EPAAv18n9


 

education policy 
analysis archives 
A peer-reviewed, independent,  
open access, multilingual journal  
 
 
 
 
 

 Arizona State University 
 

Volume 18  Number 9                April 20,  2010  ISSN 1068-2341 
 

 
Premature celebrations:  

The persistence of inter-district funding disparities 
 

Bruce D. Baker 
Rutgers, The State University of New Jersey 

Kevin G. Welner  
University of Colorado at Boulder 

 
 

Citation: Baker, B. D., & Welner, K. G. (2010). “Premature celebrations: The persistence of inter-
district funding disparities” Educational Policy Analysis Archives, 18(9). Retrieved [date] from 
http://epaa.asu.edu/ojs/article/view/718 
 
Abstract: Two interlocking claims are being increasingly made around school finance: that states 
have largely met their obligations to resolve disparities between local public school districts and that 
the bulk of remaining disparities are those that persist within school districts. These local decisions 
are described as irrational and unfair school district practices in the allocation of resources between 
individual schools. In this article, we accept the basic contention of within-district inequities. But we 
offer a critique of the empirical basis for the claims that within-district gaps are the dominant form 
of persistent disparities in school finance, finding instead that claims to this effect are largely based 
on one or a handful of deeply flawed analyses. Next, we present an empirical analysis, using national 
data, of 16-year trends (1990 to 2005) and recent patterns (2005 to 2007) of between-district 
disparities, finding that state efforts to resolve between-district disparities are generally incomplete 
and inadequate and that in some states between-district disparities have actually increased over time. 
Keywords: finance; equity; between-district disparities; within-district disparities  
 
 
 

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E ducation Policy A nalysis A rchives V ol. 18 N o. 9 2 
 
 
Celebraciones prematuras: La persistencia de las disparidades de financiamiento entre los 
distritos 
Resumen: Dos perspectivas interrelacionadas en torno a la financiación de las escuelas son 
escuchadas cada vez más frecuentemente:  que los estados han cumplido en gran medida sus 
obligaciones para resolver las disparidades  entre los distritos escolares locales y que la mayor parte 
de las diferencias que restan son las que persisten dentro de los distritos. Las decisiones locales de 
asignación de recursos entre escuelas de un distrito se describen como prácticas irracionales e 
injustas. En este artículo, aceptamos la tesis fundamental de las desigualdades dentro de los  distritos. 
pero ofrecemos una crítica al sustento empírico de la afirmación que las brechas dentro de los 
distritos son la forma dominante de las disparidades que persisten en el financiamiento de las 
escuelas ya que esas afirmaciones se basan principalmente en un  pequeño grupo de estudios con 
serios problemas. A continuación, presentamos un análisis empírico,  utilizando datos nacionales, 
tendencias registradas a lo largo de 16-años (1990 a 2005) y patrones más recientes  (2005 a 2007) 
sobre las disparidades dentro de los distritos. Concluimos que los esfuerzos de los estados  para 
resolver esas disparidades entre los distritos son generalmente incompletos e insuficientes y que en 
algunos estados, las disparidades entre los distritos  aumentaron. 
Palabras-clave: financiaciamiento; equidad; disparidades entre distritos; disparidades dentro 
distritos. 
 
Celebrações precoces: A persistência das disparidades de financiamento inter-distritales 
Resumo: Duas perspectivas inter-relacionadas sobre o financiamento das escolas são  
ouvidas freqüentemente: que os estados têm amplamente cumpridos  
suas obrigações para compensar as disparidades entre os distritos escolares locais  
e que a maioria das diferenças que permanecem são aqueles que persistem dentro dos  distritos 
escolares. Estas decisões locais de alocação de recursos entre as escolas  de um distrito são descritas 
como práticas irracionais e injustas. Neste artigo,  aceitamos a tese fundamental das disparidades 
dentro dos distritos. Mas oferecemos uma crítica ao suporte empírico para a afirmação de que as 
brechas dos distritos são a forma dominante das disparidades persistentes no financiamento das 
escolas já que essas analises estão baseadas principalmente em um pequeno grupo de estudos com 
graves deficiências. Apresentamos um  análise empírica usando dados nacionais, tendências 
registradas durante 16-anos (1990 a 2005) e os padrões recentes (2005 a 2007) sobre as disparidades 
dentro dos distritos e concluímos que os esforços dos estados para resolver estes  disparidades entre 
os distritos são geralmente incompletos e inadequados e em  alguns estados, as disparidades entre os 
distritos aumentou. 
Palabras-chave: finançiamento; eqüidade; disparidades entre-distritos; isparidades dentro dos  
distritos escolares. 
 

Introduction 

An increasing volume of rhetoric around school finance rests on claims that states have 
largely met their obligations to resolve disparities between local public school districts. This premise 
is then extended to the contention that the bulk of remaining disparities are those that persist within 
school districts, due to irrational and unfair school district resource allocation practices between 
individual schools (see, for example, McClure, Wiener, Roza, and Hill, 2008; Public Impact, et al., 
2008). In short, since states have done their job to promote equity and adequacy of school funding, 
school district officials must now meet their corresponding obligations. This argument is also often 



Premature celebrations          3 
 

attached to the remedy of weighted student funding (see Roza, 2006, pointing readers to the 
Fordham Institute’s “Fund the Child” campaign). 

Notably, no leading researchers in economics and school finance have joined this 
overwhelming shift in emphasis away from state-level concerns. Many have opted instead for a 
broad description of the funding problem that encompasses both within-district and between-
district resource disparities (see, e.g., Bifulco, 2005; Burke, 1999; Duncombe and Johnston, 2004; 
Downes, 2004; Imazeki and Reschovsky, 2004; Stiefel, Rubenstein & Berne, 1998; Rubenstein et al., 
2007). Nonetheless, arguments favoring a devolution in focus from states to school districts have 
gained significant traction in policy debates, and they have the rhetorical advantage of providing 
state policymakers with an enticing, revenue-neutral policy solution (see Public Impact, et al., 2008). 
If states have done their job, no more money is needed, nor must these policymakers consider 
painful movement of limited funding away from wealthier districts. Rather, districts must simply 
reshuffle what they have, in order to achieve optimal distribution. But, as discussed below, the 
increase in popularity of these political arguments is backed by little or no empirical evidence for the 
premise that states have already met their end of the bargain (Baker, 2007).1 

This article explores the extent that states have actually resolved substantial disparities in 
available resources between local public school districts. Although we review recent literature on 
within-district disparities and their relation to between-district disparities, we do not separately 
estimate the relative disparities within and between districts. Rather, we remain focused on the single 
question of whether and to what extent states have substantially resolved past between-district 
disparities such that it really is time to check that box and move on to other policy concerns. In 
short, this article begins with a critique of the within-district comparative claims and then examines 
between-district disparities in state and local revenues per pupil across all states—over the 16-year 
period from 1990 to 2005 across all states, and with more fine-grained detail for the years 2005 to 
2007. 

 
Recent Evidence on Between-district Disparities and State School Finance 

Policies 
 

In the late 1990’s through early 2000’s a handful of rigorous studies evaluated changes to the 
distribution of school funding within and across states. Related studies examined those changes in 
connection with the existence of judicial mandates for reform in a given state, ultimately looking at 
the relationship between funding changes and student outcomes. Notable examples include Murray, 
Evans and Schwab’s (1998) evaluation of the effect of judicial mandates on the level and distribution 
of funding and Card and Payne’s (2002) evaluation of the link between judicial mandates, school 
finance reforms, changes in the level and distribution of resources, and student outcomes. These 
teams of authors found that state school finance lawsuits—specifically high court rulings in favor of 

                                                
1 A secondary current rhetorical argument against increased funding, or school finance reform more generally, 
rests on the related contention that no substantial improvements to student outcomes or closure of 
achievement gaps have followed from a purported dramatic increase in state-level funding and improved 
funding equity over time. Moreover, the argument continues, given the absence of outcome improvements, 
the asserted major increases to funding have proven ineffective and the fault lies with the policies of local 
districts or others, rather than with the state funding formulas. This argument as well hinges on whether there 
really have been substantive and sustained improvements across states in school funding equity and adequacy, 
and whether school finance reforms have improved student outcomes or reduce achievement gaps. The 
contention that there have not been improvements is woven throughout the recent work of Hanushek and 
Lindseth (2009) and Hill, Roza and Harvey (2008). 



E ducation Policy A nalysis A rchives V ol. 18 N o. 9 4 
 
plaintiffs—tend to lead to positive changes in the level and distribution of funding. Card and Payne 
found as well that those changes lead to improvements to the distribution of outcomes. Other 
recent studies on improvements to equity or adequacy of funding over time have focused 
longitudinally on specific states and reached similar conclusions (see, for example, Downes, Zabel & 
Ansel, 2009 [Massachusetts]: Downes, 2004 [Vermont]; Deke, 2003 [Kansas]). 

In addition, a handful of relatively recent publications have described disparities—absolute 
as well as changes—in school funding by race and/or poverty across states. For example, Bifulco 
(2005) conducted a longitudinal analysis of racial disparities in school funding, looking at data from 
the late 1980s through 2002 across all states. He found that in 2002 the average black student’s 
funding was approximately 8.5% higher than the average white student’s funding, with no 
adjustments applied. However, when resources are adjusted for well-documented factors affecting 
the costs of producing comparable student outcomes (including student need factors such as 
poverty and limited English language proficiency), as well as economies of scale and regional labor 
market variation, he found that the average black student’s district had from 3.2% to 15.8% less 
funding than the average white student’s district. 

