How we count matters. For decades, the National School Lunch Program (NSLP) has provided students free or reduced-price meals while equipping school administrators with data on students’ socioeconomic status. Counts of students receiving free or reduced-price lunch (FRPL) have long been used to allocate school funding, monitor for accountability, understand opportunity and achievement gaps, and ensure students receive the social services they need.

Before 2010, families filled out paper forms to report their household income to school administrators and become eligible for FRPL. Researchers valued these data as relatively unbiased, universal, and inexpensive to collect. Since 2010, however, changes to NSLP have made these data less reliable as a proxy measure of student poverty.

The Community Eligibility Provision

In 2010, Congress introduced the Community Eligibility Provision (CEP). CEP expands access to free school meals by allowing schools or districts with high shares of low-income students to provide free meals to all students, but states have different ways of documenting this. One state might report the share of FRPL-eligible students in CEP schools as 100 percent, while another state might report the most recent percentage documented using the old paper forms.

In response, states and school districts are exploring alternative measures of student poverty in addition to or instead of FRPL status. This report considers key issues for administrators and data users as the take-up of CEP and landscape of alternative measures expands.

Today’s Measures of Student Poverty

For school accountability purposes, all 50 states and the District of Columbia use enrollment in the Supplemental Nutrition Assistance Program (SNAP) to identify low-income students through a process known as direct certification. Additionally, 45 states use participation in Temporary Assistance for Needy Families (TANF), 18 states use family income data from Medicaid, and 15 states use participation in the Food Distribution Program on Indian Reservations. Many states have also created “special student statuses” to identify categorically eligible students, such as those experiencing homelessness (15 states), living in foster care (26 states), or having migrant status (14 states).

But because some states have more expansive safety net programs than others, data from programs such as TANF and SNAP are inconsistent across states. Similarly, we know that different populations use public benefits at different rates, so too heavy a dependence on enrollment in safety net programs to measure student poverty might lead to an undercount. For example, the high share of English language learners and of Hispanic and Latinx students—who use public benefits at lower-than-average rates—in Baltimore City Public Schools has meant that some schools have lost funding they previously qualified for before transitioning to CEP.

Other Proxy Measures Worth Considering

To more accurately identify low-income students, more research is needed into viable alternatives, such as the following characteristics:

• parental educational attainment
• family income as reported to the Internal Revenue Service
• geocoded data from census-type sources
• receipt of additional safety net services
• student mobility measures
• early exposure to poverty

As states and school districts continue to pursue alternative measurements of student poverty, data users—including families, policymakers, and researchers—will rely on administrators to clearly document and communicate these changes. The stakes are high: misunderstandings can lead to incorrect test score comparisons or school funding declines even as the number of low-income students in a given school or district remains unchanged.

Accurate and reliable measures of student poverty are critical for advancing educational equity and effectiveness. How we count matters.