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Two articles write 300 words for each article and name the article on top of summary 

300 words. Provide at least one specific detail or example in your summary.

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DOES SCHOOL FINANCE REFORM REDUCE

THE RACE GAP IN SCHOOL FUNDING?

Abstract
This paper offers new evidence on the impacts of school finance
reforms (SFRs) precipitated by school finance litigation, exploring
the extent to which the impact of SFR differs by district racial com-
position. Using difference-in-differences and event study models
with a series of district and year (or state-by-year) fixed effects,
and a sixteen-year panel of over 10,000 school districts, my anal-
yses exploit variation in funding across school districts, and tim-
ing of school finance court orders across states, to estimate the
effect of SFR on the distribution of district funding by racial com-
position. Models include relevant control variables available in
national data and results are robust to numerous alternative spec-
ifications, including estimating impacts on percent changes in re-
sources (in addition to levels), restricting analyses to districts in
SFR states, controlling for additional covariates available in only
some years and some states, and adding controls for state-specific
time trends. In addition, I estimate changes in New York State to
assess whether and to what extent results are sensitive to addi-
tional controls for revenue-raising capacity and district costs. Re-
sults suggest that SFR can work to alleviate racial funding gaps,
though impacts are moderate.

Michah W. Rothbart

Maxwell School of Citizenship

and Public Affairs

Syracuse University

Syracuse, NY 13244

mwrothba@syr.edu

https://doi.org/10.1162/edfp_a_00282

© 2019 Association for Education Finance and Policy

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Does SFR Reduce the Race Gap in Funding?

1 . I N T RO D U C T I O N
A persistent issue in U.S. public education is the enduring racial gap in academic per-
formance. One potential contributing factor to the achievement gap is a disparity in
resources. Historically, school district financial resources and share of students who
are black are negatively correlated, though this has narrowed in recent decades (Card
and Krueger 1996; Card and Rothstein 2007). School finance reform (SFR) may par-
tially explain narrowing resource disparities. Court-ordered SFR is a court ruling that
mandates a state to change its school funding system and provide fairer educational
opportunities to students across the state.1 Most often, SFRs explicitly work to break
the link between district wealth and school spending. By this definition, twenty states
had at least one SFR by 2010.2 SFR may also weaken the link between race and educa-
tion funding due to, for example, historical segregation that concentrates nonwhite stu-
dents in districts with low property wealth (Ryan 1999; Rothstein 2017). Alternatively,
SFR may not affect racial funding gaps, perhaps narrowing gaps in district funding be-
tween wealthy and poor districts without addressing gaps across racial groups. Thus,
the questions remain: To what extent do the impacts of SFR on district funding vary
by racial composition, and to what extent does SFR work to close racial funding gaps?
This study aims to answer these questions, providing estimates on the extent to which
the impacts of SFR are larger as district minority representation increases, and whether
local revenue responses offset increases in state aid.

Previous work finds that total district funding is correlated with race (Card and
Krueger 1996; Ryan 1999). Districts with greater white student compositions, on av-
erage, raise greater levels of local revenues than those with greater nonwhite compo-
sitions, perhaps because of fewer resource constraints (Card and Payne 2002; Baker,
Sciarra, and Farrie 2010). Moreover, others document gaps in local revenues due to his-
torical inequities in access to wealthier school districts (and higher property value com-
munities) precipitated by segregational housing and zoning policies, which still remain
insufficiently remedied today (Rothstein 2017). Perhaps surprisingly, then, the share of
students who are white is also positively correlated with levels of state aid—intended
to be an equalizer of resources—after controlling for revenue-raising capacity and dis-
trict costs (Stiefel et al. 2005; Chellman 2008). One explanation of this phenomenon
is that state funding formulas reflect implicit voting preferences, particularly if there is
a mismatch of racial composition between the voting-age population and school-aged
children (Poterba 1997; Ladd and Murray 2001; Figlio and Fletcher 2012).3

SFR provides a possible policy mechanism to close the race gap in school funding.
Previous research finds that SFR reduces the funding gap between wealthy and poor
districts (Murray, Evans, and Schwab 1998; Card and Payne 2002; Corcoran and Evans

1. Throughout this paper, SFR refers to court orders for reform, regardless of the timing of school finance legisla-
tion. That is, I use an inclusive definition of SFR, exploiting the first highest court order in each state, regardless
of whether and when actual changes in state funding mechanisms occur or if other cases are brought forward.