Similarly, intermittent reporting by the Education Trust has noted persistent funding gaps 
between school districts with high and low poverty rates and high and low enrollment 
concentrations of students of color.2 These reports show that some states have relatively small gaps, 
while others provide greater amounts of funding to higher poverty, higher minority districts. But the 
reports also identify states that provide much lower levels of funding to higher poverty and higher 
minority districts. On net, Education Trust finds that higher poverty and higher minority 
concentration school districts continue to receive relatively less funding from their states. 

Taken together, these findings suggest that states have not yet fully met their end of the 
bargain and that significant work remains to be done at the state policy level before turning attention 
entirely to within-district resource disparities. That said, the Bifulco analysis leaves off eight years 
ago, and the Education Trust analysis of funding gaps has methodological and data limitations that 
prevent readers from making accurate judgments in this regard.3 It also does not address progress—
or lack thereof—over time. 

 
Recent Evidence on Within-district Disparities 

 
The examination of within-district resource allocation in public education is not new. Studies 

conducted in the 1990s found significant disparities in resources between schools within the same 
district. In the mid- to late-1980’s, major litigation was pursued (and settled) in Los Angeles 
concerning that district’s intra-district funding disparities (see discussion of Rodriguez v. Los Angeles 
Unified School District in Roos, 1998). Going back even further, a key issue in the legal challenges to 

                                                
2 Education Trust has in the past prepared an annual report titled the “Funding Gap.” The 2006 Funding 
Gap report appears here: 
http://www.edtrust.org/sites/edtrust.org/files/publications/files/FundingGap2006.pdf. The report was 
produced in 2003, 2005, 2006 and in 2008, but the 2008 report was retracted due to data errors 
(http://www.edtrust.org/dc/publication/the-funding-gap-0) 
3 One notable shortcoming of the Education Trust analysis is that it looks only at districts clustered in the 
ends of each state’s distribution—high and low poverty or high and low minority—and thus fails to evaluate 
whether the funding gaps indentified are part of a pattern across all districts by poverty. In addition, the 
Education Trust report relies on relatively arbitrary a priori assumptions of the additional “costs” associated 
with children in poverty (assuming a 40% additional cost, used as a “weight” for adjusting state and local 
revenues in their analysis). 



Premature celebrations          5 
 

separate-but-equal policies leading up to Brown v. Board of Education (1954) concerned the fact that 
districts’ separate schools for blacks were resourced at levels below those for whites. 

An issue raised by the Los Angeles Unified School District Rodriguez litigation is the degree 
to which intra-district funding disparities are linked to teacher seniority and transfer policies. 
Because teachers with more experience tend to transfer to schools serving fewer low-income 
students of color, and because those teachers are paid more than beginning teachers (Lankford, 
Loeb, & Wyckoff, 2002), the salary resources devoted to more advantaged schools tend to be higher. 
While this can be disregarded as not really about district allocation of funding, we should keep in 
mind that teacher experience is associated with teacher quality—at least in the early career years—
and that teacher quality is generally regarded as the most important resource a school can provide to 
its students (see Clotfelter, Ladd & Vigdor, 2005; Rivken, Hanushek & Kain, 2005). Nothing in the 
analysis that follows should be understood as suggesting that this is not an important issue; our 
intent is merely to question the associated argument that disparities between districts are no longer 
of major importance. 

Research over the past fifteen years has started to tease out the nature of intra-district 
spending patterns. These studies are largely limited to a few states or individual districts where 
school-site expenditure data have been available, including California, Ohio and Texas, as well as 
select cities in New York State and the city of Chicago. In one early study, for instance, Hertert 
(1995) simultaneously evaluated variations in California between-school spending both across 
districts and within districts, using data from 1990-91. She found generally that between-school 
disparities across districts were greater than the average district-level spending disparities across 
districts. Moreover, in some cases between-school disparities within districts were greater than 
between-school disparities across districts. Hertert accounted for district size and district type to 
correct for some explainable differences in spending across districts, and she found that accounting 
for school type did mitigate (but did not completely explain) between-school differences. In another 
such study, Burke (1999) estimated resource distributions at the school level rather than the district 
level—revealing significant intra-district disparities that in some states (Illinois and New York) 
exceeded inter-district disparities.4 Burke’s and Hertert’s studies represent the most direct test of the 
relative magnitude of within- and between-district resource variations, but both overlook a number 
of factors addressed in more recent studies, including differences in costs from one district to the 
next and one school to the next (see Baker, 2009). 

A related body of relevant research looks in isolation at within-district spending (that is, it 
does not compare these spending patterns to between-district patterns). Steifel, Rubenstein and 
Berne (1998) analyzed school-level data from four large urban districts (Chicago, Fort Worth, New 
York City and Rochester) in an effort to measure within-district disparities in resources. They asked 
two basic questions. First, how much variation exists across school-level budgets within the districts? 
Second, to what extent is that variation associated with factors that may affect the costs of providing 
equal educational opportunity across those schools—most notably, rates of children in poverty?5 
Like Hertert (1995) and Burke (1999), they found significant variation in resources across schools 
within districts. But they also looked more closely and found that some of that variation was 

                                                
4 This result may relate to the size of Chicago and New York City within their state systems. However, the 
article’s presentation of data and analyses are insufficiently transparent to allow for this determination. 
5 In related work, Schwartz and Stiefel (2004) explore the relationship between within-district disparities and 
immigrant populations in New York City, finding “that some resources increase over time when there are 
large increases in the percentage of immigrants in a school” (p. 303). Work by Stiefel and colleagues at New 
York University, primarily focused on within-district resource variations in New York City, dates back to the 
period of early interest in within-district resource allocation (see Berne and Stiefel, 1994). 



E ducation Policy A nalysis A rchives V ol. 18 N o. 9 6 
 
positively associated with poverty rates across schools.6 

Texas’ within-district patterns appear to also show such variation. Ajwad (2006) used data on 
Texas school-level expenditures for elementary schools to evaluate whether districts have targeted 
greater resources toward schools in higher-poverty neighborhoods. Using fixed effects expenditure 
regressions and neighborhood resident population characteristics rather than school enrollments, he 
shows that Texas school districts, on average, target additional resources toward elementary schools 
in higher-poverty neighborhoods. Ajwad also finds, however, that the average dollar differences in 
targeted funding are relatively small. 

Baker (2009) also focuses on within-district disparities, but—adding an important contextual 
element to the approaches used in above-discussed studies—he applies school-level cost function 
modeling7 to estimate adjustments for costs, and he places disparities in the context of other districts 
sharing the same labor market. He finds that “in some cases, resource levels in urban core 
elementary schools are relatively insufficient for competing with schools in neighboring districts to 
achieve comparable outcomes” (p. 1). That is, between-district disparities may constrain the ability 
of some districts to resolve within-district disparities, at least for the Ohio and Texas school districts 
he examined. 

But the most prominent work in this area has been conducted by Marguerite Roza and her 
colleagues, and those studies therefore deserve a close look. As with several of the above-discussed 
analyses, Roza, Guin, Gross and Deburgomaster (2007) use Texas school-level expenditure data. 
They examine changes in internal resource allocation from 1994 to 2003. Rather than estimating the 
statistical relationship between school-level expenditures and cost factors—as did Ajwad (2006), 
Baker (2009), and Rubenstein, Schwartz, Stiefel and Bel Hadj Amor (2007)—Roza and her 
colleagues adopt an approach that involves calculating a Weighted Student Index (WSI) to track 
equity levels and changes over time. A significant shortcoming of the WSI approach, however, is 
that it fails to measure differences in resources with respect to student population variation across 
schools. It instead measures whether a child in poverty in one school receives the same level of 
resources as a child in poverty in another school (even if that level is $0, or 0% more than the non-
poor child).8 The extent to which resources are targeted on the basis of poverty or other costs and 

                                                
6 Interestingly, this finding was not systematic across settings or school types (e.g., Rochester middle schools 
showed stronger positive relationships between poverty and resources than Rochester elementary or high 
schools). 
7 Cost modeling is commonly used to determine the additional costs of achieving constant outcome targets. It 
has been used primarily in district-level analyses like those conducted by Imazeki and Reschovsky (2004) and 
Duncombe and Johnston (2004). The approach involves estimation of econometric models (regression 
equations) to determine specifically the additional costs associated with student poverty, competitive wages, 
economies of scale and other uncontrollable factors.  
8 Roza and her colleagues also test whether variations in their WSI are a function of four different factors: (a) 
school grade level, (b) percent white in the school, (c) teacher experience, and (d) the academic rank of the 
school in the state. The authors suggest that this analysis is undertaken with the goal of determining whether 
observed resource variation (as measured by the WSI) is a function of “intentional” and “unintentional” 
factors. It is difficult to interpret, however, how this ad hoc mix of outcome measures, organizational features 
and racial composition relates to more common sets of “cost” factors, or factors outside the control of local 
school officials that influence the costs of achieving any given level of outcomes (see Duncombe and Yinger, 
2008). The dependent variable (WSI) measures resource variation in terms of differences across schools 
between student subgroups, rather than aggregate resource differences across schools with respect to 
population differences across schools. A more straightforward interpretation (at least with respect to whether 
resource variation is a function of uncontrollable cost factors) would be possible from an analysis that used 



Premature celebrations          7 
 

needs is not addressed or accounted for in the study. That is, if every school serving low-income 
students were funded regressively, this would not be addressed; but if some schools serving low-
income students were regressive and some were progressive, the study would highlight that 
difference. Those questions are certainly of some import, but they do not get at core equity 
concerns. 

