Does Texas’ compensatory education funding get to the intended students?


Journal website: http://epaa.asu.edu/ojs/   Manuscript received: 7/16/2020 
Facebook: /EPAAA  Revisions received: 12/8/2020 
Twitter: @epaa_aape  Accepted: 12/16/2020 
 

 

education policy analysis 

archives 
A peer-reviewed, independent,  
open access, multilingual journal  
 
  

 Arizona State University 

 
Volume 29 Number 36  March 22, 2021 ISSN 1068-2341 

 

Does Texas’ Compensatory Education Funding Get to the 

Intended Students? 

Laurence A. Toenjes 

University of Houston (Retired) 

United States 

Citation: Toenjes, L. A. (2021). Does Texas’ compensatory education funding get to the intended 
students? Education Policy Analysis Archives, 29(36). https://doi.org/10.14507/epaa.29.5786 
 
Abstract: Two questions about Texas school expenditure patterns are examined. First, “How 
progressive are spending patterns among high and low poverty schools?” Second, “How unequal are 
expenditures per pupil between schools with at least 70% of their students classified as economically 
disadvantaged, in different districts?” The data, for school year 2017-2018, are restricted to 3,453 
elementary and middle schools in 90 large Texas districts. The schools in each district were divided 
into high and low poverty groups. The differences in the average per pupil spending for operations 
between the two groups ranged from plus $1,382 in one district to a negative $802 in another. The 
average expenditures in schools with at least 70% economically disadvantaged students were 75% 
greater in one district than in another. It is demonstrated that districts with less extreme average 
levels of low-income students have more opportunity to act as good Samaritans, generally exhibiting 
substantially greater spending in their high poverty schools. This finding supports arguments for 
student funding weights that increase with increasing proportions of economically disadvantaged 
students. An incidental finding is that a commonly used measure of funding progressivity is a direct 
function of district and school level variances in poverty averages, and is therefore biased by them. 
Keywords: Educational finance; financial equity in education; Texas; intra-district equity; funding 
progressivity 
 
 

http://epaa.asu.edu/ojs/
https://doi.org/10.14507/epaa.29.5786


Education Policy Analysis Archives, Vol. 29, No. 36  2 

 

 

¿Los fondos de educación compensatoria de Texas van a los estudiantes previstos? 
Resumen: Este artículo examina dos preguntas sobre los patrones de gasto de las escuelas 
en Texas. Primero, “¿Cuán progresivos son los patrones de gasto entre las escuelas de alta 
y baja pobreza?” En segundo lugar, “¿Cuán desiguales son los gastos por alumno entre las 
escuelas con al menos el 70% de sus estudiantes clasificados como desfavorecidos 
económicamente, en diferentes distritos?” Los datos, para el año escolar 2017-2018, están 
restringidos a 3,453 escuelas primarias e intermedias en 90 distritos grandes de Texas. Está 
demostrado que los distritos con niveles promedio menos extremos de estudiantes de 
bajos ingresos tienen más oportunidades de exhibir un mayor gasto en sus escuelas de alta 
pobreza. Este hallazgo respalda los argumentos a favor de las ponderaciones de 
financiación de los estudiantes que aumentan con las proporciones crecientes de 
estudiantes en desventaja económica. Un hallazgo incidental es que una medida 
comúnmente utilizada de la progresividad del financiamiento es una función directa de las 
variaciones de los promedios de pobreza a nivel de distrito y escuela y, por lo tanto, está 
parcial. 
Palabras claves: Finanzas educativas; equidad financiera en educación; Texas; equidad 
dentro del distrito; progresividad del financiamiento 
 
Os fundos de educação compensatória do Texas vão para os alunos pretendidos?  
Resumo: Este artigo examina duas questões sobre os padrões de gastos escolares no 
Texas. Primeiro, "Quão progressivos são os padrões de gastos entre escola s de alta e baixa 
pobreza?" Em segundo lugar, "Quão desiguais são os gastos por aluno entre escolas com 
pelo menos 70% de seus alunos classificados como economicamente desfavorecidos, em 
diferentes distritos?" Os dados, para o ano escolar de 2017-2018, são restritos a 3.453 
escolas de ensino fundamental e médio em 90 grandes distritos do Texas. Distritos com 
níveis médios menos extremos de alunos de baixa renda mostraram ter mais oportunidades 
de exibir gastos maiores em suas escolas de alta pobreza. Essa d escoberta apóia o caso de 
pesos de financiamento de estudantes que aumentam com proporções crescentes de 
estudantes economicamente desfavorecidos. Uma descoberta acidental é que uma medida 
comumente usada de progressividade do financiamento é uma função direta das variações 
nas médias da pobreza no nível distrital e escolar e, portanto, é tendenciosa.  
Palavras-chave: Finanças educacionais; equidade financeira na educação; Texas; equidade 
dentro do distrito; progressividade do financiamento 

 

 

  



Education Policy Analysis Archives, Vol. 29, No. 36  3 

 

 

Introduction 

The State of Texas distributes approximately $4.0 billion each year to school districts based 
on the numbers of economically disadvantaged (ED) students that attend its schools. The purpose 
of these funds is to help reduce the academic achievement gap between ED and non-ED students 
(Texas Education Agency, 2020a). Students classified as economically disadvantaged were those who 
were eligible for the federal free or reduced-price lunch (FRPL) program. Apart from the question as 
to whether this amount of additional funding is sufficient to significantly improve the performance 
of ED students, there is also the factual question as to whether these funds, distributed by the state 
to school districts, reach the schools that the qualifying students actually attend. 