2. Students in these twenty states constitute 69 percent of the total U.S. elementary and secondary public student
population in 2010. In 2014, the South Carolina Supreme Court became the twenty-first state with a SFR ruling.
Other states have rulings focused on capital expenditures or on a specific class of students (for example, English
language learners). I follow Corcoran and Evans (2008, 2015) and do not include rulings focused on targeted
funds or populations as SFRs.

3. It is important to note that no state has a funding formula that enumerates race as a determinant of the levels
of state aid distributed.

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Michah W. Rothbart

2008, 2015; Jackson, Johnson, and Persico 2014, 2016). The same may hold for the race
gap in funding if, for example, the share of students who are nonwhite is positively
correlated with the share of students at-risk or in poverty. As another example, perhaps
historical racial inequalities are embedded in extant state funding formulas, and SFR
forces legislatures to revisit these laws, also decreasing racial funding gaps. In this pa-
per, I estimate the extent to which SFR impacts are larger as nonwhite share increases,
potentially alleviating racial funding gaps.

Using a sixteen-year panel spanning 1996–2011, my analyses exploit variation in
funding across school districts and timing of SFR court orders across states to esti-
mate the extent to which the size of SFR’s impact increases with share of students who
are nonwhite. Models include relevant control variables available in national data but,
due to data limitations, do not include controls for time-varying district wealth, such
as property tax base. Instead, I estimate difference-in-differences (or event study) spec-
ifications with a series of fixed effects to identify the impact of SFR, using interaction
terms between SFR and a vector of variables capturing racial composition. As a robust-
ness check, I conduct a detailed descriptive analysis of the changes in New York State
(NYS) school finance since its SFR, using a thirteen-year panel spanning 2000–2012.
This analysis includes measures of revenue-raising capacity and costs unavailable na-
tionally to assess model sensitivity.

For state aid, on average, I find that the effect of SFR is increasing in the shares
of students who are black, Hispanic, and American Indian.4 Conversely, SFR leads to
smaller increases in state aid as the share of students who are Asian increases. SFR
also has a smaller, offsetting effect on the relationship between race and local revenue;
SFR has smaller effects on local revenues as the shares of students who are black and
American Indian increases, and larger effects on local revenues as the shares who are
Asian increases. Effects on local revenues are small relative to effects on state aid. Thus,
the impact of SFR on total revenues is similar to state aid—it increases with shares of
students who are nonwhite, closing racial funding gaps.

Results are robust to a series of alternative specifications, including restricting the
sample to “ever SFR states” (those that have a SFR at any point before 2012), assessing
timing of impacts using an event study framework, controlling for state-specific time
trends, and estimating effects in NYS using additional control variables unavailable
nationally.5 In addition, a placebo test indicates that the impact estimates are a result of
SFR, not the threat of one. Changes following court rulings in favor of the status quo
(“uphold”) are small and insignificant.

The rest of the paper is organized as follows. I begin with an overview of the relevant
literature, followed by a description of data and measures used and an outline of the
empirical strategy. Results for national and NYS analyses, and conclusions follow.

4. The National Center for Education Statistics Common Core of Data Public Elementary/Secondary School Uni-
verse Survey identifies a category of students as “American Indian/Alaska Native.” While some prefer the
phrase “Native American,” I follow the National Center for Education Statistics category names in this paper.

5. In addition, all results shown in this paper show impacts as the level change in dollars per pupil, but results
are robust to assessing the impact on percent changes in resources (the natural logarithm of state aid, local
revenues, and total revenues). These results are available from the author upon request.

677

Does SFR Reduce the Race Gap in Funding?