Roza, Guin, Gross and Deburgomaster (2007) begin with a quick comparison of within-
district and between-district disparities in Texas and then jump quickly to their case that “At least in 
Texas, funding decisions within districts currently have a greater impact on a school’s resources than 
inequalities in access to resources across school districts” (p. 70). The first section of their article 
reports that variations in spending between Texas districts tend to fall within 5% to 10% of mean 
spending statewide, compared to disparities within large Texas districts (again, disparities in funding 
of similar students at different schools, as explained above) that tend to be on the order of 15%. 
Unfortunately, documentation is largely absent concerning the method for calculating the 
coefficients of variation presented by the authors.9 

There is a bit of a tautology at work here, slightly under the surface. If a researcher were to 
use the pupil weights and cost factors adopted in the Texas funding formula itself, or weights 
derived from the implementation of that formula (as is the case with the WSI approach used by 
Roza and her colleagues)10 as a basis for accounting for need and cost variation, the result would 
almost by definition show little disparity between districts funded under that formula. But such an 
analysis would be based on a deeply flawed assumption—that the state school finance formula itself 
represents true costs. Categorical weights and other distributional criteria included in state formulas 
tend to be the result of political wrangling as much as they are reflections of empirical 
determinations of need. In reality, what an analysis like Roza’s effectively shows is only that the state 
school finance formula does what the state school finance formula does. It is not surprising, then, 
that others such as Reschovsky and Imazki (2004, showing about a 25% coefficient of variation) 
report much greater between-district disparities in Texas, both in raw unadjusted spending and in 
spending adjusted for need and cost.11 

Interestingly, Roza, Guin, Gross and Deburgomaster’s use of formula-induced spending 
variation as a proxy for cost and need variation conflicts with the concurrent work of Roza and 
Guin themselves. In What is the Sum of the Parts?, also released in 2007, Roza, Guin and Davis 
calculate ‘implicit weights’ of district spending on specific populations of students for multiple 

                                                                                                                                                       
per pupil expenditures as the dependent variable and identified standard cost factors as independent variables 
in an expenditure function framework. 
9 The coefficient of variation, or CV, is the standard deviation divided by the mean, or standard deviation 
expressed as a percent of the mean. 
10 Roza, Guin, Gross and Deburgomaster (2007) explain that the WSI is “a ratio … of the actual funding 
received by each school to the funding we would expect if schools received the district’s average allocation 
for its particular mix of students” (p. 78). This calculation is made without judgment regarding whether the 
estimated average differences relate either logically or empirically to needs or costs. As such, there are clear 
limitations to the usefulness of an estimate of between-school differences in district-level budgets by factors 
such as student poverty rates, limited English proficiency rates, and disability rates. This estimate would 
merely reflect the average effect of the state distribution formula with respect to these factors. The estimate 
tells us nothing about whether the state weights can rationally be used for cost or need adjustment. 
11 In addition, Baker (forthcoming), in a study of large urban Texas districts in their labor market context, 
applies cost-adjusted equity analysis for schools within the large urban core and then across all schools within 
the labor market for each large urban core. Baker finds that in Dallas the within-Dallas variation is greater 
than variation across schools throughout the labor market, but that in Houston, San Antonio, Austin and 
Fort Worth, the labor-market wide variation is greater than the within-urban-core variation.  



E ducation Policy A nalysis A rchives V ol. 18 N o. 9 8 
 
states. That is, their analysis is based on a calculation of how much is spent on a specific student 
population in a school district compared to how much is spent on the average student in the same 
district. They explain that these weights represent budgeting priorities or the “relative investment in 
each student type” (p. 16). The authors show that the budgeting priorities (implicit weights) for 
different student types vary from one district to the next, pointing out that such variation is 
problematic. 

In contrast, Roza, Guin, Gross and Deburgomaster, in the above-discussed Do Districts Fund 
Fairly? (2007) article, calculate separate implicit WSIs for each district as a basis for estimating 
within-district disparities adjusted for student characteristics, across multiple districts. That is, the 
analysis highlights only funding variations within each district, not allowing for identification of 
funding variation for similar students between districts, and not allowing for judgments about 
whether weights are linked to need. Such application of within-district implicit weights is thus deeply 
problematic at two levels. 

First, this approach determines within-district inequities by interpreting or—more 
accurately—defining the variation in district-level spending priorities as variation in cost and need.12 

Second, this approach creates illogical apples-to-bratwurst comparisons by applying different 
sets of implicit weights across schools for each different district and then, by aggregating the results 
across districts, mashing together the apples and bratwurst into an unwholesome smoothie. For 
example, if the child in poverty in school S1 in district D1 receives 20% more than the average child 
in district D1, then district D1 will be considered equitable if the child in poverty in all schools in 
that district receive 20% more than average funding. But a second district (D2) would be considered 
comparably equitable to district D1 if each child in poverty in each school received 10% less than 
average funding—because D2 disadvantaged poor children as systematically as D1 advantaged them. 
D2 simply had different “spending priorities” which were equitably applied. Averaging D1’s equity 
with D2’s equity, we would find the system to be extremely equitable, despite D2’s systematic 
disadvantaging of poor children. Such a finding masks real inequities that would be revealed by the 
more standard methods applied by Ajwad (2006), Baker (2009) or Rubenstein, Schwartz, Stiefel and 
Bel Hadj Amor (2007).  

Simply put, Roza, Guin, Gross and Deburgomaster (2007) fail to acknowledge that Roza, 
Guin and Davis (2007) severely undermines their findings at these two levels: (a) that the implicit-
weight-driven WSI reflects spending priorities that cannot reasonably be used as an independent 
determination of equity that adjusts for student characteristics, and (b) that allowing implicit weights 
to vary by district in a school-level equity analysis—for which findings are eventually aggregated 
across districts—produces findings that are difficult to interpret at best, and entirely meaningless at 
worst. Yet this Education Next study has arguably become the most frequently cited basis for the 
broader assertion that within-district inequity should supplant between-district inequity as the 
funding policy concern du jour. 

 
The Missing Link and Misguided Assumptions 

 

                                                
12 Recall that Roza and her colleagues explain that the WSI is a ratio of actual funding received 
relative to expected funding if the school received the district average for its mix of students. 
Though this approach has little to do with adjustment for “costs” or “needs,” Roza, Guin, Gross 
and Deburgomaster (2007) state: “The WSI allows us to compare per-pupil funding in schools while 
accounting for the types of students a school serves.” ( p. 71) 



Premature celebrations          9 
 

None of the above-discussed studies addresses more broadly the level of between-district 
disparities across all states and school districts. This is important because the recent policy 
arguments directly implicate the relationship between within-district and between-district disparities. 
That is, some researchers and advocates have begun to contend that within-district disparities are 
more important than any remaining between-district disparities. 

Carr, Gray and Holley (2007), for example, contend that average per pupil spending in 
higher-poverty districts in Ohio has increased over time at a rate faster than in lower-poverty 
districts. They also contend that this spending has reached an average level higher than for lower-
poverty districts (Chart 2, page 3). The authors present cursory analyses to support these 
contentions, failing to explore in any depth whether these differences are systematic across districts. 
That is, are there significant inequities between districts among higher- and lower-poverty districts? 
Instead, they move quickly from their broad findings to the argument that the state of Ohio has met 
its state obligation and that the remaining focus should be on within-district inequities. 

To the authors’ credit, as we discuss later herein, it turns out that Ohio does indeed have a 
progressive distribution of fiscal resources across school districts—more resources targeted to 
higher-poverty districts—on average.13 Whether Ohio has met its obligation as a state is a separate 
question. Also to their credit, the authors do not attempt to extrapolate their Ohio-based claims to 
other states or to the nation as a whole.  

More troublesome claims are presented in a recent highly publicized report on school 
finance reform from the Center for Reinventing Public Education (Hill, Roza and Harvey, 2009). 
The report states that recent “intradistrict or subdistrict studies have questioned the importance of 
district-level spending differences, showing that there is more variation in per-pupil spending within 
than between districts” (p. 18). The authors’ only citation for the presumption that within-district 
inequity is a greater national problem than between-district inequity is to the study of Texas large 
districts by Roza, Guin, Gross and Deburgomaster (2007) noted above. That is, the authors make a 
nationwide policy argument on the basis that prior research has documented within-district inequity 
as the greater problem. The flaw here is two-fold. First, Hill, Roza and Harvey cite only a piece 
coauthored by Roza which considers only Texas data, not national data or data on any state other 
than Texas. Second, as we have discussed above, the analysis of Texas data itself is extremely 
problematic. 

