Key Takeaways

California’s system for funding schools—the Local Control Funding Formula (LCFF)—is well into its second decade. Overall funding levels have nearly doubled since LCFF’s inception, and targeted funding for high-need students (defined as low-income, English Learner, or foster youth) has been shown to boost student achievement. Yet California schools continue to face fiscal and academic challenges.

What changes to the funding formula could help address these challenges—and how might they affect district funding levels? We model the state’s funding system—excluding charter schools and county offices of education—and simulate a variety of alternative allocations to highlight fiscal costs and trade-offs. We find:

• Future enrollment declines may offer the opportunity to modify the funding formula without reducing funding for any districts. We estimate that if per student funding continues to grow at the same rate as overall education funding, the state could leverage an additional $7.5 billion for schools, even without new revenue sources. The state could direct this increase through the LCFF or toward other programs and one-time expenditures—or it could use the revenues to fund changes to the current formula.→
• Basing funds on student enrollment rather than attendance would benefit higher-need districts. Current LCFF allocations are based on students’ average daily attendance. We model a $3.8 billion switch to enrollment-based funding. Most districts would see changes of less than $250 per student. Gains would be largest in districts with the most absences. Districts with high attendance would receive less funding than they would with an equivalent increase under the LCFF.→
• Regional cost adjustments would redirect funding from rural and high-need districts to urban and suburban areas. We model a potential $2.5 billion cost adjustment to the LCFF’s base grant to account for geographic differences in competitive teacher wages. Relative to a comparable increase in LCFF funding, this change would provide the average high-need student with less funding per pupil. Rural and town districts would receive significantly less, while urban and suburban districts would see moderate increases per pupil.→
• Providing additional funding for students with overlapping needs would direct more resources to most high-need districts. The current funding formula does not apply any additional funds for students in multiple targeted categories (e.g., English Learners from low-income families). Boosting funding for these so-called “dual need” students would increase revenues for most high-need districts—by about 2 percent on average, or roughly $2 billion statewide—as these districts tend to have more dual-need students.→
• Changing how the formula addresses concentrated need could increase revenues for districts with moderate shares of high-need students. The current formula includes large funding increases for districts with the highest concentrations of high-need students (above 55%). Tweaks to the formula to “smooth” this discrete cutoff could still provide substantial increases for the highest-need districts while also providing resources for districts with moderate but still significant concentrations of high-need students (at a cost of roughly $1.5 billion statewide).→

The stakes of these design choices are substantial. Even small changes to the LCFF could shift the distribution of billions of dollars across California’s more than 1,000 school districts serving nearly 6 million students. While we remain agnostic about the “correct” funding approach, our analysis aims to provide policymakers with a clearer understanding of the trade-offs involved in these decisions.

Established in 2013 with the goals of providing more transparency and local control, the LCFF has contributed to progress at California schools but continues to face challenges. In this section, we provide a brief overview of the LCFF’s history and describe ongoing concerns that motivate the potential adjustments considered in this report.

Prior to the LCFF, California schools were funded under the so-called “revenue limit” system. This approach sought to equalize per pupil funding across districts but was followed by decades of carve-outs, exceptions, and specialized programs. By the late 2000s, the system was critiqued as outdated, opaque, and unequal. The LCFF simplified the school funding system, removing nearly all of the special programs in favor of a general-purpose base grant and targeted funding for districts based on the number of high-need students they serve. Districts are required to use the added funds to improve outcomes for high-need students but have substantial spending flexibility to achieve those ends.

Although the LCFF was a major change to how schools were funded in California, it did not directly increase state funding—it only changed the allocation of funds across districts. However, there were concurrent, substantial increases in state revenues for schools due to the growing economy and the tax increases passed through Proposition 30 in 2012. The rapid revenue growth facilitated general increases in school spending, and the LCFF distributed more of the additional funding to higher-need districts.

Adjusted for inflation, average revenues per student rose from just over $15,000 in 2012–13 to nearly $30,000 in 2023–24 (Figure 1). LCFF-specific revenue rose from roughly $8,000 per student to nearly $15,000. LCFF revenues account for about half of total revenues, although they comprise about 70 percent of districts’ general fund revenues, depending on the year.

It is important to note that our focus on LCFF revenues throughout this report means that we exclude other significant revenue sources. Federal funding, non-LCFF state funding, and other local funding mechanisms, like parcel taxes or bonds for capital expenditures, play an important role in district operations, and ultimately, student outcomes, but these other sources of funding are outside the scope of this report.

Despite the massive funding increases, California’s school spending is only slightly above average in comparison with other states, though it falls below average when adjusted for the cost of living (Lafortune and Guinan 2025). Meanwhile, California’s school funding system ranks as one of the more progressive in terms of the additional funding allocated to higher-need students and districts (defined most often by income), though its exact ranking depends on the measure.