2 . T H E L I N K S B E T W E E N S F R , D I S T R I C T R E S O U R C E S , A N D R A C E
Although research on equity and efficiency of educational state aid distribution is rich
(Bradbury et al. 1984; Downes and Pogue 1994; Duncombe and Yinger 1998; Odden
and Picus 2008; Picus, Goertz, and Odden 2008, 2015), few assess the role of race in
determining district resources. Those that do generally find that district racial composi-
tion matters (Stiefel et al. 2005; Chellman 2008; Baker and Green 2009). While Baker
and Green (2009) suggest race can play a role in SFR, few studies have examined the
extent to which the impacts of SFR differ by race. One exception, Sims (2011), finds
little relationship between the size of SFR impacts and nonwhite share, but does not
control for time-invariant district characteristics, statewide policy or economic changes
over time, or disentangle the effects on state aid from those on local revenues.

Determinants of School District Financial Resources

Schools districts are funded by three levels of government—federal, state, and local—
but historically, local and, specifically, local property taxes play the largest role. Thus, lo-
cal revenues are unequally distributed across districts (Baker, Sciarra, and Farrie 2010).
There is also a correlation between district racial composition and local revenues, per-
haps due to (or partially due to) historical discrimination limiting access of nonwhites to
districts with large property tax bases (Rothstein 2017). The share of revenue from state
aid, however, has increased over the past few decades—due in part to SFRs—leading
to more equal distributions of resources, at least between wealthy and poor districts
(Corcoran and Evans 2008, 2015).

State aid generally serves two key purposes: (1) mitigate differences in revenue-
raising capacity and (2) address differences in costs due to district characteristics and
students’ needs (Picus, Goertz, and Odden 2008, 2015). Revenue-raising capacity is
often measured by the size of the property-tax base, sometimes complemented by in-
come and wealth measures (Bradbury et al. 1984; Duncombe and Yinger 1998). Costs
are often measured as the share of students requiring additional educational supports
(e.g., with special education needs [SPED], English language learners [ELL], and eligi-
ble for free lunch [poor]). In addition, cost factors such as price of inputs (e.g., teacher
salaries) and district size are sometimes considered (Bradbury et al. 1984; Duncombe
and Yinger 1998).

Relationship Between District Funding and Racial Composition

Race may also be correlated with both costs and revenue-raising capacity (Stiefel et al.
2005; Chellman 2008; Baker and Green 2009; Rothstein 2017). For example, districts
with a higher share of nonwhite students may have fewer resources, even after con-
trolling for factors such as poverty. Why? There are at least four reasons. First, racial
composition might proxy for unmeasured variables that drive either costs or revenue-
raising capacity. For example, poverty measured as the share of students eligible for
free lunch misses the depth of poverty among those so designated “poor” (the very
poorest may cost more to educate) and the wealth among the “nonpoor” (if the families
of the wealthiest students increase revenue-raising capacity).6 State aid formulas may

6. As another example, SPED share ignores the level of accommodation students need or their likelihood for
certain diagnoses, both of which may be correlated with race.

678

Michah W. Rothbart

have a disparate racial impact due to poor measurement of costs and revenue-raising
capacity.

Second, the demand for public education spending may respond to racial compo-
sition, affecting funding through voter bias. Funding for public education is lower in
states, counties, and school districts with different racial compositions among the el-
derly and school-aged populations (Poterba 1997; Ladd and Murray 2001; Figlio and
Fletcher 2012). More specifically, funding is lower in places with predominantly white
elderly and predominantly black child populations than in places with high shares of
whites in both age groups.7

Third, racial composition itself may affect the costs of education. Districts with large
nonwhite populations may find teachers demand higher (compensating) wages, offer
less advantageous peer groups, and teacher training is not well-designed for efficient
instruction in these contexts. For example, Baker and Green (2009) find that districts
with high shares of black students offer lower peer-group and teacher quality, which
disadvantages these districts.

Fourth, historical, structural racism that isolates poor and minority groups in low
property wealth districts may limit the ability of districts with high minority concentra-
tions to raise local revenues. Policies, such as redlining, limiting access to borrowing,
and discriminatory housing policies, segregate minorities into districts with slower eco-
nomic and property value growth, which may reduce current revenue-raising capacity
(Rothstein 2017).