One might think that answering this question should be fairly straight-forward, given the 
financial detail found in the Texas PEIMS (Public Education Information System) data system, and 
the fact that there is a provision in the law that permits the Texas Education Agency (TEA) to 
conduct periodic studies to see if school districts are complying with the intended use of the 
compensatory education funds. Chapter 109, Section 109.25 of the Texas school code gives the 
authority to perform audits of the use of the state compensatory education funds (Texas Education 
Agency, 2020b).  If audits are undertaken, paragraph (f) states that TEA will issue a final report. In 
response to a Public Information Request for examples of such reports, the representative for TEA 
replied that “TEA has conducted a good faith search for any and all information related to your 
request and has not been able to locate information that may be responsive to your request.” This 
apparent lack of oversight is one of the reasons why this study was undertaken. 

The specific purpose of this paper is to address two questions. The first question is, to what 
extent do schools with greater proportions of ED students exhibit greater total operational 
expenditures per pupil than other schools in the same district with lower levels of ED students? The 
observations presented below will shed light on the extent to which these additional compensatory 
education funds actually get to the schools that the qualifying students attend. 

The second question is, how unequal are expenditures per pupil in schools with at least 70% 
of their students classified as economically disadvantaged, comparing the averages for these schools 
within each district to those in the other districts? These comparisons are, therefore, between the 
high poverty schools across all of the 90 districts whose data are used in this study. There were 79 
districts that had one or more schools with ED rates of at least 70%.  

In the course of addressing the first of these questions, a side question arose with respect to 
an apparent problem with one of the commonly used indexes that attempts to quantify the degree of 
a positive relationship between the percentages of economically disadvantaged students and the 
levels of per student funding. The positive or negative relationship between poverty and funding is 
referred to as the degree of progressivity of a state funding system, when school districts are the foci 
of interest, or the degree progressivity of school districts, when schools are the foci of interest. 

Policy Background 

The underlying concern of this paper is the continuing academic achievement gaps 
between groups of students from different socioeconomic backgrounds. The academic 
performance differences between schools with low percentages o f economically disadvantaged 
students and those with high percentages of such students are plainly visible within the data for 
Texas elementary and middles school students used herein . Comparing the lowest and highest 
quintiles of students, based on poverty, one sees the following:  for the lowest poverty quintile, 
the average pass rate on Texas’ STAAR1 test is 91%; for the highest poverty quintile, the 



Education Policy Analysis Archives, Vol. 29, No. 36  4 

 

 

average pass rate is 70%. STAAR is the acronym for the State of Texas Assessments of 
Academic Readiness. STAAR1 refers to test results based on the least demanding scoring 
criterion, namely “Approaching Grade Level.” 

The goal of ensuring that all students received at least comparable levels of resources 
was the original purpose of the profession of school fin ance (Cubberley, 1905). The early focus 
was on what came to be called wealth neutrality, designing school finance formulas to channel 
state money to districts in such a manner as to offset differences in property values per student, 
the primary funding base for most school districts. More recently, attention has also focused on 
vertical equity, which attempts to guarantee that students with greater academic deficiencies 
receive greater amounts of funding designed to eliminate or overcome those deficiencies (Berne 
& Stiefel, 1999). Inevitably the concern with vertical equity has moved the focus somewhat away 
from inter-district equity concerns to intra-district concerns, as in many cases students with 
greater needs tend to be concentrated in different schools within the same districts. A tension 
has thus developed between those still most concerned with inter -district, indeed interstate, 
funding differences, and those focusing more on intra-district disparities (Baker & Welner, 2010; 
Hill et al., 2008). 

Inter-District versus Intra-District Equity Issue 

The specific focus in this paper is on the differences in spending patterns within each of the 
90 of the largest school districts in Texas, with particular attention given to spending differences 
related to poverty differences. When I became curious about the extent to which dollars generated 
within Texas’ school funding formula for low-income students actually reach those students, and 
learned that the Texas Education Agency apparently does not even monitor the distribution of those 
funds, my curiosity increased even more. 

Analysis and discussion of intra-district expenditure differences should not diminish the 
importance of inter-district expenditure differences. In considering either, a rational comparison 
must acknowledge differences in the socioeconomic characteristics of the schools and districts as 
well as other cost-related factors.  