California’s school funding allocation system is not without problems, however. Though the system does include adjustments for the challenges facing small, remote districts, with grants for small schools and transportation costs, regional cost-of-living differences are not accounted for. Further, the way the state identifies high-need students—primarily by low-income status as proxied by participation in the school meals program—has long been problematic and its viability is increasingly under pressure with the state’s recent move to universal free meals (Lafortune, Ugo, and Guinan 2024). The rise of chronic absenteeism after the pandemic is yet another challenge as lower attendance rates reduce funding more for districts struggling the most to get students back in the classroom.

There are other issues as well. For example, one longstanding concern has been that funding is based on the demographics of districts rather than of schools (Hill and Ugo 2015). Resources for high-need students and schools depend crucially on how students and dollars are distributed across schools within the same district (Blagg, Lafortune, and Monarrez 2022). This issue is pronounced in districts with a wide variation in school concentrations of high-need students—and for high-need charter schools. Evidence suggests targeted dollars did not always get spent on the high-need students that generated them (Lafortune, Herrera, and Gao 2023).

Major reforms to school financing are often accompanied by increases in state funding (Candelaria and Shores 2019; Lafortune, Rothstein, Schanzenbach 2018). Otherwise, changes under fixed funding levels necessarily create winners and losers, which is often politically and operationally infeasible. For example, changes under LCFF were phased in over a period of rising revenues to insulate districts from potential losses. In this section, we describe how declining enrollment could allow the state to modify the LCFF without generating lower funding for any districts.

From an economic perspective, projecting future revenue increases is fraught—and often inaccurate. Thus, rather than consider potential revenue increases, we frame our simulations around a mechanism that could lead to higher per student revenues even absent new state tax revenue: declining enrollment.

Statewide, enrollment has fallen by about 7 percent since 2014–15 and is projected to fall nearly 11 percent by 2034–35. For districts with shrinking populations, fewer students means less funding. And declining enrollment means no additional state revenue. But there is one key nuance: as the state budget grows, the Proposition 98 guarantee will likely lead to additional funding per student, even absent higher levels of funding overall.

This greater per student funding could simply be added into the current LCFF allocation, or it could be used to finance one-time or “off-formula” programs, such as teacher residencies, expanded learning opportunities, and literacy curricular reform. Alternatively, it could be used to allow for formula changes, shifting the distribution of revenues but using the rising funding to ensure no district is worse off than before.

How much revenue might we expect given current trends and projections? Our estimates rely on the following assumptions:

1. Enrollment declines as projected over the next five years, to 5,470,544 students in 2030–31.
2. The ADA (average daily attendance)-to-enrollment ratio remains constant at its level over the past two years (93.7%). In 2030–31, this implies a statewide ADA of 5,123,922.
3. The Proposition 98 guarantee grows in inflation-adjusted terms at the average level of growth in the past decade (roughly 2.7%). This implies an inflation-adjusted guarantee of $132.2 billion in 2030–31.

Then, if per student funding grows at exactly the rate of Proposition 98 funding, enrollment declines would generate “excess” funding under the guarantee of $7.5 billion in 2030, roughly $1,500 per student. Extending to 2035 yields an excess of $17.9 billion. Alternatively, if we cap inflation-adjusted per student spending growth at 2 percent instead of 2.7 percent, this would mean $11.5 billion and $26.3 billion in 2030 and 2035, respectively.

This “excess” is hypothetical, created by allowing the baseline rate of per student funding to increase more slowly than it would have otherwise if all Proposition 98 funding were distributed as it has been in recent years (i.e., mainly via LCFF). Falling enrollment will likely mean some combination of higher year-to-year cost-of-living (COLA) adjustments and/or additional state funding for one-time and other non-LCFF programs. However, the state could consider funding modifications to the LCFF that address ongoing critiques and emerging challenges. If the state pursues this option, it will be essential that policymakers have a better understanding of the trade-offs involved in the various funding alternatives.

We examine the current funding formula and potential alternatives using a flexible, dynamic school funding model—built around the structure of the LCFF—that can simulate multifaceted changes to any of the main formula components. In this section, we describe our approach to documenting and understanding trade-offs in the allocation of funding. We begin by describing how the current funding formula works and then outline the kinds of adjustments that we simulate in our model.

How Does the Current Formula Work?

Under the LCFF, districts receive an equal funding amount (base grant) for every student, by grade level, plus additional funding for each high-need student (supplemental grant). Districts with large shares of high-need students are given even more funding for each high-need student above a set threshold (concentration grant). The base grant varies by grade level, with higher rates for students in grades TK (transitional kindergarten)–3 and 9–12. Districts receive an additional 20 percent in supplemental grant funding for each high-need student ($14,952 total) and an additional 65 percent on top of the supplemental grant ($23,051 total) in concentration grant funding for each high-need student above a district high-need share of 55 percent.

