Many posters on the blog have questioned the validity of using free and reduced lunch statistics to gauge the number of low-income students in a school, maintaining FRL is an imprecise and unreliable surrogate. Some posters contend it overestimates low-income students, while others say it undercounts them.
In a new report for the Brookings Institution, researcher Matthew M. Chingos says FRL data cannot serve as a proxy for poverty because more and more schools now offer free lunch to all their students.
“The use of FRL for policy and research purposes is quickly unravelling, due in large part to policy changes enacted by Congress in 2010 that expand ‘community eligibility,’ which allows schools with at least 40 percent of students identified as eligible for FRL to provide free lunches to all of their students and eliminate paper applications going forward. As a result, many schools will be unable to report student achievement for their FRL students,” he writes.
Why is that a loss? Because states are still obligated to report the performance of economically disadvantaged students, who historically lag more affluent peers. So, there has to be a way to identify such students. The question is also important to researchers who focus on how schools can improve educational outcomes for children from low-income households.
“New measures could include existing data on participation in means-tested programs, such as food stamps and Medicaid, or direct measures of socioeconomic status collected by states through new links between administrative data systems,” says Chingos.
“More direct measures of socioeconomic status could also be collected by states through their longitudinal data systems. Family incomes could be obtained from state administrative records, such as unemployment insurance systems. More ambitiously, Congress could authorize the Internal Revenue Service to work with states to link their student-level data to family income data that would identify disadvantaged students, but be sufficiently coarse to protect privacy,” he writes.
Anyone have suggestions on how to get an accurate sense of how many poor kids are in a school amid growing concerns over student data privacy?