In spite of the above (or perhaps related to the first two factors), previous research
finds that nonwhite student share has a negative, significant, and independent effect
on the level of state aid provided to districts (not just on local revenues), despite the fact
that states do not explicitly account for race in their state aid funding formulas (Stiefel
et al. 2005; Chellman 2008). The relationship between race and total resources might
grow even stronger then, due to other inequalities outlined above, including costs of
education, access to districts with larger tax bases, immigration patterns, and effects of
structural segregation.

Court-Mandated School Finance Reform

Previous work finds SFR increases school spending overall and increases the share of
funding that comes from state aid as opposed to local revenues (Corcoran and Evans
2008, 2015).8 Further, when SFR increases state aid, increases are not fully offset by
reductions in local revenues. Instead, spending gaps narrow between wealthy and poor
(and high- and low-spending) districts, driven by spending increases in low-spending
districts rather than reductions in high-spending districts (Murray, Evans, and Schwab
1998; Card and Payne 2002; Corcoran and Evans 2008, 2015; Jackson, Johnson, and

7. This is consistent with findings for other public expenditures, which include income redistribution, roads,
libraries, and sanitation (Alesina, Baqir, and Easterly 1999; Luttmer 2001; Lind 2007).

8. The history of SFR is often described as having multiple waves, the first challenging aid formulas on equity
concerns and the second pursuing challenges based on adequacy concerns (Thro 1994; Verstegen 1998). Plain-
tiffs pursue equity cases on the principle that all students in a state should attend schools that receive similar
levels of educational funding. Plaintiffs pursue adequacy cases on the principle that all students in a state
should have access to a minimally acceptable level of education. Note that both equity and adequacy SFRs
are fundamentally about fairness in financing education. Most SFR cases since 1990 (a majority of the cases
providing identification in this paper) are based upon adequacy concerns.

679

Does SFR Reduce the Race Gap in Funding?

Persico 2014, 2016; Lafortune, Rothstein, and Schanzenbach 2018). These distribu-
tional effects appear to hold years after the SFR (Liscow 2018). Thus, SFR generates
higher, more adequate, or more equitable funding, or all three. Further, equalization of
state aid is greater in SFR states than those with rulings that uphold school finance for-
mulas, suggesting that the threat of a SFR lawsuit is likely insufficient to elicit funding
formula changes (Card and Payne 2002). Finally, some find SFR narrows achievement
gaps between wealthy and poor districts (Card and Payne 2002; Jackson, Johnson, and
Persico 2014, 2016; Johnson and Tanner 2018; Lafortune, Rothstein, and Schanzenbach
2018).

That said, few studies examine impacts of SFR on racial disparities. One notable ex-
ception, Sims (2011), finds little impact of SFR on the relationship between total district
revenues and racial composition. Unfortunately, Sims does not include district fixed
effects, so results may be biased by unobserved time-invariant characteristics. Further,
Sims does not differentiate between state, local, and federal revenue sources or between
the various nonwhite groups (i.e., black, Hispanic, Asian, and American Indian). Per-
haps most importantly, none of the previous studies control for potential common state-
year-specific shocks using state-by-year fixed effects. Thus, contemporaneous statewide
changes in resource constraints, nonwhite share, and non-SFR policies are all potential
confounders to previous estimates.

This paper builds on Sims (2011) and contributes to the literature by examin-
ing whether the impact of SFR on district funding (state aid, local revenue, and
total revenue) differs by racial composition. Previous SFR research uses difference-
in-differences models to exploit the staggered timing of SFR across states; this study
introduces state-by-year fixed effects and a set of interaction terms to estimate the extent
to which the SFR effect increases (or decreases) with nonwhite shares. Little previous
work differentiates between the effects of SFR court orders and the threat of litigation,
which I begin to disentangle by exploring the effects of court rulings that “uphold”
state funding formulas. In addition, following Lafortune, Rothstein, and Schanzenbach
(2018), I explore the effects of SFR over time using an event study framework.

