Indirect Impacts of Federal Changes on School Funding

Federal Data Use in State Funding Formulas

This year has seen a number of large shifts in federal policy, including staffing reductions at the US Department of Education (USDOE), changes to Medicaid/SNAP eligibility, and even withholding federal funds after most districts budgets had passed – only to then release those funds days before school starts. Policymakers are understandably focused on the most immediate and direct impacts of these changes and the disruptions they have caused.

However, as a firm that has spent four decades working in school finance- including developing and refining state funding formulas- we are also concerned about the indirect impacts of changes at the federal level on school funding, as most states use federal data in their school funding formulas. These formulas determine how state education funds are distributed to districts and schools statewide.

Most states use eligibility for federal free or reduced-price meals (FRL) to identify students for state at-risk/compensatory education funding streams, while some also use Medicaid and SNAP eligibility to directly certify students. Other examples of federal data use in state funding formulas include using U.S. Census Bureau or Bureau of Labor Statistics data for regional cost adjustments, and the Consumer Price Index data (CPI) for inflation adjustments. Recently, our home state, Colorado, began using the National Center of Education Statistics’ locale codes to provide additional funding for more remote settings.

The recent changes to Medicaid and SNAP eligibility have a more near-term impact on states that rely on that data to certify students for additional state funding, while the recent staffing and funding reductions at federal agencies make it unclear if other federal data sets will continue to be available and as reliable.

How States are Using Federal Data to Determine State Funding

Below are examples of how states use federal data in their school finance formulas which highlight different levels of exposure if federal data becomes unavailable or unreliable.

Adjustments for At-Risk Students/Compensatory Education

For years, states have grappled with how best to identify at-risk students amid shifting policy landscapes. FRL eligibility, once a dependable proxy for household income, has become less reliable in recent years. With the adoption of programs like the Community Eligibility Provision (CEP) and Universal School Meals, districts have had less incentive to prioritize collecting FRL applications, and families were less motivated to submit them, since meals were provided at no cost. Many states still use FRL data, but others have implemented alternative methods to verify students’ economic status.

Maryland’s funding formula provides additional funding for compensatory education students primarily based on the number of students eligible for FRL in the prior fiscal year. For districts that participate in CEP, funding is calculated using the greater of two methods: either the sum of directly certified students plus those identified through an alternative income form, or a formula that multiplies pre-CEP FRL percentages by current enrollment. While this approach addresses concerns about the reliability of FRL data, changes in Medicaid and SNAP eligibility could lead to fewer compensatory education students being identified through direct certification, which in turn results in districts and schools receiving fewer dollars to serve them.

Texas determines State Compensatory Education (SCE) funding using tiered weights based on FRL and census block data. Previously, Texas utilized FRL eligibility alone as a proxy for economically disadvantaged students. Beginning in 2020, the state shifted to utilizing federal census block data in addition to FRL, requiring school districts to enter the census block for each economically disadvantaged student (as identified through FRL status or direct certification) into the state data system. Texas’s 18,638 census block groups are sorted into five tiers that generate different SCE funding amounts. Changes in the availability of census block group data could impact student counts for SCE funding purposes.

Nevada recently adopted a new approach to identifying at-risk students by implementing a data-driven model rather than relying on traditional income-based criteria. The state uses a machine learning algorithm to identify the students that are predicted to be least likely to graduate from high school with their cohort. The algorithm uses over 70 different factors including academics, attendance, behavior, home/enrollment stability, and more to assign each student a GRAD score; students in the lowest quintile of scores are designated as at-risk for additional funding and support. FRL eligibility and Title I designation are just two of the factors used in the algorithm, suggesting limited reliance on federal data. Still, the algorithm’s complexity makes it hard to predict how changes in federal data might affect the identification of at-risk students.

Regional Cost Adjustments

Another source of federal data commonly relied on in state funding formulas is data that addresses differences in district costs due to their geographic region or rurality. Of the states that have regional cost adjustments, most use comparative wage indexes (CWI), which compare regional differences in wages as a proxy for the relative cost differences between districts.

While some states collect their own wage data, like Massachusetts and New York, others use federal data sources to develop their CWIs. New Jersey, for example, uses a 5-year average of survey data from the U.S. Census Bureau’s American Community Survey, while Florida has used the U.S. Bureau of Labor Statistics’ Occupational Employment and Wage Statistics (OEWS) survey.

Colorado uses both state-collected and federal-collected data to address cost differences between districts. First, the state’s funding formula includes a regional Cost-of-Living adjustment that is based on data collected by the state, using a market basket approach which estimates the cost of a set basket of goods and services in communities and then provides additional funding based on the individual index values for each district. Second, as previously mentioned, it also incorporates federal NCES locale data to determine eligibility for additional rural school district funding. States that rely exclusively on federal data to calculate regional cost adjustments may be more impacted by any changes in the availability or reliability of federal data, while states that use state data or a hybrid approach are better insulated but bear the burden of collecting their own data.

What Does This Mean for States?

These examples highlight the varying degrees to which federal data is embedded in state funding formulas, and in turn, the different degrees of exposure that states have as the impact of federal policy changes are realized. The recent changes to Medicaid and SNAP eligibility will likely have the most immediate impact by reducing the number of students identified as at-risk in states relying on that data. Any future changes to the availability or reliability of other federal data sources, such as NCES or ACS data, due to staffing or funding reductions for federal agencies pose further challenges for states.

As state education leaders and policymakers grapple with the direct and indirect impacts of recent federal policy changes, APA Consulting is here to lend our school finance expertise. Get in touch here - and in the meantime, follow us on LinkedIn for more.

Points of Contact

Justin Silverstein, BS, is co-CEO of APA and a national expert in school finance with 25+ years of experience helping policymakers and districts strategically allocate education funds. He has led statewide studies, including funding reforms in Maryland and Nevada, and guided budget reviews and compensation analyses to optimize resource use.

Justin can be reached via email at jrs@apaconsulting.net.

Amanda Brown, EdD, is Vice President at APA with over 20 years of experience in school finance and evaluation. She leads state and local efforts to strengthen funding systems, support reform, and guide districts in strategic resource use.

Dr. Brown can be reached via email at arb@apaconsulting.net.