Updating California's School Funding Formula: Assessing Alternatives and Trade-Offs

• In Event: Beyond “Money Matters”: How Policy Design Shapes the Distribution and Effectiveness of School Spending

Abstract

Educational outcome gaps persist across the U.S. despite decades of policy intervention. School finance formulas, the metrics and weights that determine how resources are allocated, are a key approach to addressing these gaps. Even seemingly minor formula adjustments can redistribute billions of dollars.

A growing body of research has estimated the effects of past school finance reforms on student outcomes, but a complementary challenge remains: policymakers considering future reforms face substantial uncertainty about how specific formula mechanisms will affect funding and, thus, student outcomes. This paper addresses that gap directly, offering analyses of potential changes to California's Local Control Funding Formula (LCFF) built with an approach flexible enough to consider a wide array of possibilities.

We examine the tradeoffs in California's current approach to targeting student need, and whether modifications to base grant mechanisms, weighting schemes, poverty definitions, or concentration weights could direct resources more effectively and efficiently. We draw on district-level financial, demographic, and outcome data from the California Department of Education (2023–24), supplemented with data from the NCES and ACS on urbanicity, regional wages, educational attainment and more. We construct a comprehensive school finance simulator that replicates California's LCFF allocation and models policy alternatives across several dimensions including how students are counted, how need is defined, and how funds are targeted to high-need students. Simulations are estimated under three scenarios: zero-sum reallocations that require no new state spending, variable funding allocations that allow for additional investment, and hold-harmless constraints that ensure no district loses funding.

Our estimates reveal several important trade-offs in how additional resources would be distributed across districts. Switching to enrollment-based funding would direct relatively more resources toward higher-need districts, though districts with high attendance rates would see smaller gains; cost-of-living adjustments would favor more affluent districts in relative terms; and funding students with overlapping need categories would further concentrate additional resources in the highest-need districts. Importantly, these distributional effects vary considerably even among districts that look similar on standard measures. Districts with comparable shares of high-need students can differ substantially in how many of those students fall into multiple need categories, meaning a shift toward funding overlapping need would affect ostensibly similar districts quite differently. Our simulator's district-level granularity surfaces distributional dynamics that aggregate state-level analysis would obscure.

As researchers develop more nuanced estimates of how past reforms affected outcomes, our simulator complements that work by providing a flexible tool for evaluating a wide range of potential formula changes—individually or in combination. Rather than analyzing a single policy proposal, we can map the distributional consequences across many alternatives, helping policymakers understand not just what a specific reform would do, but how the broader landscape of options compares under real fiscal and political constraints. As California weighs the next generation of funding reforms, this kind of granular, flexible analysis can help translate research evidence into better-informed policy decisions.

Author

• Iwunze Ugo, Public Policy Institute of California
  Presenting Author