means by which certain communities are able to make use of taxes and zoning as instruments to keep specific segments of the population out of a school district, or by evaluating the effect of private schooling on income segregation, valuable insights can be drawn from these types of analyses. A thorough look at the institutional set-up of education can reveal the role that segregation and various school finance mechanisms play in long-run inequality.
Raquel Fernandez and Richard Rogerson, 1997, “Keeping people out: income distribution, zoning, and the quality of public education,” International Economic Review 38(1): 23-42.
Sarah Reber, 2007, “School desegregation and educational attainment for blacks,” Cambridge, MA: NBER working paper 13193.
Thomas Nechyba, 2003. “School Finance, Spatial Income Segregation and the Nature of Communities,” Journal of Urban Economics 54(1), 61-88, July.
 Thomas Nechyba, School finance, spatial income segregation, and the nature of communities, 66: It is notable that although this analysis focuses on income segregation, it can apply to problems involving racial segregation as well, “only to the extent that such segregation is driven by income differences.”
 Nechyba, 65
 Nechyba, 74: “While it might be expected that state financing will lead to less segregation than local financing, the relatively small magnitude of this effect compared to the huge effect of private schools is surprising, as is the different effect of public schools in a world with and without private school markets.”
 Raquel Fernandez and Richard Rogerson, “Keeping people out: Income distribution, zoning, and the quality of public education,” 1
 Fernandez and Rogerson, 32
 Sarah J. Reber, “School desegregation and educational attainment for blacks,” 3
 Reber, 6: To prevent white flight, schools with larger proportions of black students received more funding to “level up to the levels previously experienced only in the white schools”
Technical Presentation on Baum-Snow and Lutz’s “School Desegregation, School Choice, and Changes in Residential Location Patterns by Race”
by Christopher Bradford TP_BradfordChristopher
Baum-Snow and Lutz consider the responses to school choice and residential location following the desegregation of the American public school system. Data include 92 metropolitan statistical areas (MSAs) that underwent large-scale court-mandated desegregation between 1960 and 1990. The study uses these data to determine the extent to which desegregation was a causative agent for changes in public/private school enrollment and for residency in central districts/suburban areas for both whites and blacks.
The authors note that, while the 1954 case Brown v. Board of Ed of Topeka ruled school segregation unconstitutional, most large school districts did not initiate robust school desegregation until specific court orders forced them to do so. As such, the authors devise a model that accounts for this variation in school desegregation over time, Equation 1:
(1) ln yrjt = αrj + βrt + cDrjt + εrjt
Here r represents a region (e.g. South), j represents a specific MSA, t represents time, βrt accounts for year-specific effects, Drjt represents the central district’s degree of desegregation at time t, and yrjt represents the variable of interest. The three main variables of interest yrjt for which the authors solve are (1) public school enrollment by race, (2) private school enrollment by race, and (3) population in central districts by race.
As a methodological note, the authors comment that identification of the constant c, the parameter of interest that modifies the degree of desegregation at a given time Drjt, necessitates that timing of desegregation is uncorrelated with any time-dependent, causative omitted variables. The authors feel that desegregation implementation occurred with “pseudo-random timing” due to two observations (8). First, the NAACP tended to file cases in the order in which they were most likely to succeed, not according to where the perceived need for desegregation was greatest. Second, the time between court uptake of a case and issuance of the court’s verdict and implementation was highly variable by district. In an attempt to correct for omitted variables in the overall model, the authors allow the term βrt to differ for South and non-South Census regions, as unmeasured (or immeasurable) variables such as differential degrees of discrimination could vary by region.
In order to gauge racial integration following court-mandated desegregation, the authors utilize a “dissimilarity index,” which ranged from 0 (perfect integration) to 1 (perfect segregation). This dissimilarity index is given by:
Here bit and wit represent the number of black and white students at a given school i at time t, and Bt and Wt represent the total number of black and white students in that district at time t. Using data from the 1970, 1980, and 1990 Censuses, the authors find that the dissimilarity index decreased by a national average of 0.15 due to desegregation, representing a reduction of nearly one standard deviation.
In addition to the dissimilarity index, the authors construct an “exposure index,” which gives the percent of black students in the average white student’s school. This exposure index is given by:
Here tit represents the total enrollment of both races in a given school i. Substituting the exposure index into Equation 1 yields the result that desegregation increased interracial exposure in schools by 0.09, or roughly half of one standard deviation.