While considerations of both types of differences are valid and important, the two 
perspectives are not symmetric. Achieving more cost-based inter-district revenue equity requires the 
heavy lifting. Either more state revenues must be provided and the school finance formula revised to 
distribute the new revenues appropriately, or, perhaps even more difficult, the existing amounts of 
state revenues would have to be redistributed. An even more extreme solution would be to 
redistribute local revenues, as is done to some degree in Texas with the school finance formula’s 
recapture provision. If the additional state revenues to achieve inter-district equity are forthcoming, 
then existing intra-district expenditure inequities could also likely be achieved with less parent and 
local taxpayer objections. If state governments were to impose rules and regulations forcing the 
achievement of intra-district equity without providing new revenues, local resistance to redistributing 
the existing fixed amounts within each district would likely be very strong. And achieving intra-
district equity, district by district, would not alleviate any existing inter-district inequities. 

Bruce D. Baker and others have been highly skeptical of some of those who have focused 
on intra-district inequities. After providing a summary of previous studies of the intra-district equity 
issue, Baker and Welner critique several on their merits (2010, p. 2): 

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 



Education Policy Analysis Archives, Vol. 29, No. 36  5 

 

 

school district resource allocation practices between individual schools (see, for 
example, McClure et al., 2008; Public Impact et al., 2008). 

Baker also took issue with two other studies that focused on alleged intra-district inequities 
(Baker, 2017). One of these came out of the Buckeye Institute in Ohio (Carr et al., 2007); the other 
was published by Families for Excellent Schools (Families for Excellent Schools, 2015). In both of 
these Baker took specific issue with their faulty methodology. He also made the assertion that “Over 
the next decade through the late 2000s, within district inequality became a convenient scapegoat 
issue for federal policymakers, informed by beltway think tanks.” (Baker, 2017). 

It is Baker’s contention that all four of the above-cited studies placed too much emphasis on 
intra-district expenditure differences and in doing so neglected or diminished inter-district 
disparities. As mentioned previously, sidestepping or ignoring remaining inter-district revenue 
disparities, whether in one state or among states, is setting aside the most difficult and costly task. 
The question is, what might account for the focus in intra-district revenue disparities while ignoring 
the remaining substantial inter-district and interstate differences? One possible explanation is that 
several of the organizations involved, and many of the foundations that support them, are 
supporters or members of the conservative and corporate oriented State Policy Network and the 
related American Legislative Exchange Council. For example, the authors of the four works cited 
above by Baker and Welner are associated with or received funding from at least 20 foundations that 
have provided funds to organizational members of the State Policy Network. These relationships are 
documented in Appendix C. In addition, papers contained in Mapping Corporate Education Reform: 
Power and Policy Networks in the Neoliberal State also explore the relationship between education policy 
and corporate-funded support (Au & Ferrare, 2015). 

Funding Progressivity Issue 

 In addition to inter-district versus intra-district considerations a related issue is that of the 
degree of funding progressivity of states and school districts. Progressivity is a measure of the degree 
to which states, or school districts, direct greater resources to schools with relatively greater 
proportions of ED students. The degree to which expenditures within Texas school districts are 
concentrated on schools with larger proportions of economically disadvantaged students is a central 
focus of this paper. One of the measures that has been used to evaluate statewide educational 
funding progressivity, for all states, will be employed below as a benchmark to this paper’s findings.  

Some studies that examined the issue of funding progressivity have broadened the concept 
to include measures of progressivity as related to the proportions of minority students. Minority-
based funding progressivity is not explicitly considered here. Minority status, as such, plays no direct 
role in the distribution of funds to Texas schools, though funds for Limited English Proficient 
students might be put in this category. However, it is noted that the correlation coefficient between 
the percentages of White students and the percentages of ED students in the data analyzed here has 
a value of –0.863, meaning more White students, less poverty, or fewer White students, more 
poverty. The same measure for Hispanic students is 0.753, and for Black students 0.151. It should 
therefore be no surprise that in a number of the districts included in this study that allocate 
significantly more dollars per pupil to schools with high levels of ED students, the students in those 
schools are predominately Black and Hispanic students.  

Recent work by Sean Reardon and his associates at Stanford Center for Education Policy 
Analysis has shed new light on the relationship between poverty and educational performance. 
Using “…8 years of data from all public school districts in the U.S.” they reached the following 
conclusion:   



Education Policy Analysis Archives, Vol. 29, No. 36  6 

 

 

The association of racial segregation with achievement gaps is completely accounted 
for by racial differences in school poverty: racial segregation appears to be harmful 
because it concentrates minority students in high-poverty schools, which are, on 
average, less effective than lower-poverty schools. (Reardon et al, 2019, p. 1)  

Data and Methodology 

Data 

As one of the objects of this study is to compare the degree of progressivity of expenditures 
among and within school districts in Texas, it was decided to restrict the analysis to districts with 
sufficient numbers of schools to make such comparisons meaningful. The minimum number of 
elementary and middle schools qualifying a district to be selected was set at 14. It was anticipated 
that the inclusion of high schools would complicate matters, for two reasons. First, many districts 
have established a variety of different types of high schools, ranging from magnet schools for the 
performing arts or STEM specializations, to those dealing primarily with students under 
adjudication. Second, from previous work, it was observed that rates of participation in the federal 
free or reduced price-lunch (FRPL) program often dropped substantially among high school 
students, even though their families’ incomes made them eligible. This would have introduced an 
inconsistency in this poverty measure, which forms the basis for measuring economic progressivity. 
In addition, it is the author’s opinion that the earliest grades are the most critical in addressing the 
educational needs of economically disadvantaged students. Therefore, determining the extent to 
which school districts direct more funds to their higher poverty schools in the pre-high school 
grades is especially critical.  