3 . DATA A N D M E A S U R E S
Data and Measures for National Analysis

The study merges three key datasets: (1) district revenues and enrollment from the
United States Census Bureau’s Annual Survey of Local Government Finances File (F33
File); (2) student composition from the National Center for Education Statistics Com-
mon Core of Data Public Elementary/Secondary School Universe Survey Data (School
Universe Survey); and (3) a compiled, cumulative history of judicially-mandated SFRs
(Card and Payne 2002; Corcoran and Evans 2008, 2015; Education Law Center 2014;
SchoolFunding.Info 2016).

District funding is measured as per pupil state aid, local revenues, and total rev-
enues. State aid per pupil is total state aid to a district (including formula assistance,
special education, bilingual education, capital outlays, debt service, among others) di-
vided by district enrollment. Local revenue per pupil is total local revenues (including
property taxes, other taxes, fees, among others) divided by enrollment. Total revenue
per pupil captures all local financial resources including per pupil state, local, and

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Michah W. Rothbart

federal revenues. These variables reflect the size of school districts’ budgets and mixes
of revenue sources. I adjust all dollar figures for inflation and report them in 2011 dollars
using the Consumer Price Index.

District black, Hispanic, white, Asian, and American Indian student percentages
capture district racial composition and district poverty as the percentage of students
classified as eligible for free lunch (all aggregated from the School Universe Survey and
weighted by enrollment).9 District size is captured by district enrollment (in 1,000s).
Other measures of district cost factors, such as share of students who receive SPED
and ELL services, are unavailable nationwide in some years.10

As noted above, information on SFRs comes from a compiled, cumulative history of
judicially mandated SFRs.11 Cases strictly related to capital/facilities financing or strictly
procedural rulings (that remand a case to a lower court) are excluded. I use an inclusive
definition of SFR—the first court order from the highest court in each state—which
is conservative because a district is “treated” even if changes in funding do not occur
immediately (or at all) and even if other cases are brought forward at a later date. Table
A.1 provides a full list of school finance court cases used here. I construct two vectors of
variables to capture state SFR history. First, SFR is a binary variable that takes a value
of one if a state has (at any time previously) an SFR court order and zero otherwise.
Second, SFRYr is a vector of binary variables that reflect the number of years before
and after a state’s first SFR court order.

I limit the sample to unified school districts providing K–12 education to ensure
differences in the grades served by different districts do not bias my estimates.12 In
addition, I restrict the sample to districts with both financial and demographic data
and exclude districts in Hawaii and Washington, DC (since each has only one school
district). The sample includes 10,000–11,000 school districts per year over the 16-year
period between 1996 and 2011.13 The sample represents forty-nine states, over 80 per-
cent of districts, and over 90 percent of total enrollment.

Data and Measures for NYS Case Study and Robustness Check

Whereas national data lack consistent measures of revenue-raising capacity and costs,
such as property values, NYS provides numerous measures of both cost factors and
revenue-raising capacity over a thirteen-year period (2000–2012), including share of
ELL and SPED students, effective local property tax rate, and district wealth, allowing a
more nuanced analysis.

9. In some years and states additional racial categories are given, such as Pacific Islander. In these cases, I con-
figure the categories in the same manner as if all states use a five-category system. Share Pacific Islander, for
example, is added to the Asian share of students, as it would have been without the Pacific Islander designation.

10. SPED and ELL data are available in some states and in some years. I test the robustness of the main results to
inclusion of controls for SPED and ELL for academic years 1999–2011 in the districts for which these data are
available, finding consistent results displayed in table 5.

11. Sources include Card and Payne (2002); Corcoran and Evans (2008, 2015); Education Law Center (2014);
SchoolFunding.Info (2016).

12. There are approximately 16,000 school districts in the United States. These districts vary in terms of size and
grades served. Finding consistent measures of school district resources is difficult. Most importantly for this
study, costs of operating primary and secondary schools vary greatly and states support these levels of education
at disparate levels.

13. The sample for models that include SPED and ELL data include about 8,000–9,000 school districts per year
over a thirteen-year period between 1999 and 2011.

681

Does SFR Reduce the Race Gap in Funding?