Figure 2 shows overall LCFF funding by district share of high-need students, using typical enrollment shares across grades. Funding increases substantially for higher-need districts, particularly above the 55 percent threshold. For a district with nearly all high-need students, this corresponds to per student funding that is nearly 50 percent above the base grant, or roughly $5,600 more per student. About half of districts have a share of high-need students between 40 and 80 percent, with funding per student ranging from $12,400 to $15,200.

We Simulate Funding Outcomes under Several Potential Modifications

In this report, we focus on three key funding formula mechanisms: the size and structure of the base grant, the criteria defining high-need students, and the way additional funds are allocated based on these criteria. Our model allows us to accurately replicate these components of the LCFF, isolate the potential impacts of adjusting any single component, and simulate the dynamic interactions when multiple components are changed. We present simulation results for each modification in the following sections. Notably, our current model excludes charter schools and county offices of education, which enroll roughly 13 percent of the state’s students; where total funding amounts are reported for the state, these will slightly underestimate the total cost inclusive of charter and county office of education schools.

In this report, we focus on the differences in per pupil allocation under LCFF compared to our simulations to understand which districts would receive more or less funding under each scenario. We do not evaluate the adequacy of the current level of funding but consider the distributional effects of alternatives to evaluate who is affected by a formula change. We also do not model the revenue mechanism—that is, how the funds to pay for the calculated funding amounts are generated (i.e., state aid, property taxes, Education Protection Account). Since the additional funding required to implement a change could have also been spent within the formula, we also compare results to a cost-equivalent increase distributed through the current LCFF formula. In this sense, we can understand not only the impact of any specific change, but also how it would compare to the status quo of using any additional funding to increase the total amount in the current funding formula.

There are many ways to examine and evaluate simulated formula outcomes. Our primary metric is the difference between simulated and actual funding per student, at the district level. We compare these differences across districts of varying demographics, most often districts with different shares of high-need students.

When we compare across student demographic groups, we calculate the average funding for the typical student in a given group at their specific district. In other words, assuming all students in the same district get the same level of funding, we can calculate average funding by student groups using their enrollment at each district. To the extent that districts target funds differentially across student groups within the district, this could under- or overstate the “true” funding amount students are exposed to.

The base grant serves as the foundation of California’s school funding formula, providing the per pupil allocation needed to cover the basic costs of educating a typical student. In this section, we simulate three important ways of modifying the base grant. First, we examine how increasing the base grant would affect funding allocations—and potential trade-offs that would be required to do so while holding total state funding fixed. Second, we examine the impact of moving from average daily attendance to an enrollment-based funding formula. Third, we examine the impact of including adjustments for regional cost differences.

Increasing the Base Grant Affects High-Need Districts the Most

The simplest alternative to simulate is arguably not a modification at all: increasing the base grant. State funding increases have led to higher base grants, often through a year-to-year cost-of-living adjustment (COLA) on the formula. Figure 3 plots the funding increase for every district under increases of 5, 10, and 15 percent.

Because base funding interacts with supplemental and concentration grant funding, an increase of 5 percent, or roughly $575 per student, leads to larger increases in districts with higher shares of high-need students. For districts with shares of high-need students close to 100 percent, the increase is, on average, closer to $800 per student. Similarly, for a 15 percent increase in the base of about $1,700 per student, some of the highest-need districts see nearly $2,500 more per student. This underscores a simple but key component of LCFF that affects the simulations that follow: any modifications to base funding reflect the current structure of the funding formula, particularly its progressivity that tilts funding toward higher-need districts.

Using Enrollment Instead of Attendance Would Increase Funding for High-Need Students

Since base grant funding is distributed per student, the first step in allocating it is determining the number of students in a district. Under the LCFF, average daily attendance (ADA) is used to count students. While this was once a common practice nationally to incentivize districts to address poor attendance (Ely and Fermanich 2013), California is now one of only six states currently using an attendance-based school funding formula (Education Commission of the States 2024). Funding based on attendance reduces revenues for districts with relatively high absenteeism rates, and these districts tend to serve greater shares of high-need students (Technical Appendix Figure B2). States that have recently switched to enrollment-based funding cite both equity goals and the need for districts to have more budgetary certainty (Hahnel and Baumgardner 2022). Especially in the midst of continued enrollment declines and elevated rates of chronic absenteeism (Guinan and Hill 2025), understanding how a switch to enrollment-based funding would redistribute resources across California districts has renewed policy relevance.

Counting students using enrollment in the funding formula could cost the state an additional $3.8 billion (excluding charter and county office of education schools). To understand the implications of this change, we compare three funding scenarios: current LCFF funding (baseline), the enrollment-based funding simulation, and a hypothetical $3.8 billion (8.4%) increase distributed through the current LCFF formula (Figure 4). This third comparison helps isolate the effect of using enrollment rather than attendance—showing how enrollment-based funding targets resources differently than the existing formula.