In the same report, Hill, Roza and Harvey cite the findings of Rose, Sonstelie and Reinhard 
(2006) on within-district teacher salary disparities in California. Again, they extend these findings to 
the rest of the country but offer no evidence to support their key contention that “What is true in 
California happens to some degree in every other state and every large district” (p. 10).14 

On other occasions, Roza and Hill have argued that persistent between-district disparities 
may exist but are relatively unimportant. Following a state high court decision in New York 
                                                
13 While Ohio’s system is generally progressive, there is important variation among labor markets. Across 
districts within some Ohio labor markets, the relationship between poverty and per pupil spending is stronger 
and more systematic, and in others, weaker and less systematic. Accordingly, some Ohio cities are poorly 
positioned with respect to their neighbors. 
14 Interestingly, the nearly concurrent study by Roza and her other co-authors used much more measured 
wording. For example, Roza, Guin and Davis state merely that: “Several studies give cause for concern by 
demonstrating that spending differences among schools within districts at times exceed spending differences 
across districts (e.g., Hertert 1995; Roza, Guin, Gross, and Deburgomaster, forthcoming 2007).” The authors’ 
statement recognizes that limited nature of their Texas study and of Hertert’s 1995 book chapter, which 
suggest only that within-district disparities “at times” exceed between-district disparities. Hertert’s study, as 
discussed previously, supports this contention, but specifically in California and lacking attention to cost 
adjustment, as would be commonly expected in more recent research. 



E ducation Policy A nalysis A rchives V ol. 18 N o. 9 10 
 
mandating increased funding to New York City schools, Roza and Hill (2005) opined: “So, the real 
problem is not that New York City spends some $4,000 less per pupil than Westchester County, but 
that some schools in New York [City] spend $10,000 more per pupil than others in the same city.” 
That is, the state has fixed its end of the system enough. 

This statement by Roza and Hill is even more problematic when one dissects it more 
carefully. What they are saying is that the average of per pupil spending in suburban districts is only 
$4,000 greater than spending per pupil in New York City but that the difference between maximum 
and minimum spending across schools in New York City is about $10,000 per pupil. Note the rather 
misleading apples-and-oranges issue. They are comparing the average in one case to the extremes in 
another. 

In fact, among downstate suburban15 New York State districts, the range of between-district 
differences in 2005 was an astounding $50,000 per pupil (between the small, wealthy Bridgehampton 
district at $69,772 and Franklin Square at $13,979). In that same year, New York City as a district 
spent $16,616 per pupil, while nine downstate suburban districts spent more than $26,616 (that is, 
more than $10,000 beyond the average for New York City). Pocantico Hills and Greenburgh, both 
in Westchester County (the comparison County used by Roza and Hill), spent over $30,000 per 
pupil in 2005.16 These numbers dwarf even the purported $10,000 range within New York City (a 
range that we agree is presumptively problematic); our conclusion based on this cursory analysis is 
that the bigger problem likely remains the between-district disparity in funding. 

That said, these are anecdotal comparisons between values not adjusted for variations in 
costs.17 What these examples and the above review do highlight is the very shallow depth of the 
current research base that might be used to tease out the relative sources of school-level funding 
disparities. Studies of within-district disparities are largely confined to a few states or individual 
districts where school-site expenditure data have been available. Yet, notwithstanding the fact that 
state school finance policies are idiosyncratic, studies having oft-suspect validity from select 
locations have been extrapolated by prominent researchers and advocates to have broader 
implications for within- and between-district disparities in other states.18 
                                                
15 “Downstate Suburban” refers to areas such as Westchester County and Long Island and is an official 
regional classification in the New York State Education Department Fiscal Analysis and Research Unit 
Annual Financial Reports data, which can be found here: 
http://www.oms.nysed.gov/faru/PDFDocuments/2008_Analysis.pdf and 
http://www.oms.nysed.gov/faru/Profiles/profiles_cover.html 
16 Interestingly, however, Bridgehampton and New York City have relatively similar “costs” due to 
Bridgehampton’s small size and New York City’s high student needs (see Duncombe and Yinger, 2009). The 
figures offered in this paragraph are based on Total Expenditures per Pupil from State Fiscal Profiles 2005. 
http://www.oms.nysed.gov/faru/Profiles/profiles_cover.html. Results are similar when comparing current 
operating expenditures per pupil.  
17 Both are estimated to have per pupil costs about 2.25 times average cost to achieve comparable outcomes. 
Individual district cost indices provided by authors. By contrast, Pocantico Hills is estimated to have per pupil 
costs only 29% above average (also small size) and Greenburgh about 42% above average costs. As a result, 
per pupil spending in Greenburgh and Pocantico Hills is actually far greater than $10,000 more than New 
York City spending.  
18 As one additional example, William Ouchi (2004), based on a study of within district allocation 
and governance practices in Houston, Seattle, New York, Chicago and Los Angeles broadly 
proclaims: “Today’s urban school districts have more than enough money in their budgets to do 
their jobs well.” Baker and Thomas (2006) counter that New York City and Chicago Public Schools 
each spent well below that of surrounding districts in the years in which Ouchi studied these 
districts, but ignored their surroundings and state policy context. 



Premature celebrations          11 
 

There are, by our count, five inter-connected issues swimming around in this soup: 
1. The existence of within-district funding disparities. 
2. The extent of any such within-district disparities. 
3. The continuing existence of between-district disparities. 
4. The extent of any such between-district disparities. 
5. The relative causal importance of within- and between-district disparities. 

Our best reading of the extant literature tells us that numbers (1) and (3) should be non-
controversial: disparities do exist, but they vary tremendously by jurisdiction. As discussed above, 
the evidence regarding number (2) is very limited, which also means we can provide no answers 
regarding number (5). But it is number (4) that is most interestingly implicated by the recent policy 
push—the contention that we as a nation have made such progress on addressing between-district 
disparities that we can now turn our attention elsewhere. As such, a fifty state analysis of the current 
status of between-district funding inequities is warranted. 
 

Goals of this Analysis 
The following analyses are focused only on the question of between-district disparities. That 

is, we are investigating the sometimes-explicit, sometimes-implicit argument that states have fulfilled 
their equity obligations, allowing a narrowed focus on district-level resource distribution policies. 
Our analyses are in two parts. First, we evaluate changes in the income-revenue relationship across 
school districts within states from 1990 to 2005. That is, we evaluate the extent to which combined 
state and local revenues per pupil are associated with median household income at the school district 
level. We apply Census 1990 income estimates for data from 1990 to 1995 and Census 2000 income 
estimates for data from 1996 to 2005. The goal of these analyses is to identify the extent to which 
state and local revenues across all states and within each state are linked to local differences in 
income. We characterize state school finance systems where state and local revenues are positively 
associated with income as regressive, meaning that wealthier districts receive more revenues. Similarly, 
we characterize state school finance systems where state and local revenues are negatively associated 
with income as progressive. This approach is similar to that used by Card and Payne (2002). 

Second, using the most recent three years of federal data on local school district finances, we 
generate—for each state—predicted values of state and local revenue per pupil at 0% poverty, 10% 
poverty, 20% poverty and 30% poverty (based on U.S. Census Poverty Rates),19 holding constant 
district economies of scale, population density, regional variations in competitive wages, and year. 
This approach allows us to construct a more complex and thorough model of state and local 
expenditures and also allows us to evaluate the extent to which states are providing predictably 
progressive (systematically supporting poor districts and the children they serve) resources across all 
districts within the state. 

 
Longitudinal Changes, from 1990 to 2005 

The first data set consists of 10,189 unified public school districts matched (the same 
districts over time) over the 16-year period from 1990 to 2005. The dependent measure of interest is 
                                                
19 U.S. Census Small Area Income and Poverty Estimates (SAIPE) data are used in this analysis in part due to 
gaps in reporting of subsidized lunch data across all districts and states. In general, free and reduced price 
lunch rates run about 2 to 3 times U.S. Census poverty rates because higher-qualifying income thresholds are 
applied (130% poverty level for free lunch and 185% poverty level for reduced price lunch). Accordingly, the 
30 percent poverty category here corresponds to an approximately rate of 90 percent free and reduced price 
lunch. 



E ducation Policy A nalysis A rchives V ol. 18 N o. 9 12 
 
the total state and local revenues per pupil at the level of the school districts. Other relevant 
measures include the National Center for Education Statistics (NCES) Education Comparable Wage 
Index (available from 1997 to 2005), district enrollments, and data on median household income 
drawn from the U.S. Census and NCES School District Demographics System. 

The objective of the statistical model is to evaluate the relationship between the income 
measure and state and local revenues per pupil, over time, but conditional on major factors affecting 
the differential costs of providing education in different settings and under different conditions. As 
such, the following equation is applied: 

 
EQ1: 

lnSLOCREVPPd,t = b0 + b1ECWId,t  + b1SCALEd,t + b2STATEd+ b3YEARd,t + b4lnINCOMEd,t + 
b5(lnINCOMEd,t x YEARd) + e 

 
Where SLOCREVPP is the total of state and local revenue per pupil for each school district, ECWI 
is the National Center for Education Statistics Education Comparable Wage Index, SCALE is a 
matrix of dummy variables indicating enrollment size, STATE is a matrix of dummy variables for 
each state, YEAR is a matrix of dummy variables for each year in the data set, and INCOME is 
median household income (from U.S. Census 1990 and 2000). 

