Since 1989, 27 states have passed one or multiple school finance reforms (SFRs), which are typically defined in the literature as court orders or legislative statutes that mandate major redesigns of a state's school funding formula. In most cases, SFRs increase the overall level of state spending on public schools, as well as target larger spending increases to districts serving economically disadvantaged students. As many state governments have significantly increased their contributions to K-12 education as a result of SFRs, questions arise regarding how state legislatures pay for such increases in education funding. On the one hand, because of state balanced budget requirements, it is feasible that legislators could be compelled to reduce noneducation state expenditures in order to fund additional school spending. On the other hand, legislators could choose to fund SFR-induced increases in educational expenditures by increasing revenues through raising state income or sales taxes. A handful of studies have examined the effects of SFRs on state non-education expenditures using longitudinal state finance data and event study designs (Baicker & Gordon, 2006; Lafortune et al., 2018; Liscow, 2018; Murray et al., 1998), with Liscow (2018) also examining the effects of SFRs on state revenues. While both Murray et al. (1998) and Lafortune et al. (2018) found no evidence that SFRs lead to decreased non-education state expenditure, Baicker and Gordon (2006) found that every dollar increase in state intergovernmental education funding received by counties was offset by a 22-cent reduction in state intergovernmental funding of welfare, health, and transportation. In contrast to Baicker and Gordon's (2006) findings, Liscow (2018) found no effect of SFRs on state non-education expenditures and that increases in education funding were financed by tax increases (Liscow, 2018). In addition to the results of prior studies being inconclusive, there are also a few limitations to the extant literature in general. Namely, prior studies have only examined the average effect of SFRs on state revenues and expenditures and thus overlook possible variation in effects across states. Based on variations in state policy, political, and demographic contexts, it is feasible that SFR-induced school spending increases in some states could be funded by decreasing non-education expenditures, while other states fund increases in education spending by raising taxes. Understanding state-specific heterogeneity is crucial to providing a more complete picture of how increases in state education spending are funded and could potentially reconcile the conflicting results of prior studies. Alleviating some of the unknowns regarding funding SFRs could also induce more states to increase school spending through SFRs in the future. Another limitation of the extant literature is that the results of prior studies, which were estimated using event study designs, rely on the assumption that there were no other events or policy changes that occurred at the same time as an SFR that could be responsible for changes in state revenues or expenditures. Thus, for example, the validity of the finding by Baiker and Gordon (2006) that SFR-induced spending increases in education were funded by decreasing welfare, health, and transportation expenditures is reliant on the assumption that no other policy changes that influenced these expenditures were passed in the same year as an SFR. This is a strong assumption to make, given that a multitude of events and policy changes related to various state budget categories occur every fiscal year. For example, state welfare reforms were prevalent throughout the 1990's, which is the same time period that many states experienced SFRs. To help address these gaps in the literature, this study examines the following. First, I use U.S. Census Bureau state-level finance data to determine which of the 28 states that experienced an SFR between calendar years 1989 and 2011 increased education expenditures following an SFR. To identify state-specific effect estimates, I will use the ridge augmented synthetic control method [ridge ASCM] (Ben-Michael et al., 2020), which allows me to construct a unique comparison group (i.e. "synthetic control") for each treated state that has undergone an SFR. The synthetic control method (SCM) in general is very similar to the differences-in-differences (DID) or event study quasi-experimental designs, which compare the change in outcomes over time of a treated group to the change in outcomes over time of a weighted comparison group. However, SCM offers a more systematic way to assign weights to the comparison group than DID, which can result in the comparison group more closely resembling the treatment group and thus serving as a better counterfactual (Abadie et al., 2010). For the states that experience a positive average effect of SFRs on education spending, I will then estimate, separately by state, the effects of SFRs on various categories of state revenues and expenditures using ridge ASCM. Measures of state revenues include general revenues, tax revenues, income tax revenues, and sales tax revenues. Expenditure categories include higher education expenditures, health and hospital expenditures, welfare expenditures, and highway expenditures. Finding an effect of an SFR on tax revenues or a non-K12 education expenditure category would suggest that changes to that budget category helped to fund increases in education spending. In order to confirm this, I will also conduct an archival search through legislative documents to find evidence that links a certain budget change to an SFR directly or increased education revenues in general. The idea is that if a state does, for example, raise taxes following an SFR in order to fund increased education spending, then there should not only be evidence of increased tax revenues in a state's budget data, but also evidence via a legislative act that mandates such tax increases for the purposes of increasing education funding. Although providing legislative evidence does not negate the possibility that another event could be motivating the changes observed in state revenues or non-K12 expenditures, it does serve as an additional validity check of the ridge ASCM findings and increase confidence in my results.