Even when restricted to elementary and middle schools within school districts that 
contained at least 14 such schools, the resulting data set is substantial, comprising 3,453 schools in 
90 districts, with a total enrollment of 2,365,078 students. These schools included 69% of all 
elementary and middle school students enrolled in grades 1 through 8 in school year 2017-2018 in 
public schools in Texas. This number of students—2,365,078—exceeds the total number of 
students in all but three other states in the US. So even though not all Texas school districts and 
schools are included in the data being examined, the data does include a consequential number of 
districts, schools, and students. Table 1 presents data items on several subsets of Texas school 
districts. 

The dollar amounts used to compare per pupil spending levels between low-poverty and 
high-poverty elementary and middle schools are Total General Funds Program Expenditures 
(Objects 6100-6400 only), for the school year 2017-2018. The school-level expenditure data were 
downloaded from the TEA website which makes available accounting data for all schools and 
school districts (Texas Education Agency, 2020c). Amounts to maintain and operate the school 
facility itself (Plant Maintenance/Operations) were subtracted from those amounts. Federal 
revenues are not included. Non-federal compensatory education funds that are related to the 
incidence of economically disadvantaged (ED) students are included. The expenditure amounts 
reported by schools were not adjusted for possible differences in wage costs in different parts of the 
state, as the comparisons of most interest are those between groups of schools in the same districts 
and therefore in the same labor markets. 



Education Policy Analysis Archives, Vol. 29, No. 36  7 

 

 

Table 1 

Enrollment Data for Several Groupings of Texas School Districts 

Type or enrollment 
groupings 

No. of 
districts 
or charter 
systems 

No. of 
schools 

Enrollment Enrollment as % 
of regular district 
enrollment 

% economically 
disadvantaged 
students 

All districts and 
charters 1,200 8.759 5,385,012 105.8 58.8 

All charters  177 705 296,213 5.8 67.5 

All regular districts  1,023 8,054 5,088,799 100.0 58.3 

Regular districts less 
than 1,600 642 1,534 382,463 7.5 58.1 

Regular districts 
between 1,600 and 
5,000 

197 1,45 551,017 10.8 59.9 

Regular districts 
greater or equal to 
5,000 

184 5,475 4,155,319 81.7 58.0 

90 districts with at 
least 14 elementary 
and middle schools 
(this study) 

90 3,453 2,365,078 46.5 58.9 

Note: First 7 rows of data taken from Texas Education Agency (2018f). Final row’s data obtained from Texas 
Education Agency (2020c). 

 
Texas school district funding calculations begin with a per pupil funding factor called the 

Basic Allotment. The Basic Allotment is modified in several steps, first using districts’ Cost of 
Education Indexes. Then a scale or enrollment factor is applied for districts with fewer than 5,000 
students. Since all of the 90 districts considered in this study had more than 5,000 students, this 
adjustment for small or mid-sized districts was not applicable. These adjustments to the Basic 
Allotment result in a funding parameter called the Adjusted Allotment (AA). The AA is then 
multiplied by the number of regular students (those with no additional weighting factors) to 
determine the largest grant line item, and also by the numbers of full time equivalent students in 
special programs where add-on weights are applied. One of these programs is the compensatory 
education program, with the additional weight of 0.2. The number of compensatory education 
students is based on the numbers of students who qualified for the free or reduced-price lunch 
program during the prior year. In the school year 2017-2018, the year corresponding to the data 
being used, 58.8% of the 5.0 million students in all districts, including charter schools, qualified for 
compensatory education funding. 

The sum of all of the allotments just described totaled $37.3 billion for the 2017-2018 school 
year. The amount for compensatory education was $4.0 billion. The total allotment amount for each 
district is first funded by local maintenance and operations (M&O) tax revenues, with any shortage 
made up by the state. This is the calculation that gives rise to that special feature of Texas school 



Education Policy Analysis Archives, Vol. 29, No. 36  8 

 

 

finance. If the local revenue from a district’s M&O tax rate exceeds the total of all allotments to 
which it is entitled, the negative difference, with some refinements, is submitted to the state in the 
form of a recapture payment. There are also two “enrichment tiers” on top of the Tier 1 just 
described, which generate some additional state dollars for districts that adopt M&O tax rates above 
the threshold rate of $1.04 per hundred dollars of equalized assessed valuations. A final twist is that 
local funds raised with tax rates between 1.04 and 1.07 are not subject to recapture, no matter how 
property wealthy a district might be, but additional monies raised with tax rates above 1.07 to the 
maximum permissible 1.17 are, again, subject to recapture, and are matched at a lesser level. 