State and Local Revenue per Pupil is expressed here as a natural logarithm because it is 
assumed that marginal increases in state and local revenue diminish at very high levels of median 
household income, also expressed as a natural logarithm. The ECWI (education comparable wage 
index) is applied annually from 1997 to 2005, but it is held at 1997 levels back through 1990 
(because of its unavailability prior to 1997). Economies of scale are captured with a series of 
enrollment group variables from less than 100 students up to greater than 2,000 students.  

Note that from 1997 to 2005, the ECWI picks up inflationary changes but prior to 1997, the 
year fixed effect picks up those inflationary changes. Our primary interest is in the income term and 
that term’s interaction with the year term—or the way in which the relationship between income and 
state and local revenues per pupil changes over time. Below, in the “Findings” section of this article, 
we plot these elasticities over time to characterize the improvement—or lack thereof—in the 
distribution of state and local per pupil revenues over time. 

 
Current Profiles, from 2005 to 2007 

Our second data set consists of over 13,000 local public school districts from 2005 to 2007 
and combines data from multiple sources including the following: U.S. Census fiscal survey of local 
governments (state and local revenues per pupil), U.S. Census small area income and poverty 
estimates of children between the ages of 5 and 17 in poverty, the NCES Education Comparable 
Wage Index (ECWI), and a county-level measure (from U.S. Census) of population density. 

We use these data to estimate a slightly more complex model than the one estimated for our 
longitudinal analysis. In this case, our goal is to test the sensitivity of state and local revenues per 
pupil to student poverty rates across districts. Similar to our approach with the first set of analyses, a 
regressive state school finance system is one in which state and local revenues per pupil are 
negatively and systematically associated with poverty rates across districts, a progressive system is 
one in which state and local revenues per pupil are positively associated with poverty across districts, 
and a neutral system is one in which no relationship exists between poverty rates and state and local 
revenues per pupil. We assume a policy objective of progressiveness, where the ultimate objective of 
state school finance policy is to provide equal educational opportunity to children attending districts 
in high-poverty settings by providing additional resources in those settings (see Duncombe and 



Premature celebrations          13 
 

Yinger, 2008). We estimate the elasticities20 between poverty and state and local revenues, 
conditional on major factors with demonstrated potential to affect the relative costs of providing 
educational services in one setting or location versus another. 

We estimate a three-year regression model using a pooled cross-state universe of local public 
school districts and local education agencies. That model is specified as follows: 

 
EQ2: 

lnSLOCREVPPd,t = b0 + b1ECWId,t-2  + b2SCALEd,t + b3lnDENSITYc + b4(SCALEd,t x 
lnDENSITYc) + b5STATEd+ b6YEARd,t + b7(STATEd,t x YEARd,t) + b8POVERTYd,t + 

b9(POVERTYd,t x STATEd) + e 
 
In this model, lnSLOCREVPP is the natural logarithm of state and local revenue per pupil for 
district “d” at time “t”, ECWI is the education comparable wage index for district “d” at time “t-2” 
(lagged primarily due to lack of an updated index),21 SCALE is a series of categorical dummy 
variables indicating district enrollment size for district “d” at time “t”, and SCALE is interacted with 
the natural log of the 2007 population density (DENSITY) for the primary county “c” of the 
district’s location. This interaction term is included to capture the national average spending 
differences associated with economies of scale given population density. STATE is the state of 
district “d”, which is also included in an interaction term with two year dummy variables (for 2005-
06 and 2006-07, with base year 2004-05) in order to generate a coefficient of state-specific revenue-
per-pupil increases (both state and local). 

The next term is POVERTY, which is the U.S. census poverty rate for resident 5- to 17-year 
olds in district “d” at time “t”, which is followed by an interaction term between POVERY and 
STATE. It is this final interaction that is of primary interest to us in evaluating the fairness of state 
school finance systems. That is, how do state and local revenues per pupil vary by poverty, within 
states? 

This model is weighted for district enrollment because the ultimate goal of analyses of this 
type is to evaluate how, on average, the state school finance system treats its children. As such, it 
makes little sense to identify a state as fair if that state has a large number of very small high-poverty 
school districts that are well funded, but a handful of large districts that are poorly funded serving 
many more children than the aggregate of the small districts. The same is true of the inverse 
scenario, where a state does a good job in funding its larger urban districts but neglects poor rural 

                                                
20 Elasticities are measured on a scale from 1 to -1, where an elasticity of 1.0 would signify a perfect alignment 
between revenues and median household income, while an elasticity of -1.0 would signify a perfect negative 
relationship between revenues and median household income. An elasticity of 1.0 would indicate that for a 
1% change in income, one would expect a corresponding 1% change in state and local revenue per pupil. An 
elasticity of .1 would indicate that for a 10% change in income, one could expect a 1% change in state and 
local revenue. Relationships among variables like income and revenue must be evaluated against 
“expectations” for relationships among those specific variables, rather than against some more general 
statistical benchmark. In this case, an elasticity of .20 would be considered high.  
21 The current NCES ECWI is available for all districts from 1997 to 2005 and accounts for both between-
labor market differences in competitive wages and inflationary changes in wages over time. Because it is 
reasonable to assume that most teachers in a school district were hired two or more years ago it is reasonable 
to account for cost variation across districts by lagging the CWI by two years. Further, we account 
additionally for inflation by including a year fixed effect in our models which should pick up any differences 
in the average change in state and local revenue not captured by the inflation component of the CWI (even if 
we excluded the inflation component). Finally, our use of the ECWI is primarily to account for between-labor 
market differences which do not change substantively from year to year. 



E ducation Policy A nalysis A rchives V ol. 18 N o. 9 14 
 
ones. In both types of scenarios, the weight remains greater on large districts because of the 
numbers of children they serve. 

With the regression equation estimates, we then predict for each state the expected state and 
local revenues per pupil for a district with 0% poverty, 10% poverty, 20% poverty and 30% poverty. 
In doing so, we hold constant at the national average the NCES ECWI, we set the economies of 
scale dummy indicators to zero and the density to average (such that the hypothetical district 
represents a district of over 2,000 students, or one operating at the most efficient scale), and we set 
the year to 2006-07. 

Findings 

In this section, we provide national longitudinal findings, as well as current status and 
longitudinal findings for selected states (by region). Figure 1 presents the national mean elasticity 
between income and per pupil revenues (state and local) from the models using the data from 
10,189 districts. It shows that state and local revenues in 1990 were positively associated in a 
relatively strong way with median household income (an elasticity of over .20). Since that time, the 
relationship has moderated, with the income-revenue relationship becoming less positive, meaning 
that school funding overall has become less regressive. But funding has nevertheless remained 
positively associated with income as an average across all states. 

 
Figure 1: National Mean Relationship between Income and State and Local Revenue per Pupil over Time 

  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Source: Based on regression (lnState&Local = f(CWI, Year, MedHouseInc, MedHouseInc x Year, 
Scale, State) of federal data on 10,189 observations per year, matched over time. 

 
 



Premature celebrations          15 
 

This finding, of course, conceals substantial heterogeneity across states. Figure 2 displays the 
individual state trends of income-revenue elasticity for three Mid-Atlantic states (Delaware was 
excluded from this longitudinal analysis due to insufficient data points across districts for all years).  

Consistent with the national trend, all of the Mid-Atlantic states have become less regressive 
over time. Nonetheless, all these states start out highly regressive, and only New Jersey has a 
negative income-revenue relationship across their school districts at the end of the period. From 
1998 through 2005, the relationship between income and state and local revenues in Maryland 
actually increases. In New York, despite a fairly steady move toward less regressiveness, the 
relationship between income and state and local revenue in 2005 remains stronger than the national 
average relationship had been in 1990. 
 

 
Figure 2: Subdivision and State Level Changes in Income-Revenue Relationship in Mid-Atlantic Region 

 
 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
Source: Based on regression (lnState&Local = f(CWI, Year, MedHouseInc, MedHouseInc x Year, 
Scale, State) of federal data on 10,189 observations per year, matched over time. 

 
 
Figure 3 presents the profiles of these three states plus Delaware, showing projected state 

and local revenues at varied poverty levels for 2007 after controlling for regional costs, population 
density, economies of scale and poverty. As in Figure 2, the New Jersey distribution is progressive 
while the others are regressive. The additional information provided in Figure 3 is the overall level of 
funding available across districts at differing levels of poverty. Comparing New York and New 
Jersey, for example, low-poverty New York districts have more resources than low-poverty New 
Jersey districts, but for higher-poverty districts the differential is reversed. Both New York and New 
Jersey districts have more resources than those in Delaware and Maryland. 

 



E ducation Policy A nalysis A rchives V ol. 18 N o. 9 16 
 
 
Figure 3: State Level Profiles of State and Local Revenue per Pupil for Mid-Atlantic States in 2007 
 

  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
Source: Based on regression of over 13,000 observations per year for 2004-05, 2005-06 and 2006-07 
with state and local revenue per pupil as dependent variable, and CWI, Scale, Density, Year, Census 
Poverty Rate and State as independent variables. 
 
 
Figure 4 compares the trends in income-revenue elasticity for states in New England. 

