Why free lunch may no longer be the best way to measure student need
Free- and reduced-price lunch, used for decades to set school funding levels and track other accountability measures, may no longer serve as an accurate proxy for student poverty.
Free-lunch rates fail to reveal the deeper nuances of students’ experiences in and outside school, which means education leaders and policymakers must look to other sources to identify the students who most need broader supports, according to the Data Quality Campaign’s new “Toward a Better Measure” report.
“For years, policymakers have viewed poverty and need as synonymous—and they are not,” says Jennifer Bell-Ellwanger, president and CEO of the education-focused nonprofit, Data Quality Campaign. “Policymakers must seek out and employ more precise and nuanced measures of obstacles to student learning so that they can truly understand student need.”
Since the 1940s, schools have used students’ household income to determine eligibility for free- and reduced-price lunch. One solution that can be added in the near term is “direct certification.” In other words, family enrollment in social services such as the Supplemental Nutrition Assistance Program (SNAP) and Temporary Assistance for Needy Families (TANF) is a better indicator of poverty. Under this approach, school districts themselves would no longer have to collect data from families, thus removing an administrative burden.
On a broader scale, education leaders should look beyond economic disadvantage to assess how public policy and other social conditions are disadvantaging students. Because direct certification also has limitations, the report also recommends:
- Capturing different dimensions of disadvantage: Free lunch rates and direct certification are not designed to capture the barriers an individual student faces. Education leaders must also examine factors such as historic disinvestment and racial or economic segregation. Tax records or local poverty metrics can also provide more data on individual students’ circumstances.
- Using your existing data systems to find nuance: Free lunch and direct certification are binary, meaning they reveal student poverty at a single point in time. More sophisticated approaches, such as an examination of community-level needs, can provide a broader understanding of students’ disadvantages. And, longitudinal data collected over multiple years can identify students who are experiencing chronic poverty.
- Prioritizing equitable data use: “Asset framing”—or emphasizing and building on students’ strengths—allows education leaders to chart paths to better academic outcomes. Because terms such as “at risk” create negative biases against students, California is using “at promise” to describe economically disadvantaged, underachieving and chronically absent students. Parents should also be included in conversations about how and why schools are measuring need.
Several districts and municipalities are already making these shifts. Washington, D.C.’s accountability system measures SNAP/TANF participation, homelessness, and high school students who are more than a year older than the expected age for their grade. In New Mexico, the Department of Tax and Revenue must share tax return income data with the Department of Public Instruction. Students are then placed in one of five income categories to calculate a family income index for each school.
Other organizations are also working on the problem. Chapin Hall, a children’s advocacy institution at the University of Chicago, collects K-12, postsecondary, and social services data to pinpoint barriers that keep vulnerable students from graduating college. This data is then used to make tailored policy recommendations. The National Academies of Sciences, Engineering, and Medicine compares student-level outcomes, such as educational attainment and attendance, to system-level disparities such as racial segregation and lack of access to high-quality early childhood education.
“Leaders should always be examining whether their measures provide them with the best data to answer their specific questions. In the case of student need, those measures are outdated and insufficient,” Bell-Ellwanger says. “And while beginning to develop even more nuanced indicators of student disadvantage will take time and effort, using a more accurate measure to understand and address student need is worth it.”
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