To account for spatial variation in desegregation, the authors amend Equation 1. Each North and South region r is divided into four location segments s. The modified Equation 2 takes the form:
(2) ln E (yirjts) = arjs + brts + ϒrs Drjt
Here i represents a Census tract within a given MSA j. While the parameter of interest in Equation 1 is c, it is ϒrs in Equation 2. This term accounts for spatial variation by region and segment.
Taking data from 77 MSAs that contained a suburban region, the authors find that over 90% of these MSAs were less racially integrated than the MSA central district in 1970. The average Southern suburb had an exposure index value 0.15 less than in the central district of the MSA. In the North this discrepancy was even larger, at 0.26. The implication of these large differences in integration is that departing central district residences for the suburbs allowed whites to escape exposure to black students.
Indeed, the study finds that white enrollment in public schools in central districts declined by a national average of 12% from 1960 to 1990 due to desegregation. The figure in the South is slightly higher than the national average and slightly lower in non-Southern regions. The models predict that in non-Southern regions, desegregation led to a 16% increase in white private school enrollment in the 30-year period. The authors fail to find statistically significant evidence on the effect of desegregation on white private school enrollment in the South. Data supported that white enrollment declines in central districts public schools were offset by increased enrollment in suburban public schools, however. The evidence for non-Southern regions suggests a causal relationship between desegregation and private school enrollment for whites.
Moreover, the models predict that desegregation caused an average of 6% nationally of the central district’s white population to relocate outside the central district. Again, these results are more pronounced in the South, where 12% on average of whites residing in the central district relocated due to desegregation. Significantly, these predictions attribute a proportion of residential white flight from integrated urban school districts to suburban districts directly to desegregation.
For blacks, the models predict that desegregation resulted in a 14% increase in public school enrollment in the central district on average. These results were captured in a period of at least 5 years after court-mandated desegregation policies were implemented. Desegregation caused the percentage of blacks enrolled in private schools in central districts to decrease by 20-28% nationwide within five years of implemented integration. This figure was much larger in the South than in other regions. The data suggest that large numbers of black students left private schools to enroll in newly desegregated public schools, particularly in urban central districts.
The population of blacks living in central districts nationwide increased by about 8% due to desegregation. This result was only obtained in the non-South; the effects of desegregation on central district population in the South were indeterminate. The authors note that the effects of desegregation on black schooling and residency patterns display much more cross-regional variation than for whites. Accordingly, unobserved interregional variables may have played a larger role for influencing black schooling and residency patterns post-desegregation.
Using their models, the authors confirm two predictions of Tiebout sorting. The first predicts that public enrollment and relocation will occur more in the periphery of the central city, where wealthy individuals tend to live in. A Tiebout assumption is that the marginal utility of local public goods (such as public schooling) increases with income. Accordingly, high-income residents are predicted to be the most susceptible to decreases in marginal utility due to school quality deterioration, which is an oft-perceived consequence of school desegregation. Indeed, the study finds that both white public school enrollment and total white population decreased the most in the outer fourth of central districts.
The second prediction is that private school enrollment changes will be more dramatic near city centers. Tiebout sorting predicts that residents who use private schooling will tend to live closer to the city center than those who use public schools, as savings on commuting costs can partially defray the cost of private school tuition. This prediction is also confirmed, as the authors observe the increase in private school enrollment for blacks and the corresponding decrease for whites in regions closer to city centers than they observe the enrollment responses for public schools.
In their conclusion, the authors note that the magnitudes of population shifts due to desegregation are insufficiently large to account for the majority of total central district population loss in the past half century. The authors calculate that, had court-mandated desegregation not occurred, the decrease in white central district population would have declined by 10% from 1960 to 1990, whereas the actual decline was 13%. For blacks in the hypothetical no-desegregation scenario, the authors predict a 44% increase in central district population as compared to the 54% actual figure. Accordingly, the authors conclude that other factors must have been responsible for the brunt of city center population changes.
What the study does show is that desegregation contributed to decreased white enrollment in central district public schools and an increase in enrollment in these schools for blacks. It also shows that desegregation was a causative agent for changing both the racial makeup of urban central districts and private school enrollment for whites and blacks.
School integration remains to this day a large policy issue in America. It is also a significant local concern, with the recent struggles of the Wake County district a prominent example. For my term paper, I may explore topics that branch out from this paper, such as how racial integration in public schools correlates with educational achievement. Or I may take a different angle, and investigate residency patterns by race, perhaps by considering whether residential segregation today correlates with the time at which court-mandated school integration began.
Baum-Snow, Nathaniel and Byron F. Lutz. “School Desegregation, School Choice and Changes in Residential Location Patterns by Race.” American Economic Review, 101(7): 3019-46. 2011.