Specifically, I use district revenue, enrollment, and demographic data on the almost
700 school districts for 2000–2012, merging district financial data from the NYS Edu-
cation Department’s (NYSED’s) Fiscal Analysis and Research Unit, and demographic
data from the NYSED’s Information and Reporting Services, to the national dataset.
The sample is a balanced panel of 672 districts that operate in all thirteen years.14

To be sure, NYS provides an attractive setting for this robustness check because it is
demographically diverse, has substantial variation in racial composition across districts
and over time, offers rich data on district costs and revenue-raising capacity, and is a
SFR state. Further, it offers a mix of rural, urban, and suburban districts. As of 2011,
NYS was the third largest state in the United States in terms of total population and
public school student population.

Measures of per pupil state aid, local and total revenue, race, and poverty are the
same as the national analyses.15 Additional control variables fall into two major cate-
gories: (1) district revenue-raising capacity and (2) district costs. Measures of district
revenue-raising capacity include district combined wealth ratio (CWR), the effective lo-
cal tax rate, and share in poverty.16 CWR is an index used by NYSED in their funding
formulas that includes taxable real property value and adjusted gross income per pupil
as measured against the state average, providing a good measure of district fiscal capac-
ity.17 Effective local tax rate measures the extent to which a district is already exhausting
local taxable resources. District poverty is the same as in the national data. Measures of
district costs include student attendance rate, enrollment (same as national data), and
share of students who receive SPED and ELL services.

4 . M O D E L A N D E M P I R I C A L S T R AT E G Y
The central questions of this study are simple: To what extent do the impacts of SFR
on state aid increase as minority representation increases, and are changes in state aid
offset by local revenue responses? I answer these exploiting the staggered timing of
SFR across states using a modified difference-in-differences model (or an event study
framework) with district and state-by-year fixed effects and controls for time-varying
district characteristics.

A standard difference-in-differences model (used in previous research) provides es-
timates of SFR’s impacts by comparing the average change among districts in SFR
states to those in states without a SFR. I apply this model to replicate previous work on
the main effect of SFR, using more recent data (Murray, Evans, and Schwab 1998; Card
and Payne 2002; Corcoran and Evans 2008, 2015). I then add a vector of interactions be-
tween SFR and district racial composition to estimate the impacts of SFR by nonwhite
share. In preferred models, which identify impacts by race, I include district fixed ef-
fects and state-by-year fixed effects, relaxing some of the key identification assumptions
of traditional difference-in-differences models. In particular, preferred model estimates

14. The universe includes 680 districts operating in at least one year.
15. Shares of students who are American Indian and multi-racial are small in NYS districts (less than 1 percent

of student population in 2012). For results presented, these groups are included in the share of students who
are “Asian.” Results are robust to grouping multi-racial and American Indian with share who are black and
Hispanic as well.

16. Note that the percentage of students who are certified eligible for free or reduced-price lunch can also be
characterized as a district cost factor because children from low income households cost more to educate.

17. New York State Education Department (2012).

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Michah W. Rothbart

are robust to time-invariant differences across districts, differences between states in
every year, and differences across states that may be correlated with SFR timing.18

Difference-in-Differences Analyses

My central model is as follows:

Revist = β0 + NW′ist β1 + β2SFRst + SFR ∗ NW′ist β3 + β4Povist + Enroll′ist β5
+ γis + δt + εit , (1)

where Revist is district funding (per pupil state aid, and later, per pupil local or total rev-
enue) in district i in state s in time t; NW is a vector reflecting district i’s nonwhite racial
composition (percentage Black, Hispanic, Asian, and American Indian); SFR takes a
value of one if state s has a SFR by year t and zero otherwise; Povist controls for per-
centage of students certified eligible for free meals and Enrollist controls for possible
economies of scale (enrollment divided by 1,000 for scaling purposes, and its square);19

γ is and δt are district and year fixed effects, respectively; ϵ is an error term with the usual
properties. Model 1 and all subsequent regressions are weighted by district enrollment
using analytic weights (with robust standard errors clustered by district to address het-
eroscedasticity).20 I capture common macroeconomic factors with year fixed effects.21

In preferred …

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