The quarter of districts with the largest shares of high-need students would receive about $100 more per pupil (a less than 1% increase) under an enrollment-based model, compared to distributing the same $3.8 billion through the current LCFF formula (see Technical Appendix Figures B3 and B4 for the full distribution of funding allocations by district share of high-need students). This occurs because high-need districts also have higher absence rates—a slightly larger share of the $3.8 billion increase would reach them through funding that no longer penalizes poor attendance. But for the average high-need district the difference is fairly small because routing an equivalent funding increase through the existing formula using ADA would still benefit higher-need districts due to the structure of the formula.

The largest shifts occur across attendance rates—districts with the best attendance would receive $220 less per pupil (1.5%) relative to an equivalent increase through the current LCFF formula, while districts with the most absences would receive $260 more, or 1.8 percent (Figure 4). More than half of districts would see funding differences of less than $250 per pupil (under 2%) (Technical Appendix Table B1).

There are also differences across student groups currently targeted by the LCFF for additional funding. English Learners would receive about the same funding while low-income students gain $30 per pupil under enrollment-based funding compared to the equivalent LCFF increase (Technical Appendix Figure B5). Students not demonstrating proficiency on standardized tests would receive approximately $30 to $40 more per pupil under enrollment-based funding, while students meeting or exceeding proficiency standards would receive approximately $50 to $60 less. This suggests enrollment-based funding would direct relatively more resources toward districts struggling with both attendance and academic outcomes.

A Regional Cost Adjustment Would Redirect Funds from Rural and Town Districts to Urban and Suburban Districts

Given that roughly 80 percent of school spending goes toward staffing, local costs—including prevailing wages and housing prices—directly affect a district’s ability to attract and retain quality staff (Kemper Patrick and Carver-Thomas 2022). Income and housing costs vary widely across the state: average incomes in the Bay Area, for example, are more than double those of the Central Valley and Inland Empire (Bohn and Duan 2025). This raises an important question: if the base grant is meant to cover the cost of a standard education—that is, before any additional resources needed to address the challenges facing high-need students—should it be adjusted to account for these local cost factors that affect all students? In other words, should districts in higher-cost areas receive more base funding?

To address this question, we simulate incorporating a regional cost adjustment to the base grant based on the Comparable Wage Index for Teachers (CWIFT). CWIFT directly measures competitive wage pressures in local labor markets—the primary factor affecting teacher salaries (Rose and Sengupta 2007)—by capturing variation in the earnings of college-educated workers in the area, with higher values indicating higher-cost areas. While CWIFT is our preferred measure, we compare other regional cost measures in Technical Appendix C.

We apply these adjustments at the county level, as counties typically represent the labor markets from which districts recruit. Importantly, we also rescale the index and limit the range of the cost adjustments, capping them at the 75th percentile (about 8%) and including a floor where districts at the 25th percentile and below receive no adjustment.

Figure 5 illustrates how per pupil funding would change across districts with varying levels of high-need enrollment if the LCFF base grant included a regional cost adjustment. Adding this adjustment while ensuring no district loses funding would cost the state $2.5 billion to implement (5.4% increase). Compared to the same $2.5 billion increase through the current LCFF, the adjustment provides larger increases to districts serving fewer high-need students—districts in the bottom quarter by need would receive $100 more per pupil, while the average high-need student would receive $20 less (Technical Appendix Table C6).

A regional cost adjustment creates policy trade-offs with the LCFF’s existing funding priority of targeting high-need students. As the vast majority of high-need students are low-income, districts with higher concentrations of these students are often located in lower-cost areas. Increasing resources for higher-cost areas necessarily means that any adjustment will on average redirect funds toward districts with fewer high-need students.

Looking at different student groups, we find that funding would be lower under a regional cost adjustment for the average low-income student compared to an equal funding increase in LCFF. However, English Learners would receive about $10 more through a cost adjustment than through a broad increase in LCFF funding, suggesting these students are more concentrated in areas where wages are elevated (Technical Appendix Table C6). Students scoring below standard on standardized tests would receive about $30 less under a cost adjustment, implying that a cost adjustment likely would not direct more funding to districts struggling with student achievement.

The most significant redistribution occurs between urban and rural areas—and the impacts are imbalanced (Figure 6). Rural and town districts would receive less funding under a cost adjustment than they would under a comparable increase through the LCFF—around $400 less per pupil on average. Meanwhile, urban and suburban districts would receive more under the cost adjustment, but the gains are much smaller in magnitude (roughly $50 per pupil). Even when we control for high-need student enrollment, the urban/suburban funding advantage is stark (Technical Appendix Table C7).

