Panel Paper: Racial and Socioeconomic Disparities in School District Funding

Saturday, November 4, 2017
Columbian (Hyatt Regency Chicago)

*Names in bold indicate Presenter

Ericka S. Weathers and Victoria E. Sosina, Stanford University

Court-ordered desegregation efforts in the 1960s through 1980s improved educational attainment, socioeconomic status, and health outcomes for black students (Johnson, 2011). Evidence suggests that a key mechanism in producing these positive outcomes was the expanded access for black students to school resources (Johnson, 2011; Reber 2010). This prior work suggests that desegregation worked in part through redistributing resources that contributed to student outcomes. Yet current research tells us that past progress on desegregation may be fading. For example, black-white isolation across schools has increased in the past two decades (Reardon & Owens, 2014).

This raises the question of how recent trends in segregation are related to the distribution of school resources. The current study will provide a descriptive analysis of the relationship between racial and socioeconomic school district segregation and revenue and expenditures.

This study uses district level data from the National Center for Education Statistics, Common Core of Data in fiscal years 1995 through 2014. The analytical sample includes an average of 13,626 districts per year for a total of 272,527 district-year observations. Since we are measuring average disparities between districts within a state, our measures are properties of a given state and year combination, resulting in 980 observations.

Our dependent variables are black-white and Latino-white disparities in revenue and expenditures. These disparities are calculated as the natural log of the ratio of average per-pupil revenue or expenditures in the average black or Latino student’s district relative to spending in the average white student’s district. The independent variables are measures of segregation. The first measure is the racial disparity in the proportion of free and reduced price lunch students in a district, which we refer to as the racial poverty disparity. A second measure captures the average exposure of two different racial groups to each other, which we refer to as the racial exposure disparity. An example model is as follows:

BWRatiossy = β0 + β1RPDsy + β2REDsy + β3Schoolssy + β4SpecEdsy + Γs + Δy + εsy

Preliminary results suggest that racial disparities in poverty and enrollment are associated with disparities in revenue and expenditures. Federal revenue is compensatory when poverty increases in Latino districts, but the evidence does not suggest the same in black districts. As anticipated, black and Latino districts receive less local funds as racialized poverty increases. Yet, even after controlling for racialized disparities in poverty and other possible confounders, differences in racial segregation are associated with differences in district finance. Districts with larger shares of black students have more federal, but less total, state, and local revenue. Districts with larger shares of Latino students have more total and state, but less local revenue.

These preliminary results suggest that purportedly race neutral funding formulas distribute funding in racialized ways. If these preliminary results hold up to future data quality inspections and robustness checks, the findings could be problematic, given the relationship between funding and achievement. Future analyses will investigate whether certain years are driving the effects, consider alternative poverty measures, and will undergo additional quality assurance procedures.