The add-on compensatory education funds generated by the 0.2 additional funding weight is 
the main focus of this paper. The total amount of the extra 20%, if used effectively, could have 
significant effects on the educational outcomes of the eligible students, namely those whose family 
incomes qualified them for the federal free or reduced-price lunch (FRPL) program. Researchers 
Jackson, Johnson, and Persico (2016) found that “the estimated effect of a 22.7 percent increase in 
per-pupil spending throughout all 12 school-age years for low-income children is large enough to 
eliminate the education gap between children from low-income and non-poor families” (p. 26). 

If results such as those just noted can be achieved with additional funding of 22.7 percent, 
which is just over the additional amount provided to FRPL-eligible students in Texas, it is especially 
important to confirm that these funds are being used appropriately. Such concern should not be 
seen as diminishing the importance of questions of inter-district funding equity. While it may be 
easier for a well-funded district to be more generous to its higher poverty schools, it is still necessary 
to ensure that those funds generated for compensatory education programs in all districts are used 
for the intended purposes. 

The state permitted up to 48% of the compensatory education funds allocated to districts to 
be used for overhead expenses. The remaining 52% were to be used for the benefit of at risk 
students, educationally disadvantaged students, and those at risk of dropping out.1 A single ED 
student increases the funds going to a typical district by approximately $1,100.  

Methodology 

Using the data described, schools with large proportions of ED students should show higher 
per pupil expenditures than schools with low proportions of ED students, on average, since it can 
be expected that the revenues in support of “regular students” are distributed among schools on 
approximately a per capita basis. Thus, students not in the ED category should not be the source of 
significant per pupil funding differences between schools in the same district. It is possible, of 
course, that some campuses could have greater proportions of higher salaried teachers. This issue is 
not examined here. A higher incidence of students receiving special education services might also 
affect observed expenditure levels. The incidence of Limited English Proficient (LEP) students is 
also ignored. In the aggregate, funding for LEP students is only approximately one-eighth as great as 
the funding for ED students. The following steps describe the procedures used. 

Schools designated by the Texas Education Agency (TEA) as Elementary and Middle are 
examined, restricted to the 90 districts with at least 14 middle and elementary schools, combined.  

 Within each of the 90 districts, the schools were sorted, from low to high, on the basis of 
the percent of ED students in each. Those schools containing up to but not including fifty percent 
of the elementary and middle school students in each district with the highest levels of poverty 

                                                        
1 The TEA school finance calculated amounts for the entire state or for individual districts are available from 
the Texas Education Agency (Texas Education Agency, 2020d). 



Education Policy Analysis Archives, Vol. 29, No. 36  9 

 

 

comprise Group 2; the remainder comprise Group 1. These two groups will be referred to as the 
High Poverty and Low Poverty groups in each district. 

There are three key relevant pieces of data for each school: the school enrollment; the 
percentage of students eligible for the free or reduced-price lunch program (ED students); the Total 
General Fund (GF) expenditures, minus expenditures for maintenance. The means of the 
proportions of ED students, and the means of the GF expenditures for Group 1 and Group 2, in 
each district, are calculated, with enrollments used to determine the means. i.e., weighted by 
enrollments. 

The differences in the means of the GF Expenditures per pupil for the two groups in each 
district indicate the extent to which districts direct more, the same, or fewer dollars to the campuses 
in each group. These mean differences thus reflect the extent to which districts allocate more funds, 
or fewer funds, to those campuses with higher rates of ED students. 

In addition to the differences in the means between the low-poverty and high-poverty 
groups in each district, estimates (described below) were made of how much more, on average, 
campuses in Group 2 should receive in compensatory education (CE) funds than should be received 
by campuses in Group 1.   

  Since the two groups of schools within each district, divided on the basis of an increasing 
incidence of ED students, have approximately the same total number of students, the estimate of 
how much more compensatory education revenues per pupil should be received by Group 2, as 
compared to Group 1, can be written as: 

 
Calculated Difference (per pupil) = (ED2% - ED1%) x AA x 0.2/100.                           (1) 
 
The term AA, described earlier, is the Adjusted Allotment. Also, as pointed out above, the 

state statute actually permitted up to 48% of the CE entitlements to be used for various overhead 
expenses. (This was reduced to 45% in 2019.) Therefore, for a given district the difference between 
the two groups in the amount required to be spent directly on programs benefiting the educational 
outcomes of the ED students who generate those funds can be estimated by multiplying the amount 
calculated in Equation 1 by the factor 0.52. The extent to which districts take advantage of this 
provision may be one of the factors that affect the results. 

Results and Analysis 

The basic results for questions 1 and 2 will first be presented, after which several specific 

problems will be analyzed. 