(Vermont is excluded due to data issues including relatively small shares of children attending 
unified public school districts.) Massachusetts shifted from a positive to negative income-revenue 
relationship in the mid-1990s and then leveled off at this negative (progressive) relationship. Because 
two high-poverty districts experienced funding increases (Hartford and New Haven), Connecticut 
also shifted to a slightly negative income-revenue relationship, albeit not statistically significant. 
Rhode Island also shifted to a slightly (and non-significant) negative relationship between income 
and revenue around 2000. By contrast, income-related inequities in state and local revenues per pupil 
remained sizeable in New Hampshire throughout the period, and inequities in Maine actually 
increased between 1998 and 2005. 

 
 
 
 

 



Premature celebrations          17 
 

Figure 4: Subdivision and State Level Changes in Income-Revenue Relationship in New England Region 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Source: Based on regression (lnState&Local = f(CWI, Year, MedHouseInc, MedHouseInc x Year, Scale, State) 
of federal data on 10,189 observations per year, matched over time 

 
Figure 5: State Level Profiles of State and Local Revenue per Pupil for New England States in 2007 

 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Source: Based on regression of over 13,000 observations per year for 2004-05, 2005-06 and 2006-07 with state and local 
revenue per pupil as dependent variable, and CWI, Scale, Density, Year, Census Poverty Rate and State as independent 
variables. 



E ducation Policy A nalysis A rchives V ol. 18 N o. 9 18 
 

 
 
Figure 5 shows the 2007 state-level poverty profiles for New England states (including 

Vermont), again controlling for regional costs, population density, economies of scale and poverty. 
As revealed in Figure 4, Massachusetts and Connecticut are the only two states in New England 
where higher poverty districts have, on average, more resources per pupil, with that relationship 
being less systematic in Connecticut than in Massachusetts. In Rhode Island, the distribution 
remains poverty-neutral. Vermont, too, is fairly neutral, but at a higher spending level than the other 
states. In Maine and especially New Hampshire, higher-poverty districts have significantly less state 
and local revenue per pupil than lower-poverty districts. 

Figure 6 shows the longitudinal trends in income-revenue elasticity for North Central states. 
As this figure makes clear, Minnesota is the only state in this region that has achieved progressive 
distribution of resources with respect to income, beginning with a progressive distribution by 1990 
and building on that progressiveness during the succeeding years. This pattern is driven largely by 
elevated levels of funding in Minneapolis and St. Paul. In contrast, Iowa and Wisconsin have 
maintained relatively neutral funding with respect to income since the mid-1990s. Illinois, similar to 
Maryland and Maine, has become increasingly regressive since the mid-1990s. By 2004, Illinois had 
an income-revenue relationship comparable to the national average relationship in 1990.  

 
Figure 6: Subdivision and State Level Changes in Income-Revenue Relationship in North Central Region 

  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Source: Based on regression (lnState&Local = f(CWI, Year, MedHouseInc, MedHouseInc x Year, 
Scale, State) of federal data on 10,189 observations per year, matched over time. 

 
Figure 7 displays the 2007 poverty profiles for these states. Only Minnesota maintains a 

poverty-progressive distribution of state and local revenues per pupil. Wisconsin and Iowa maintain 



Premature celebrations          19 
 

relatively flat distributions, and Illinois maintains a highly regressive distribution of state and local 
revenues per pupil with respect to poverty.  
 
Figure 7: State Level Profiles of State and Local Revenue per Pupil for North Central States in 2007 

  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Source: Based on regression of over 13,000 observations per year for 2004-05, 2005-06 and 2006-07 
with state and local revenue per pupil as dependent variable, and CWI, Scale, Density, Year, Census 
Poverty Rate and State as independent variables. 

 
The above graphs are illustrative of the heterogeneity of school funding inequities that 

persist across states in most regions. Appendix A sets forth a table of projected state and local 
revenues at varied poverty levels for all states, based on our three-year panel analysis from 2005 to 
2007. Notably, some regions—in particular, the Southeast—include only states where the poverty-
revenue relationship remains regressive. 

Table 1 presents a subset of the data in Appendix A, summarizing spending trends in those 
states having the most and least progressive distribution of state and local revenues. Alaska22 and 

                                                
22 Our models project that in Alaska, a district with 30% children in poverty would be expected to have nearly 
three times the per pupil revenues of a district with 0% children in poverty. Notably, Alaska is a quirky state 
when it comes to the organization of its schools and distribution of children. This strong positive effect is 
partly a function of very high poverty rates in very remote rural school districts, which receive a substantial 
boost in funding due to their remoteness. It might be argued that those “similarly” small and remote school 
districts in other states like Wyoming or Montana are not in fact similar enough to Alaska schools. 



E ducation Policy A nalysis A rchives V ol. 18 N o. 9 20 
 
Utah,23 which lead the pack, are both a bit quirky. Other states with positive systematic relationships 
between state and local revenues and poverty include New Jersey, Minnesota, Massachusetts, 
Indiana, and Ohio.24 Despite our efforts to correct as fully as possible for factors such as size and 
economies of scale, we remain less confident in relationships revealed for South Dakota, Montana 
and New Mexico because of smaller overall shares of total children attending scale-efficient unified 
districts, but all three do show statistically significant positive relationships. 

Among the bottom ten states are six where the negative relationship between state and local 
revenue per pupil and poverty is not systematic (labeled with an “N” in the “confidence” column on 
the right side of the table). These states’ school finance systems are certainly still problematic; on 
average, high-poverty districts receive less than low-poverty districts. But a non-systematic 
relationship suggests differences in state and local revenues where some poor districts receive more 
than others and some less than others, even after accounting for a variety of other cost factors. 

 
Table 1: Most and least progressive states in 2007 

State 

Mean Actual 
State and 

Local Rev. 
per Pupil 

At 0% 
Poverty 

At 10% 
Poverty 

At 20% 
Poverty 

At 30% 
Poverty 

High/ 
Low Confidence

* 

TOP TEN               
Alaska $12,504 $7,859 $11,665 $17,314 $25,699 327%  
Utah $6,586 $5,700 $6,539 $7,503 $8,608 151%  
New Jersey $17,115 $13,464 $15,060 $16,845 $18,841 140%  
Minnesota $10,893 $9,391 $10,458 $11,646 $12,968 138%  
Ohio $10,933 $9,054 $9,896 $10,816 $11,821 131%  
South Dakota $8,347 $7,467 $8,066 $8,712 $9,410 126%  
Massachusetts $14,355 $12,146 $12,880 $13,658 $14,483 119%  
Montana $9,158 $7,848 $8,279 $8,733 $9,213 117%  
Indiana $9,271 $8,534 $8,991 $9,471 $9,978 117%  
New Mexico $8,890 $8,286 $8,664 $9,060 $9,474 114%  
BOTTOM TEN               
Missouri $8,689 $8,994 $8,611 $8,244 $7,893 88% N 
Maine $11,903 $12,532 $11,889 $11,279 $10,701 85% N 
Pennsylvania $12,282 $12,715 $12,020 $11,362 $10,741 84%  
Virginia $10,854 $10,758 $10,157 $9,590 $9,054 84% N 
North Carolina $8,401 $9,134 $8,615 $8,126 $7,664 84% N 
North Dakota $9,063 $9,370 $8,788 $8,241 $7,728 82% N 
New York $17,247 $17,012 $15,931 $14,920 $13,972 82%  
Illinois $10,179 $10,430 $9,589 $8,816 $8,105 78%  
Nevada $8,829 $9,916 $8,988 $8,146 $7,383 74% N 
New Hampshire $12,351 $13,113 $11,304 $9,745 $8,401 64%   

* The “N” in the column signals that the negative relationship between state and local revenue per pupil and poverty is 
not systematic. 
 

In four of these bottom ten states—Pennsylvania, New York, Illinois and New 
Hampshire—there existed in 2006-07 a strong, negative, and systematic relationship between school 
district poverty and state and local revenues per pupil. In each of these states, high-poverty districts 

                                                
23 Utah shows a relatively high ratio of state and local revenues per pupil in high poverty compared to low-
poverty districts. But Utah’s state and local revenues per pupil at all poverty levels are very low—last in the 
nation. 
24 This finding provides some support for Carr, Gray and Holley’s untested contention that Ohio has made 
progress on resolving between-district disparities. 



Premature celebrations          21 
 

were expected to receive systematically less state and local revenue per pupil than low-poverty 
districts. Since 2006-07, New York and Pennsylvania have begun implementing changes to their 
state school finance systems, which may change this distribution. Illinois and New Hampshire, 
however, have not.  

Conclusions and Policy Implications 

Overall, we find that there has been no consistent treatment or resolution of between-district 
disparities in school revenues. New Hampshire and some other states have made no apparent 
progress. Illinois is an example of a state that has actually become more regressive over time. 
Pennsylvania and New York are among those states that, while still highly regressive, may be making 
recent progress as a result of newly adopted state school finance formulas. And states like New 
Jersey, Massachusetts and Ohio have made genuine progress in moving toward a progressive system. 

This answers an important question: problems with between-district revenue disparities are 
still not resolved. Some states have made progress, but others have actually regressed. 

The above analysis, however, does not attempt to answer a second, related question. 
Researchers such as Roza have raised the comparative issue: which type of disparity—inter-district 
or intra-district—results in greater inequities. An analysis that would appropriately answer this 
question concerning relative importance would require more systematic efforts to evaluate 
simultaneously these disparities and their effects on students in a given area. Such analyses would 
also require more systematically collected data on school-site expenditures. Only a handful of states, 
including California, Ohio, Rhode Island and Texas, currently engage in a thorough collection of this 
type of data across all districts. Further, even among these states, data on school-site expenditures 
differ from state to state, making difficult any cross-state comparisons of within-district disparities. 

