Most states use an education funding formula to allocate state and local dollars to school districts. Most funding formulas attempt to account for student poverty, among other factors, in distributing funds. But there are several ways to count low-income students and even more ways to tie dollars to these student counts. Increases in school district funding could have a substantial effect on student outcomes—including high school graduation rates, postsecondary enrollment, and earnings—so it is important to understand if formula provisions actually direct more dollars to districts with more low-income students.
I describe how four states—Massachusetts, Nebraska, Texas, and Virginia—allocate funding for low-income students in their formula and to what extent the results of this formula are associated with a common measure of student poverty.
I find that funding levels are more strongly associated with the share of low-income students in urban districts, relative to rural districts. Although each state has a provision for increasing funds for low-income students, other formula factors introduce substantial variation into the relationship between low-income students and district funding.
How four states approach funding for low-income students
There are different ways of measuring low-income students for education funding. States can use data from the most recent collection of free- and reduced-price lunch applications, use the number of students who were directly certified as receiving benefits through the Supplemental Nutrition Assistance Program (SNAP) or other means-tested benefits program, or collect their own information on income from surveys. All four states examined here use a measure of students who are directly certified as participating in a means-tested program, sometimes in combination with a multiplier or other supplemental information.
Massachusetts has adopted an “economically disadvantaged” measure based on direct certification of participation in SNAP, Temporary Assistance for Needy Families, foster care, or Medicaid. To allocate funding based on this measure, the state splits school districts into deciles based on the share of economically disadvantaged students. Several areas of the Massachusetts formula provide more funding for districts in the higher deciles of economic disadvantage.
Nebraska relies on a measurement of poverty that includes either those who were certified through means-tested programs (multiplied by 1.1) or the proportion of students who received free meals after filling out a paper application. The amount allocated for low-income students is increased for every additional 5 percentage points of the share of low-income students in the district (e.g., a district with 30 percent low-income students receives an allowance five times what a district with 15 percent low-income students receives). Each district submits a “poverty plan” for spending on low-income students; districts receive the lower of their calculated poverty allowance or the spending in their poverty plan.
Texas uses a compensatory education weight to provide funds for “at-risk” students. The number of students deemed at risk is determined by the share of students enrolled for free- and reduced-price lunch or the number of students directly certified in community-eligibility schools, multiplied by 1.6. The state adds an additional 20 percent of the school district’s allotment per student for each at-risk student.
Virginia provides funding for low-income students based on the share of students directly certified for free lunch in the three prior years. Through its “at-risk” program, Virginia provides additional funding for each low-income student, with the add-on amount ranging from 1 to 13 percent of Basic Aid funding per student, with higher percentages for higher-need districts. Other state programs that allocate education dollars define and target low-income students in a similar way.
District funding and share of low-income students are more strongly correlated in urban areas
Policymakers must account for several factors when providing funding to districts. Given the importance of funding for low-income students, I estimate the correlation between a common measure of district poverty (the number of students identified or “directly certified” for free lunch, as reported by the Food Research and Action Center) and funding provided by each state formula.
A key driver of per student funding differences is how densely populated the district is. Sparse districts, which tend to be rural, typically have higher transportations costs and higher staffing and facilities costs because they serve fewer students. To account for this difference, I examine the correlation of student poverty with district funding separately for urban districts and for rural districts.
In all four states, the share of directly certified low-income students is associated with an increase in funding in urban districts. But among rural districts, the relationship is less clear. In Virginia, the correlation of poverty and funding is stronger for rural districts than for urban ones. But Nebraska has a weaker positive relationship for rural districts compared with urban districts, and Massachusetts and Texas have a weak negative relationship.
These scatterplots show that the relationship between funding and the share of low-income students is loose, even in urban districts. Several other factors drive dollars to school districts, including the needs of other students (e.g., special education and English language learners), the amount contributed by the local district, and the desire of many states to prevent districts from losing funding relative to the previous year (“hold harmless” provisions).
A perfect relationship between funding with the share of low-income students may not be possible or even desirable, given other students’ needs. And it is unclear how much more funding should go to low-income students relative to their non-low-income peers. Though each state has chosen a different path toward allocating additional dollars for low-income students, per student funding, as allocated by formula, seems to be more strongly associated with the share of low-income students in urban districts across the four states and less strongly in rural districts.