Further, the rural districts that would see some of the largest funding reductions face fixed expenses like transportation and administrative overhead that must be spread across smaller student populations, resulting in higher per pupil operational costs not captured by a CWIFT-based adjustment (Levin et al. 2018; Kolbe et al. 2021). They also face distinct challenges recruiting specialized staff such as bilingual educators (Hill and Deng 2025).

Shifting funding away from high-need and rural students could be justified if a regional cost adjustment meaningfully addressed the staffing challenges that districts in expensive areas face. To evaluate this, we examine whether districts in higher-cost areas that would receive larger adjustments currently pay higher salaries or have larger class sizes, after controlling for total LCFF revenues.

We find that districts in higher-cost areas do pay higher median teacher salaries (Technical Appendix Table C2). However, the relationship between cost-of-living measures and student-teacher ratios is weak and inconsistent, and the districts that would receive the largest adjustments may not systematically struggle more with staffing (Technical Appendix Table C3). This may be because districts in expensive areas that would benefit from a regional cost adjustment can have additional revenue sources—such as local parcel taxes and parent donations—that are often unavailable to the rural and high-need districts that would bear the brunt of the funding reductions (Sonstelie 2015; McGhee and Weston 2013).

It is unclear if a regional cost adjustment would better align funding with districts’ ability to attract and retain staff in expensive metropolitan areas. Furthermore, many rural districts face unique staffing challenges and would see much larger relative losses in per pupil funding than urban and suburban areas would gain when compared to an equivalent funding increase under the current formula.

The principle behind weighted formulas is straightforward. Students from low-income backgrounds and those facing other forms of disadvantage tend to have worse academic outcomes that reflect the various challenges they face. Targeted funding may help address these challenges and promote equitable student outcomes (Griffith and Burns 2025). Implementing this in practice, however, requires policymakers to choose which student characteristics warrant additional investment. In this section, we simulate funding adjustments that would count high-need students in different ways, including targeting additional funding for students who fall into multiple categories of need and refining the measure for low-income.

Academic challenges arise in many forms, affecting students with a wide variety of characteristics and experiences. Prior to the LCFF, the school funding system included several different categorical programs, each meant to address particular student needs. But each addition added complexity, and by the time the LCFF was implemented, the interlocking web of special programs had grown unwieldy. The new funding formula was simpler, identifying low-income, English Learner (EL), and foster youth status as broad markers that captured students with a wide variety of academic challenges. This more general measure of student disadvantage drove funding, and districts were given flexibility to spend on resources that addressed student needs as they arose locally. There are, however, some issues that remain with how student need is measured in the LCFF.

The LCFF allocates funding based in part on district enrollments of high-need students. The counts of high-need students are unduplicated, meaning that students are counted only once as high-need, even if they are in two or more of the designated groups. Despite the rationale tying academic challenges to student need, this structure does not explicitly target funds to each of a student’s needs, opting instead to split additional grants among the multiple challenges that an EL in foster care, for example, may face.

In California, the unduplicated count of high-need students adds up to about 67 percent of total enrollment. Out of these, just under one in four high-need students are both low-income and English Learners. These “dual-need” students make up about 16 percent of total statewide enrollment, or more than 880,000 students. Though direct evidence is hard to find, several studies report correlations that suggest worse outcomes for ELs from low-income families compared to other ELs with higher family incomes (Thompson 2017). In our analysis, we focus on funding for low-income students and English Learners given the substantial overlap of some of the other targeted groups with low-income status; foster youth, for example, are automatically eligible for free and reduced-price meals (FRPM), the proxy used in the LCFF to determine low-income status.

In Figure 7 below, we show the simulated results from using duplicated counts of low-income and EL students to determine district supplemental grant funding. The figure reports funding outcomes for a simple duplicated count where low-income and EL status are weighted equally, with students in either group generating an additional amount of revenue equal to 20 percent of the base grant. We maintain the concentration grant based on the unduplicated count of high-need students, as well as current base grant levels. We then compare results to (1) the current LCFF, and (2) the current LCFF structure, but with the same funding increase required to fund the addition of duplicated counts.

Relative to the current LCFF, adding duplicated supplemental grant funding for low-income and EL students would increase revenues for every district, about $1.7 billion (2.4%) overall, or $279 per student on average. Districts where more than 88 percent of students are high-need (the top 20% of the distribution) see a $659 average funding increase, while those whose shares of high-need students are below 39 percent (the bottom 20% of the distribution) see funding rise by just $43. There is significant variation across districts in their shares of dual-need students, and these differences drive the amount of funding change— with the most substantial gains for those that serve more dual-need students.

