Does the Measurement Matter? Assessing Alternate Approaches to Measuring State School Finance Equity for California’s Local Control Funding Formula
*Names in bold indicate Presenter
Extant research suggests California's school finance system became slightly more equitable following the passage of LCFF, although estimates of the extent to which equity increased vary across studies. Part of the discrepancy stems from researchers’ use of different datasets and different measures of equity. Some analyses use regression-based estimates of the relationship between district poverty rate and funding level. A second commonly used approach involves estimating the weighted average funding level for low-income and non-low-income students. Meanwhile, state and federal policymakers have preferences for their own data systems when reading reports and assessing progress. Extant literature does not offer a clear understanding of how alternate datasets and measures of equity may influence the lessons learned from major school finance reforms. Policymakers therefore may draw different conclusions based on analyses using different datasets or equity measures.
The purpose of this study is to (a) unpack how state and federal school finance datasets align; (b) understand the extent to which alternate measures of equity lead to similar or different conclusions about school finance reforms; and (c) estimate the extent to which school finance equity in California changed following the implementation of LCFF, and determine how these estimates change under different measures and data sources.
We construct parallel district-level panel datasets using data from the California Department of Education and the U.S. Census. We estimate changes over time in district-level school finance equity under California’s LCFF using each method described above, with each of our two datasets. Our results show that different datasets and methods yield similar results in general; however, we find several policy-relevant differences across analytic methods and data sources. CDE data may overstate funding for high-poverty districts, causing the system to appear more equitable compared to results based on U.S. Census data. Similarly, using average daily attendance, rather than fall enrollment, to measure per-student funding increases the estimated level of equity since high-poverty districts tend to have lower attendance rates. Regression-based approaches are more sensitive to funding changes over time, and may be more influenced by outliers, compared to weighted average approaches. Census poverty rates are also more sensitive over time compared to district-level income measures based on the percent of students eligible for free or reduced-price lunch. Based on these results, we argue for using U.S. Census data and census poverty rates; however, the specific data sources and methods for a given study may depend on study-specific goals and objectives. We discuss the implications for policy and future research.