Results 

Results, Question 1: What is the extent of differences in expenditures between high and low 
poverty schools in the same districts?  

The numerical results for all 90 districts are shown in Appendix A. A summary, in the form 
of a histogram, is shown in Figure 1. As can be seen in that figure, in 17 districts per pupil 
expenditures are actually greater, on average, in schools with lower percentages of ED students than 
in their higher poverty counterparts within the same districts. In these 17 districts the differences 
between spending in the higher versus the lower poverty groups are negative. It can also be 
observed in Figure 1 that in seven districts the expenditures per pupil in the higher poverty group 
exceed those in the lower poverty group by $1,000 or more. The arithmetic average amount by 



Education Policy Analysis Archives, Vol. 29, No. 36  10 

 

 

which expenditures per pupil in the higher poverty groups exceeded those in the lower poverty 
groups was $368. The corresponding enrollment-weighted average difference is $414 per pupil. 

Figure 2 exhibits the actual expenditure differences between each district’s high and low 
poverty groups (vertical axis) plotted against the calculated differences, using Equation 1 (above). 
The estimated amounts are not reduced by the permissive 0.48 factor mentioned previously. In 
Figure 2, for districts that fall on or near the diagonal line, the actual expenditure differences and the 
estimated differences are equal. For those, such as Round Rock ISD and Katy ISD, the amounts by 
which expenditures in their higher poverty schools exceed expenditures in their lower poverty 
schools are substantial, $1,382 in the case of Round Rock ISD.2  On the other hand, in districts, 
such as Richardson ISD and Donna ISD, substantially greater expenditures per pupil occur in their 
lower poverty schools than in their higher poverty schools.  
 
Figure 1 

Mean Differences in General Fund Expenditures per Pupil between Low and High Poverty Texas Middle and 

Elementary Schools, by District  

 
 

                                                        
2 The comparable numbers for all 90 districts are contained in Table A1. 



Education Policy Analysis Archives, Vol. 29, No. 36  11 

 

 

Figure 2 

District Average Actual Expenditure Differences between High and Low Poverty Schools versus Estimated Average 

Expenditure Differences between High and Low Poverty Schools 

 

 
 
 
In Figure 3 the expenditure differences between the high poverty and low poverty schools 

within each district are presented in an alternative fashion, by first sorting the districts from high to 
low based on those expenditure differences. Districts at the extremes of this distribution are 
highlighted and aggregated results displayed beneath the graph, for each group. The two groups 
highlighted each contain approximately 22% of all middle and elementary students in the 90 districts 
being examined. The data line labeled SEQ 1 (row 1) applies to the districts with greater differences 
in expenditures favoring their high poverty schools. Each of the data column headings is now 
described and the data commented upon. All data are for the school year 2017-2018. 

 



Education Policy Analysis Archives, Vol. 29, No. 36  12 

 

 

Figure 3 

Expenditure Differences between High and Low Poverty Schools, Averaged by District for 90 Large Texas School 

Districts 

 
 
 
 
 
 
 
 
 

Description of data elements beneath Figure 3: 
 
NBR: The number of school districts included in summary data in each row. 

EX_HIPOV:  General funds operations expenditures per pupil, averaged for the higher 
poverty schools in each district and then averaged for the 17 districts in row 1 and the 26 
districts in row 2.  

EX_LOPOV:  Same as previous, except the expenditure amounts for the lower poverty set 
of schools in each district were averaged, and these in turn averaged over the districts 
include in each row.  

DIFF:  The differences in the first two columns. On average, the first group of 17 districts 
spent $1,025 more per pupil in their higher poverty schools. The other group, row 2, spent 
$124 less in their high poverty schools, on average. 

AVG_EXPP:  This column represents the average per pupil expenditures for operations in 
all of the elementary and middle schools in each district, which are then averaged over the 
districts that were selected in each row of data. As can be seen, the districts that were more 



Education Policy Analysis Archives, Vol. 29, No. 36  13 

 

 

generous with their higher poverty schools had higher average expenditures per pupil in all 
elementary and middle schools by $369 per pupil 

AVG_ED:  The average percent ED students for all elementary and middle schools. The 
47% figure is the average of the school averages in the 17 districts in row 1, and the 75% 
figure is the average ED for all schools in the 26 districts represented in row 2. 

DIFF_ED:  For each district, the average percent of economically disadvantaged students in 
the high-poverty and the low-poverty groups was calculated, and their differences 
determined. The value in this column for row 1—42—represents the average of those 
individual district’s differences for the 17 districts included in row 1. 

MKTVAL_PP:  The figures in this column represent the average of the equalized property 
tax base values in each set of districts. The higher average per pupil property values for the 
districts represented in row 1 may explain at least part of the higher AVG_EXPP for these 
districts. In spite of the highly equalized nature of Texas’ school finance system, there 
remains an advantage for districts with higher property valuations. Also, but not shown, the 
average of the maintenance and operations tax rates for the districts in row 1 are slightly 
greater than the average for the districts in row 2. 