In order to focus on how much the allocation of state funding changes across districts relative to the current LCFF, we simulate a cost-equivalent increase in total state funding. The differences in funding between the duplicated counts simulation and the LCFF with added funding are smaller, though it is notable that funding rates are slightly higher for districts with greater unduplicated shares of high-need students. Given how often low-income and EL status overlap, districts with higher counts of unduplicated high-need students also have more dual-need students.

Though we focus on dual-need students that are both ELs and from low-income families, there are several other student groups we could consider. Foster youth are already one of the student groups in the current high-need definition and California also identifies other students with additional needs, like migrants and students experiencing homelessness. Applying duplicated counts for these groups would direct more funding to their districts, but the simulated differences in funding across districts are small, as we show in Technical Appendix D. Tying funding directly to district counts of students in various groups could, however, encourage greater spending on services for them—rising rates of student homelessness, for example, have been a recent concern (Guinan and Lafortune 2025).

Duplicating counts of high-need students raises an additional concern of how to weight allocated funding to students from different targeted groups. Our analysis applied the same 20 percent boost from the supplemental grant to each student group, but a recent review found that, for the 37 states providing dual funding for students from multiple need groups, there is wide variation in the relative weights applied to the funding allocated for them (Griffith and Burns 2025; Kaplan 2025). We consider a wider range of alternatives using different relative funding weights in Technical Appendix D.

Shifting away from School Meals to Identify Low-Income Students Could Improve Targeting

When choosing criteria to define which students are high-need, policymakers must also decide how to accurately identify these students. Consider low-income students as a key example: while family income data would be the most direct measure, this information is not systematically collected for all students. Under the LCFF, low-income students are instead identified through their participation in the free and reduced-price meals (FRPM) program. This means-tested program is a readily available proxy for income that is widely used for educational administration in California and around the US.

There is a growing divergence, however, between FRPM and other measures of poverty (Lafortune, Ugo, and Guinan 2024). The school meals program can be unreliable as a measure of student family income: students who are income-eligible but do not enroll in FRPM are missed in those counts. Eligibility also varies with volatile family incomes, and students that qualify for the program in any given month remain counted as low-income throughout the year. The challenges for this measure have been exacerbated by the recent switch to universal free meals in California. With family income forms no longer needed for school lunch, some districts have struggled to collect them and risk losing access to substantial amounts of targeted state and federal funding.

One promising alternative to FRPM often highlighted in the literature is direct certification, the identification of low-income students based on their household’s participation in other income-based social service programs like CalFresh, CalWORKs and Medi-Cal—the state’s food assistance, cash assistance, and Medicaid programs, respectively. Direct certification rates may reflect student poverty more accurately because the safety net programs through which students are certified have more extensive income verification procedures. Additionally, direct certification has a stronger link to student outcomes (Spiegel, Domina, and Penner 2024).

Direct certification does, however, come with its own limitations. Families still must participate in these other safety net programs to be counted and data from the California Department of Social Services shows that program participation varies substantially across counties. Immigrant families are also less likely to use various programs and further, recent federal and state changes in safety net program eligibility—including most notably, the recent changes under H.R. 1 in 2025—could add additional instability in these measures of identified students from year to year.

Using our funding simulator, we evaluate direct certification as an alternative definition for low-income students, modeling the effects on funding distributions across districts. Given data limitations, we simulated a funding model with direct certification under duplicated counts. Our results below focus on the comparison to the duplicated counts simulation above, which used FRPM participation to define low-income students. We also make a slight adjustment to the funding formula because though roughly 63 percent of California students are FRPM-eligible, only 39 percent are directly certified. Both measures are meant to reflect student poverty rates, but differences between the programs—the income eligibility thresholds, for example—lead to differences in the shares of students identified. The Community Eligibility Provision (CEP) of the school meals program, which uses direct certification shares instead of school meals applications, multiplies a district’s direct certification share by 1.6 to get a low-income student share that is more comparable. We instead adjust the supplemental grant factor up to 33 percent—at this rate, total state spending is equivalent to using FRPM-eligible counts at the original 20 percent rate.

The simulated funding changes here are similar to those in the duplicated counts model using FRPM. Since we set total spending to be equal, we again see it increases by $1.7 billion, or 2.4 percent. Across district shares of high-need students, we see differences ranging from 1.0 percent for districts at or below the 20th percentile of the high-need distribution and below up to 5.2 percent in districts in the top fifth.

Figure 8 below shows how districts with greater shares of high-need students would receive more funding under duplicated counts based on direct certification. However, the more significant driver of funding differences is the district share of directly certified students. Across the distribution of district shares of high-need students, we see that districts with the most students identified through direct certification have the largest funding increase relative to the current LCFF. The simulated changes in funding range from about 2.6 percent for high-need districts with low direct certification shares to 6.2 percent for those with higher shares. Substantial differences are evident among districts with fewer high-need students as well.

