Since its implementation over a decade ago, California’s Local Control Funding Formula (LCFF) has targeted additional dollars to districts with larger shares of high need students—low-income, English Learners, and foster youth. The system has long used free and reduced-price meal (FRPM) enrollment as a proxy for income to allocate additional funding and to assess gaps in achievement. But a growing body of research identifies discrepancies between FRPM and other measures of poverty. And recent changes in federal and state policy—such as universal school meals—make it increasingly important to find alternative measures.

How accurate is FRPM as a measure of student need?

Although access to subsidized meals is means tested, FRPM is primarily a student nutrition program rather than a fundamental measurement of poverty. Indeed, school meals policy has evolved considerably over the past decade, with increasing shares of students identified as eligible directly through data matches to social safety net programs and the state expansion of universal school meals.

With these increases, however, FRPM rates have diverged from other measures of poverty, income, and socioeconomic status: while Census poverty measures show declines of around 8–9 percentage points from 2012 to 2022, FRPM rates increased nearly 2 percentage points statewide. These changes and trends complicate the usage of FRPM as a measure of need for the purposes of school funding.

FRPM is also binary: students are considered low-income whether they are in deep poverty or just below the eligibility threshold. Indeed, households both above and below the FRPM eligibility threshold (185% of poverty) have a wide range of income and socioeconomic circumstance.

There are also signs of potential misallocation. Districts with similar FRPM enrollment rates—and thus funding—can have dramatic differences in underlying economic conditions, demographics, and student outcomes. The table below provides recent data for four actual districts with similar rates and yet notable differences in other characteristics and outcomes.

Examinations of FRPM data linked to tax records in other states suggest that FRPM errors can be two-sided: missing potentially eligible low-income students and counting students whose family tax records indicate income over current thresholds. Our findings are consistent with this and reflect growing consensus among national researchers on the need to consider alternatives.

How might we better target need?

FRPM generally has worse predictive power for student outcomes than alternative income measures or indices. Broader socioeconomic measures are more highly associated with need across all dimensions. FRPM remains a strong predictor of student test scores, but less so for graduation, A–G completion, and absenteeism.

Our simulations suggest alternative measures could better target student need—correcting some of the uneven allocation induced by differences in FRPM rates across districts. They have the potential to direct more funding across multiple categories of need, including EL students, homeless students, foster youth, those with low test scores, those in poverty, and those in low-income communities. Importantly, however, alternative mechanisms that do not account for concentrations of student need could redirect funding from lower- to middle-income districts.

Absent new funding, alternative formula schemes may better proxy for student outcomes—but would create winners and losers. Fully implementing a new formula that holds all districts harmless could require doubling current supplemental and concentration funding under certain alternatives. Any changes would therefore require difficult choices and/or new sources of revenue growth.

Along with considering new alternatives to FRPM for measuring low-income status, we recommend:

Consider using duplicated counts for EL, foster youth, and homeless students. We find that duplicated counts, which provide additional funding for students who fall into multiple categories of disadvantage (such as low-income EL students), would better reflect the educational needs. This change could improve district capabilities to fund programs that effectively serve diverse student populations.

Explore data connections to automatically identify low-income students based on income and/or safety net records. Currently, direct certification—students certified for FRPM automatically via participation in other safety net programs—better explains gaps in outcomes across districts and could steer more funding towards lower-income districts and those with lower test scores. Policymakers should also explore data linkages between records tracking education, state income, and employment. While imperfect, these could allow for more automatic, precise, and consistent measures that improve both equity and efficiency.

Explore methods to incorporate broader community characteristics and income levels. Our analyses show that compared to FRPM, broader need indices that account for community conditions have strong predictive power to explain student outcomes, especially when considering non-test-score outcomes like graduation and A–G completion. These factors could be incorporated directly into a revised school funding formula.