An explanation of the variations in expenditure differences between the high-poverty and 
low-poverty groups in each district will be presented below, after the results from question 2 are 
presented.  

Results, Question 2:  How much do expenditure levels in schools with at least 70% 
economically disadvantaged students vary among districts?  

The differences in expenditures per pupil between high and low poverty schools described 
above are interesting in their own right, and perhaps also say something about priorities in the 
various districts. But the amounts actually spent in the highest poverty schools are of equal if not 
greater concern, bearing upon the issue of the absolute level of expenditures in high poverty schools. 
In addressing this issue, the average expenditures per pupil in schools with at least 70% ED students 
were calculated for such schools in each district. These amounts are also included in Table A1 
(Appendix A) 

Seventy-nine of the 90 districts had at least one school with ED rates exceeding 70%. The 
average expenditures per pupil in these schools, calculated for each of the 79 districts, are displayed 
in Figure 4, with several districts identified. As can be seen, the average expenditures per pupil 
amounts for schools with at least 70% ED vary from $8,848 per student in Plano ISD (6 schools) to 
$5,058 per student in Beaumont ISD (16 schools). The per pupil expenditure level in Plano ISD’s 
highest poverty schools exceeded that in Beaumont ISD by 75%. The data beneath Figure 4 shows 
that the level of operation expenditures per pupil in the schools in Plano with more than 70% ED is 
$8,848, $1,858 more than the average for all of its elementary and middle schools. In Beaumont 
ISD, where 16 of its 20 elementary and middle schools have ED rates above 70%, the operations 
expenditures per pupil in the schools with more than 70% ED is just $5,058, nearly $4,000 per 
student less than in the comparable schools in Plano ISD. Plano ISD benefits from a much greater 
property tax base per pupil, but it is also exerting the maximum maintenance and operations tax 
effort permitted under state law, at a rate of $1.17 per hundred dollars of assessed valuation. 

 



Education Policy Analysis Archives, Vol. 29, No. 36  14 

 

 

Figure 4 

Expenditures per pupil in schools with 70% or greater economically disadvantaged students, averaged for all such 

schools in each district 

 
 

 

Descriptions of data elements beneath Figure 4 not previously defined. 

OPS_GE70 represents the average level of general fund operations expenditures within 

schools with an average level of economically disadvantaged students of at least 70%. 

MO18 is the property tax rate for maintenance and operations purposes in the school 
district. The other column headings were defined previously. 

 
 



Education Policy Analysis Archives, Vol. 29, No. 36  15 

 

 

Analysis of Results and Problems 

Analysis of differences in expenditures between high-poverty and low-poverty schools. 

The basic results of comparing expenditures per pupil in the high-poverty and low-poverty 
groupings of elementary and middle schools in each of the 90 school districts were presented above. 
The results were summarized in Figures 1, 2, and 3, and the numerical results are contained in 
Appendix A for each district. This section attempts to explain those results. 

The differences in AVG_ED and DIFF_ED for the districts represented in the 2 rows of 
data beneath Figure 3 appear to be critical in influencing the observed differences in expenditures 
between the high-poverty and low-poverty schools in these two district groupings. Districts with 
below average rates of ED students overall and with a relatively wide range of variation in ED 
among its schools have considerably greater flexibility in reallocating resources from its low-poverty, 
academically high performing schools to its high-poverty, lower performing schools. For example, 
Aldine ISD has an overall ED rate of 90% and a difference of just 6% between the ED rates in its 
high poverty and low poverty schools. Hence, all of Aldine ISD’s schools have very high poverty 
rates, leaving virtually no opportunity to allocate more resources to its schools with the highest 
poverty levels. On the other hand, a district such as Fort Bend ISD, with an average ED rate of 
42%, and a difference in ED rates between its high poverty and low poverty schools of 44%, has 
much more opportunity to reallocate resources from its low poverty to its high poverty schools. And 
in fact, Fort Bend ISD does exactly that, with a spending advantage of $1,066 in its higher poverty 
elementary and middle schools. Many of the other districts that are located at the higher ranges of 
positive expenditure differences in Figures 2 and 3 do likewise. 

Three related scatter plots are shown in Figure 5, involving the relationships between 
DIFFHILO, ED_mean, and ED_sd., where ED_mean refers to the mean rate of ED students in a 
district, and ED_sd refers to the standard deviation of the mean ED rates of the schools in a 
district. Again, these data are for the 90 large districts being examined here. First, note the positive 
relationship between DIFFHILO and ED_sd in the upper left-hand scatter plot. Higher standard 
deviation (“socioeconomic segregation”) is related to more funds being directed to high poverty 
schools, as represented by DIFFHILO. Second, note that the relationship between DIFFHILO and 
ED_mean is generally negative, with some ambiguity on the left. Third, note the relationship 
between ED_sd and ED_mean. While the correlation coefficient between these two is just –0.505, if 
ED_sd is regressed on ED_mean, using a second-degree polynomial in ED_mean, the r-squared 
value is 0.568. This relationship shows a strong tendency for low ED_sd at low levels of ED_mean, 
low ED_sd levels also at very high values of ED_mean, but relatively high values of ED_sd for mid-
range values of ED_mean. The relationship peaks at a value for ED of 0.47.  