For the foreseeable future, therefore, researchers will likely be confined to evaluating within- 
and between-district equity for specific states, one at a time. This type of focus on individual states is 
generally most appropriate anyway, given the heterogeneity of state school finance systems. Few 
generalizations regarding equity of school funding can reasonably be drawn across all states or even 
among groups of geographically contiguous states; any such generalizations should be greeted with a 
high degree of skepticism. 

We certainly recommend that more states collect and make available school-level spending 
data. Yet we hasten to add that the comparative question itself is largely misguided. There is no need 
to rank the two sources of inequity, nor is there a need to address one to the exclusion of the other. 
Moreover, our inability to draw national conclusions regarding within- or between-district inequities 
only modestly constrains any policy responses. The role of federal funding is relatively limited, 
having negligible effects on the overall relationships addressed in the analyses set forth above 
(Baker, Sciarra and Coley, forthcoming). Instead, each individual state has control over the levels of 
funding available across its local public school districts. Each state also has control over 
accountability policies that might push those districts toward greater internal equity in resource 
distribution. The solutions to these state-level problems lie largely with state governments, in the 
absence of a dramatically expanded federal role. Downes (2002) similarly explains that: 

 
School finance reforms, tax and expenditure limitations, and legislation enabling the 
creation of charter schools have as many differences across states as they have 
commonalities. The challenge facing researchers is to determine what lessons can be 
learned only from national-level analyses and only from state-level case studies and 
to distill these lessons for policymakers (p. 161). 



E ducation Policy A nalysis A rchives V ol. 18 N o. 9 22 
 

The main lesson to be drawn from the cumulative state-level analyses documented earlier in 
this article is that it is premature to shift policy attention away from between-district spending 
disparities. Just to be clear, it is difficult to evaluate the policy impact to date of advocacy in favor of 
this shift the emphasis. Looking first at litigation, we see little evidence, as of yet, that state courts 
are willing to emphasize one over the other, or to back off from mandating that states resolve 
between-district disparities until districts first do their part. Moreover, we expect that state courts 
will continue to acknowledge that the primary responsibility for ensuring equity across both schools 
and districts rests with the state, pursuant to language in state constitutions. 

But we are less sanguine about the potential emphasis shift in state legislatures. A goal of this 
article is to head off what we perceive as a growing wave of policy advocacy and punditry seeking to 
provide ineffective but deceptively convenient revenue-neutral school-finance “solutions” for state 
legislatures (see Baker and Elmer, 2009). We point to the esteemed list of signatories of the original 
Fordham Institute Fund the Child advocacy document as evidence that our concerns are not 
unfounded (Fordham Institute, 2006).25 We also point to proposals from the Center for American 
Progress for using Title I aid as the carrot for requiring states to fix within-district but not between-
district disparities as evidence that influential policy think tanks at least plan to use this argument to 
influence federal policy (McClure, Wiener, Roza, and Hill, 2008). Such arguments have made their 
way into congressional hearings in the past year, and we expect increased emphasis on these issues 
as reauthorization of ESEA moves forward.26 

Research and policy attention to within-district resource allocation should certainly continue, 
since it appears that this is an important issue as well. But policymakers should be skeptical of claims 
that the resource needs of high-poverty schools can generally be met merely via a reshuffling of 
district-level resources. Further, policymakers must be cognizant that persistent between-district 
disparities may significantly constrain districts’ ability to resolve within-district disparities (Baker, 
forthcoming). Remedies should consider both simultaneously. While the circumstances of each state 
and each district will differ, spending patterns in the country as a whole are still far from the 
progressivity that would be needed to provide equitable and adequate resources to these schools. 

                                                
25 See  http://www.edexcellence.net/fundthechild/signatories.cfm  
26 See testimony of Marguerite Roza at: 
http://edlabor.house.gov/documents/111/pdf/testimony/20090930MargueriteRozeTestimony.pdf 
See the testimony of Linda Murray at: 
http://edlabor.house.gov/documents/111/pdf/testimony/20090930LindaMurrayTestimony.pdf  
 



Premature celebrations          23 
 

Appendix A 
Average actual state and local revenues and predicted state and local revenues from regression 
model (Current Profiles EQ2) with poverty set at varied levels  

State 

Mean Actual 
State and 

Local Rev. 
per Pupil 

At 0% 
Poverty 

At 10% 
Poverty 

At 20% 
Poverty 

At 30% 
Poverty 

High/ 
Low [a] 

Confidence 
[b] 

Alaska $12,504 $7,859 $11,665 $17,314 $25,699 327%  
Utah $6,586 $5,700 $6,539 $7,503 $8,608 151%  
New Jersey $17,115 $13,464 $15,060 $16,845 $18,841 140%  
Minnesota $10,893 $9,391 $10,458 $11,646 $12,968 138%  
Ohio $10,933 $9,054 $9,896 $10,816 $11,821 131%  
South Dakota $8,347 $7,467 $8,066 $8,712 $9,410 126%  
Massachusetts $14,355 $12,146 $12,880 $13,658 $14,483 119%  
Montana $9,158 $7,848 $8,279 $8,733 $9,213 117%  
Indiana $9,271 $8,534 $8,991 $9,471 $9,978 117%  
New Mexico $8,890 $8,286 $8,664 $9,060 $9,474 114%  
Connecticut $15,132 $13,181 $13,765 $14,375 $15,013 114%  
Tennessee $6,966 $6,429 $6,683 $6,946 $7,220 112%  
Oregon $8,525 $8,175 $8,417 $8,666 $8,922 109%  
Wyoming $16,238 $16,254 $16,684 $17,126 $17,580 108% N 
Oklahoma $7,053 $6,665 $6,813 $6,964 $7,118 107%  
Iowa $9,879 $9,723 $9,867 $10,014 $10,163 105%  
Arizona $8,091 $7,801 $7,906 $8,012 $8,120 104%  
Arkansas $8,158 $8,136 $8,233 $8,332 $8,432 104%  
Kentucky $8,585 $8,531 $8,627 $8,724 $8,823 103%  
California $9,774 $8,879 $8,974 $9,069 $9,166 103%  
Georgia $9,969 $9,544 $9,623 $9,703 $9,784 103%  
South Carolina $9,155 $9,057 $9,122 $9,188 $9,255 102%  
Rhode Island $13,114 $12,159 $12,222 $12,285 $12,349 102%  
AVERAGE  $10,153 $10,127 $10,144 $10,207 101%  
West Virginia $9,072 $9,349 $9,361 $9,373 $9,385 100%  
Nebraska $9,881 $9,633 $9,589 $9,545 $9,501 99% N 
Vermont $17,552 $15,802 $15,648 $15,495 $15,344 97% N 
Washington $9,366 $9,076 $8,969 $8,863 $8,758 96%  
Mississippi $7,102 $7,608 $7,505 $7,403 $7,303 96%  
Wisconsin $10,999 $10,813 $10,662 $10,513 $10,367 96%  
Texas $8,813 $8,738 $8,542 $8,350 $8,163 93%  
Michigan $10,200 $10,077 $9,825 $9,580 $9,341 93% N 
Kansas $10,040 $10,300 $10,023 $9,754 $9,492 92% N 
Colorado $9,012 $9,149 $8,882 $8,623 $8,372 92% N 
Louisiana $8,806 $9,558 $9,259 $8,970 $8,689 91% N 
Florida $9,947 $10,216 $9,884 $9,562 $9,251 91% N 
Maryland $12,948 $12,313 $11,856 $11,417 $10,993 89% N 
Alabama $8,591 $9,465 $9,107 $8,764 $8,433 89%  
Delaware $13,572 $13,564 $13,045 $12,546 $12,065 89% N 
Idaho $6,898 $7,471 $7,166 $6,873 $6,593 88% N 
Missouri $8,689 $8,994 $8,611 $8,244 $7,893 88% N 
Maine $11,903 $12,532 $11,889 $11,279 $10,701 85% N 
Pennsylvania $12,282 $12,715 $12,020 $11,362 $10,741 84%  
Virginia $10,854 $10,758 $10,157 $9,590 $9,054 84% N 
North Carolina $8,401 $9,134 $8,615 $8,126 $7,664 84% N 
North Dakota $9,063 $9,370 $8,788 $8,241 $7,728 82% N 
New York $17,247 $17,012 $15,931 $14,920 $13,972 82%  
Illinois $10,179 $10,430 $9,589 $8,816 $8,105 78%  
Nevada $8,829 $9,916 $8,988 $8,146 $7,383 74% N 
New Hampshire $12,351 $13,113 $11,304 $9,745 $8,401 64%  

[a] Ratio of predicted state and local revenue at 30% poverty to predicted revenue at 0% poverty 
[b] N = slope not statistically significant (p<.05) 

 



E ducation Policy A nalysis A rchives V ol. 18 N o. 9 24 
 

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Premature celebrations          27 
 

About the Authors 

Bruce D. Baker 
Rutgers, The State University of New Jersey  
 
Kevin G. Welner  
University of Colorado at Boulder 
 
Email: bruce.baker@gse.rutgers.edu 
 
Bruce D. Baker is associate professor in the Graduate School of Education at Rutgers, The State 
University of New Jersey. He is author of Financing Education Systems, and numerous peer reviewed 
articles on education finance and the economics of education and has testified in state school 
finance litigation regarding between district disparities in Kansas, Missouri, Arizona and New Jersey.  
 