Although there are differences in funding across district shares of high-need students, the key change in moving from FRPM shares to direct certification is less about overall high-need student enrollment rates and instead more about how the two rates relate to each other. Among all of the students who are FRPM-eligible—including those who complete meals applications, but also automatically eligible students like foster youth, homeless, and migrant students—the share who are directly certified has long been quite high. When Medi-Cal was added as one of the programs through which a student could be directly certified in 2017, the share of FRPM-eligible students identified through direct certification rose to roughly two-thirds and has remained at about that level in the several years since.

Given that, the difference between our simulations using FRPM and direct certification comes down to the share of students who are FRPM-eligible but not directly certified. The gap between those two rates could vary across districts for a few reasons. Districts with different local income distributions may have relatively fewer students who are eligible for the various safety net programs, which tend to have lower income-eligibility thresholds. There could also be variation in safety net program participation across the state. Finally, with different auditing standards between the school meals applications and other safety net programs, districts with more errors on meals forms could have lower relative direct certification rates.

After designating some set of students as high-need, the next concern in developing the state’s weighted funding formula is determining just how much additional funding to direct to them. In this section, we consider the trade-offs involved in changing the way the LCFF targets additional funds to districts with larger shares of high-needs students.

In addition to the base and supplemental grants, the LCFF provides concentration grants for districts with shares of high-need students above 55 percent. The motivation here is that concentrated poverty presents additional challenges over and above those facing any particular high-need student. For example, a low-income student may have inconsistent access to transportation or health care, but a low-income community could lack broader supports like reliable public transit or local hospitals.

The structure for the LCFF’s targeted funding is often described as a kinked design. Figure 9 below shows LCFF funding per student across rising district concentrations of high-need students: moving from the low end, funding gradually increases as high-need shares rise to 55 percent, where it then continues increasing, but now at a higher rate. This abrupt change in the rate at which districts receive funding for each additional high-need student is the kink. The design has the advantage of being straightforward while still addressing the concerns about the increasing challenges associated with rising levels of need in a given community. There are, however, some costs to the coarseness of the measure. The amount of funding generated by each additional high-need student is dramatically different above and below the threshold—20 percent of the base grant compared to 85 percent. The concerns about community-level disadvantages, however, do not change so discretely, nor is there a fundamental reason to believe 55 percent is the ideal cutoff for such a sharp increase.

Figure 9 also shows an alternative, “smoothed” formula. This alternative still addresses the rising challenges that emerge with increasing concentrations of high-need students, but it avoids the kink that arguably results in underfunding districts with moderate shares of high-need students below the 55 percent threshold. The alternative starts with a 20 percent increase in funding per high-need student and gradually rises to an 85 percent increase as the district share of high-need students increases. Compared to the current LCFF, funding rises for every district, with the total increase adding up to $1.5 billion.

The smoothed formula provides more funding for districts whose concentration grants would not have accumulated to substantial levels—or at all, in the case of districts with high-need shares just below 55 percent. Moderate-need districts with shares of high-need students between 45 and 65 percent of enrollment would see total gains equal to the gains for districts that were outside of those bounds. Increases in funding are smaller at the highest-need districts given the drop from the steep 85 percent base grant increase per high-need student to the more gradually rising smoothed amount; and the lowest-need districts also see relatively small gains: though the additional funding for the marginal high-need student is slightly above the original 20 percent in the supplemental grant, these districts still do not enroll enough targeted students to generate substantial revenues.

Funding per student at moderate-need districts would be, on average, $660 (4.9%) higher, while districts with more or fewer high-need students would have average increases of about $230 (1.6%). Generally, the gains are larger for districts below the 55 percent concentration grant threshold. Districts where less than 45 percent of students are high-need would see funding increase by 2.4 percent rather than the 1.3 percent for districts above 65 percent high-need. Gains for districts between 45 and 55 percent high-need would be 5.1 percent, compared to 4.8 percent for districts above 55 and below 65. Overall, the smoothed formula directs funds to where needs are rising rather than splitting districts into two groups and leaving one where concentrated disadvantage is not yet serious enough to warrant additional funding.

As discussed earlier, declining enrollment and state budget growth could mean billions in additional per student funding. In this section we use our earlier estimate of an additional $7.5 billion in per student funding by 2030 to examine which changes to the LCFF—and of what magnitude—might be possible with this amount of funding. We present these hypotheticals not as projections or recommendations, but rather as an exercise to concretely compare alternatives under a potential increase in future funding due to enrollment declines.