Kevin G. Welner is professor at the University of Colorado at Boulder School of Education and 
director of the Education and the Public Interest Center (EPIC). He is the author of NeoVouchers: 
The emergence of tuition tax credits for private schooling (2008, Rowman & Littlefield) and Legal rights, local 
wrongs: When community control collides with educational equity (2001, SUNY Press). 
 

 

 

education policy analysis archives 
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E ducation Policy A nalysis A rchives V ol. 18 N o. 9 28 
 

education policy analysis archives 
editorial board  

Editor Gustavo E. Fischman (Arizona State University) 
Associate Editors: David R. Garcia & Jeanne M. Powers (Arizona State University) 

 
Jessica Allen University of Colorado, Boulder Christopher Lubienski University of Illinois, 

Urbana-Champaign 
Gary Anderson New York University  Sarah Lubienski University of Illinois, Urbana-

Champaign 
Michael W. Apple University of Wisconsin, 

Madison  
Samuel R. Lucas  University of California, 

Berkeley  
Angela Arzubiaga Arizona State University Maria Martinez-Coslo University of Texas, 

Arlington  
David C. Berliner  Arizona State University  William Mathis University of Colorado, Boulder 
Robert Bickel  Marshall University  Tristan McCowan  Institute of Education, London  
Henry Braun Boston College  Heinrich Mintrop University of California, 

Berkeley  
Eric Camburn  University of Wisconsin, Madison  Michele S. Moses University of Colorado, Boulder 
Wendy C. Chi* University of Colorado, Boulder Julianne Moss  University of Melbourne  
Casey Cobb  University of Connecticut  Sharon Nichols  University of Texas, San Antonio  
Arnold Danzig  Arizona State University  Noga O'Connor University of Iowa  
Antonia Darder  University of Illinois, Urbana-

Champaign 
João Paraskveva  University of Massachusetts, 

Dartmouth  
Linda Darling-Hammond Stanford University  Laurence Parker University of Illinois, Urbana-

Champaign 
Chad d'Entremont Strategies for Children Susan L. Robertson Bristol University 
John Diamond Harvard University  John Rogers University of California, Los Angeles 
Tara Donahue Learning Point Associates  A. G. Rud Purdue University 
Sherman Dorn University of South Florida  Felicia C. Sanders The Pennsylvania State 

University 
Christopher Joseph Frey Bowling Green State 

University  
Janelle Scott University of California, Berkeley  

Melissa Lynn Freeman* Adams State College Kimberly Scott Arizona State University  
Amy Garrett Dikkers University of Minnesota  Dorothy Shipps  Baruch College/CUNY  
Gene V Glass  Arizona State University  Maria Teresa Tatto Michigan State University  
Ronald Glass University of California, Santa Cruz  Larisa Warhol University of Connecticut  
Harvey Goldstein Bristol University  Cally Waite  Social Science Research Council  
Jacob P. K. Gross  Indiana University  John Weathers University of Colorado, Colorado 

Springs  
Eric M. Haas  WestEd  Kevin Welner University of Colorado, Boulder 
Kimberly Joy Howard* University of Southern 

California 
Ed Wiley  University of Colorado, Boulder 

Aimee Howley  Ohio University  Terrence G. Wiley Arizona State University  
Craig Howley  Ohio University  John Willinsky  Stanford University  
Steve Klees  University of Maryland  Kyo Yamashiro  University of California, Los Angeles 
Jaekyung Lee  SUNY Buffalo  * Members of the New Scholars Board 

 
 

 



Premature celebrations          29 
 

archivos analíticos de políticas educativas 
consejo editorial 

Editor:  Gustavo E. Fischman (Arizona State University) 
Editores. Asociados Alejandro Canales (UNAM) y Jesús Romero Morante  (Universidad de Cantabria) 

 
Armando Alcántara Santuario Instituto de 

Investigaciones sobre la Universidad y la 
Educación, UNAM  México 

Fanni Muñoz  Pontificia Universidad Católica de 
Perú 

Claudio Almonacid  Universidad Metropolitana de 
Ciencias de la Educación, Chile 

Imanol Ordorika   Instituto de Investigaciones 
Economicas – UNAM, México 

Pilar Arnaiz Sánchez Universidad de Murcia, 
España 

Maria Cristina Parra Sandoval Universidad de 
Zulia, Venezuela 

Xavier Besalú  Costa Universitat de Girona, España Miguel A. Pereyra Universidad de Granada, España   
Jose Joaquin Brunner  Universidad Diego Portales, 

Chile 
Monica Pini Universidad Nacional de San Martín, 

Argentina 
Damián Canales Sánchez  Instituto Nacional para 

la Evaluación de la Educación, México 
Paula Razquin UNESCO, Francia   

María Caridad García  Universidad Católica del 
Norte, Chile 

Ignacio Rivas Flores Universidad de Málaga, 
España      

Raimundo Cuesta Fernández  IES Fray Luis de 
León, España 

Daniel Schugurensky Universidad de Toronto-
Ontario Institute of Studies in Education, Canadá   

Marco Antonio Delgado Fuentes Universidad 
Iberoamericana, México 

Orlando Pulido Chaves Universidad Pedagógica 
Nacional, Colombia 

Inés Dussel  FLACSO, Argentina José Gregorio Rodríguez Universidad Nacional de 
Colombia   

Rafael Feito Alonso Universidad Complutense de 
Madrid, España 

Miriam Rodríguez Vargas Universidad Autónoma 
de Tamaulipas, México 

Pedro Flores Crespo Universidad Iberoamericana, 
México 

Mario Rueda Beltrán Instituto de Investigaciones 
sobre la Universidad y la Educación, UNAM  
México   

Verónica García Martínez Universidad Juárez 
Autónoma de Tabasco, México 

José Luis San Fabián Maroto Universidad de 
Oviedo, España 

Francisco F. García Pérez Universidad de Sevilla, 
España 

Yengny Marisol Silva Laya Universidad 
Iberoamericana, México 

Edna Luna Serrano  Universidad Autónoma de Baja 
California, México 

Aida Terrón Bañuelos Universidad de Oviedo, 
España 

Alma Maldonado  Departamento de Investigaciones 
Educativas, Centro de Investigación y de 
Estudios Avanzados, México 

Jurjo Torres Santomé Universidad de la Coruña, 
España   

Alejandro Márquez Jiménez Instituto de 
Investigaciones sobre la Universidad y la 
Educación, UNAM  México 

Antoni Verger Planells University of Amsterdam, 
Holanda   

José Felipe Martínez Fernández  University of 
California Los Angeles, USA 

Mario Yapu Universidad Para la Investigación 
Estratégica, Bolivia   

 
 



E ducation Policy A nalysis A rchives V ol. 18 N o. 9 30 
 

arquivos analíticos de políticas educativas 
conselho editorial 

Editor:  Gustavo E. Fischman (Arizona State University) 
Editores Associados: Rosa Maria Bueno Fisher e Luis A. Gandin  

(Universidade Federal do Rio Grande do Sul) 
 

 
Dalila Andrade de Oliveira Universidade Federal de 

Minas Gerais, Brasil 
Jefferson Mainardes Universidade Estadual de 

Ponta Grossa, Brasil 
Paulo Carrano Universidade Federal Fluminense, 

Brasil 
Luciano Mendes de Faria Filho Universidade 

Federal de Minas Gerais, Brasil 
Alicia Maria Catalano de Bonamino Pontificia 

Universidade Católica-Rio, Brasil 
Lia Raquel Moreira Oliveira Universidade do 

Minho, Portugal 
Fabiana de Amorim Marcello Universidade 

Luterana do Brasil, Canoas, Brasil 
Belmira Oliveira Bueno Universidade de São Paulo, 

Brasil 
Alexandre Fernandez Vaz Universidade Federal de 

Santa Catarina, Brasil 
António Teodoro Universidade Lusófona, Portugal 

Gaudêncio Frigotto Universidade do Estado do Rio 
de Janeiro, Brasil 

Pia L. Wong California State University Sacramento, 
U.S.A 

Alfredo M Gomes Universidade Federal de 
Pernambuco, Brasil 

Sandra Regina Sales Universidade Federal Rural do 
Rio de Janeiro, Brasil 

Petronilha Beatriz Gonçalves e Silva Universidade 
Federal de São Carlos, Brasil 

Elba Siqueira Sá Barreto Fundação Carlos Chagas, 
Brasil 

Nadja Herman Pontificia Universidade Católica –
Rio Grande do Sul, Brasil 

Manuela Terrasêca Universidade do Porto, Portugal 

José Machado Pais Instituto de Ciências Sociais da 
Universidade de Lisboa, Portugal 

Robert Verhine Universidade Federal da Bahia, 
Brasil 

Wenceslao Machado de Oliveira Jr. Universidade 
Estadual de Campinas, Brasil 

Antônio A. S. Zuin Universidade Federal de São 
Carlos, Brasil