Our baseline comparison is to allow additional funds to flow into the LCFF. For policy changes that cost less than $7.5 billion, we allocate any additional funding via a larger base grant. We describe alternatives in more detail below:

• Baseline: 11.6 percent increase in the base grant.
• Cost-of-living adjustment + base grant: Use the Comparable Wage Index for Teachers (CWIFT) to adjust for regional cost differences and increase the base grant by 7.5 percent.
• Enrollment count + base grant: Switch to enrollment-based funding and increase base grant by 5.4 percent.
• Higher supplemental grant: Increase the supplemental grant from 20 to 42 percent.
• Higher supplemental and concentration grant: Increase the concentration grant from 65 to 91.75 percent; increase supplemental grant from 20 to 35 percent.
• Duplicated EL and FRPM counts + base grant: Replace supplemental grant with a 20 percent grant on FRPM and a 20 percent grant on EL students. Retain existing concentration grant based on district high-need share. Add additional increase of 8.8 percent to the base grant.
• Duplicated EL and directly certified counts + base grant: Replace supplemental grant with a 33 percent grant on directly certified students and a 20 percent grant on EL students. Retain existing concentration grant based on district high-need share. Add additional increase of 8.8 percent to the base grant.
• Single, “smooth” grant + base grant. Use the nonlinear “smooth” function as discussed above (Figure 9) to replace supplemental and concentration grants. Add an additional 9 percent to the base grant.

Figure 10 shows the per student funding change under these hypotheticals. Each option, though similar in cost (and hence the overall per student funding increase), brings different distributional increases. As discussed earlier, cost-of-living adjustments would on average favor higher-income and/or urban areas of the state, meaning much of the additional funding would go to more affluent districts. But the modest cost adjustment discussed earlier could be paired with a broader increase in the base—increasing purchasing power for districts in high-cost areas while still benefitting high-need students by roughly $160 per student more.

Conversely, funding based on enrollment instead of attendance would boost funding in districts with more high-need students; the state could use additional funding to enact this switch and boost the base grant by nearly 6 percent for all districts. Increasing supplemental and concentration grants benefits nearly all districts somewhat and would provide much larger funding increases for high-need and EL students.

Similarly, as higher-need districts have more students that fall into multiple high-need categories, providing additional funding for dual-need students—and increasing the weight—would largely benefit such districts. On average there is little difference by student group between using FRPM versus direct certification in a duplicated count, though this masks variation across districts in the impact of such a switch. Finally, replacing supplemental and concentration grants with a “smooth” function that gradually increases funding as the district share of high-need students rises, instead of the abrupt change seen in the current formula, would benefit districts with moderate shares of high-need students. With additional funding, this change could be enacted along with a base grant increase of 9 percent and would still benefit the average high-need and EL student by slightly more than non-high-need students.

Since the establishment of the LCFF, local flexibility and autonomy have increased, state education revenues have grown dramatically, and targeted funding has improved outcomes for high-need students. However, challenges remain. Achievement gaps for high-need students are persistent, and districts that receive less targeted funding are seeing increasing cost pressures. In this report, we consider modifications to the LCFF that could address some of these ongoing issues while maintaining much of the current structure and adhering to the reform’s stated objectives. The alternatives, however, come with their own complications: some districts could see higher or lower funding allocations, and the total costs could be substantial.

Increased per student funding due to future enrollment declines may offer policymakers the opportunity to modify the LCFF without reducing funding for any districts. Questions of how to weigh the needs of different students and districts in these funding allocations are complex, and the stakes of these design choices are significant. In this report, we do not advocate for any particular policy approach. Instead, our simulations highlight important trade-offs and bring concrete numbers to the questions facing policymakers as they consider updating the state’s funding formula. It is important to note that we do not capture major sources of education funding outside the LCFF, including federal grants, parcel taxes, and capital funding, among others.

In examining these potential changes to the LCFF, we estimate the effects on funding allocations across districts. We find that two of the adjustments we consider would direct more resources to higher-need districts. Switching to an enrollment-based system would result in funding gains for districts with the most chronically absent students, which also tend to have high shares of high-need students. Providing additional funding for students with overlapping needs (e.g., English Learners from low-income families) would also benefit most high-need districts, as these districts have more students in multiple targeted categories.

In contrast, a regional cost adjustment would slightly reduce funding for the average high-need student, providing a marginal benefit to urban and suburban districts while lowering per pupil funding substantially for rural and town districts. Finally, changing how the formula addresses district-level concentrations of need could direct more funding toward districts with moderate shares of high-need students, which are arguably underfunded in the current system. However, this approach would result in less relative funding for the lowest- and highest-need districts.

Despite the LCFF’s improvements to California’s school funding system, lagging test scores, rising achievement gaps, and stubbornly high chronic absenteeism suggest something more is needed. To some, this is evidence that the level of funding growth thus far has been inadequate, while others cite these facts as evidence that funding alone will not be enough to address the state’s challenges and that state allocations or district practices should be changed. Either way, ensuring that state dollars are allocated effectively, efficiently, and equitably is key to ensuring California’s students have the best chance at success.