Education Policy Analysis Archives, Vol. 29, No. 36  16 

 

 

Figure 5 

Relationships among expenditure differences, poverty standard deviations and poverty means, 90 large Texas school 
districts (2018) 

 
 

If DIFFHILO is regressed on ED_sd and ED_mean, the coefficient for ED_sd is 26.867 

and that for ED_mean is –4.420 (see Table 2). The positive sign and high degree of significance of 

the regression coefficient for the district level standard deviations suggests that more variation in 

poverty among schools results in greater proportions of funds to poor schools. The question is, why 

is this so? 

 
Table 2 
Regression of differences in per pupil expenditures  (DIFFHILO) against means and standard deviations of school 
poverty in high and low poverty schools in Texas (2018) 

 

 Unstandardized 
Coefficients 

Standardized 
Coefficients t Sig. 

B Std. Error Beta 

(Constant) 199.
759 

188.961  
1

.057 
.

293 
ED_sd 26.8

67 
5.903 .451 

4
.552 

.
000 

ED_mean -
4.420 

1.891 -.232 
-

2.337 
.

022 
Dependent Variable: DIFFHILO Adj. R-sqr = 0.348 



Education Policy Analysis Archives, Vol. 29, No. 36  17 

 

 

While both independent variables in this regression are highly significant, the r-squared of 
just 0.348 indicates that other influences are at work. Adding the average expenditures per pupil for 
the schools in each district to the regression increased the r-squared only modestly, to 0.376. The 
coefficient of this additional variable, however, was significant (0.022). This suggests that the over-all 
level of resources may have some positive effect on greater expenditures in higher poverty schools, 
but the greater factor appears to be the degree of segregation of ED students among the various 
schools, as measured by ED_sd.  

The analysis to this point has consisted of calculating the average expenditures per pupil for 
the higher poverty and lower poverty groupings within each of the 90 school districts being 
examined. These groups were selected so that the numbers of students in each group, within each 
district, were approximately the same, as close as could be arranged given that individual schools 
were either totally in one group or the other. The differences in average expenditures per pupil in 
these groups were termed the HILO differences, within each district. The relationship of these 
differences to the districts’ average percentages of ED students was displayed in the upper right-
hand graph in Figure 5 above.  

The HILO differences can be viewed as a measure of expenditure progressivity. But how 
would this measure compare to one of the more commonly used and accepted measures? That 
comparison will be made. 

At least two such measures have been applied, either to all of the districts in all or most of 
the states in the US, or to the districts in an individual state. One of these, a regression-based 
approach, has been developed and promoted by Baker and Farrie (2010). Baker and Farrie include 
independent variables intended to correct for such factors as economies of scale, student 
geographical density, and their interaction. They also include measures of poverty and a comparable 
wage index. The purpose of the other variables—district size, geographical student density, and the 
comparable wage index—is to be able to estimate the poverty coefficient “corrected” for these 
influences. The resulting poverty coefficients, termed the Poverty multiplier, are listed, by state, in 
Table 4, State Progressiveness Factor (Baker & Farrie, 2010). 

The other method of assessing expenditure progressivity considered, which was developed 
and promoted by Chingos and Blagg (2017), involves calculating two weighted sums of each 
district’s per pupil expenditures. In one instance the weights are the numbers of poor students in a 
district; in the other the numbers of non-poor students are used as the weights. The index can be 
presented in two forms. Subtracting the weighted sums for nonpoor students from the weighted 
sums for poor students gives the index in difference form. Forming the ratios of the weighted sums 
for poor students over the weighted sums for nonpoor students yields the Chingos-Blagg 
progressivity index (CBI) in ratio form. 

The data involved in the calculation of the Chingos-Blagg index (CBI) correspond to that 
which is used in calculating the HILO expenditures differences described above. Since all of the 90 
districts being examined have enrollments of more than 5,000 students, none of the small or 
medium-sized adjustment factors used in the Texas school finance formula are relevant. The same is 
true for the sparcity factor. Additionally, as the basic units of analysis, schools, in each case are all 
within the geographical confines of their respective districts, including a comparable wage index is 
also not relevant. These considerations suggested that the refinements that Baker used in his 
regression approach are unnecessary. As the required data elements to calculate the CBIs are 
coincident with the data used to calculate the HILO expenditure differences described earlier, the 
Chingos-Blagg progressivity measure seemed to be a natural choice to use as a check on them. 
Instead of using district level expenditure and poverty data, necessary for calculating a state level 



Education Policy Analysis Archives, Vol. 29, No. 36  18 

 

 

progressivity measure, the CBIs will be calculated with school level expenditure and poverty data, 
for each district, giving district measures of expenditure progressivity. 
 

Comparison of the HILO Expenditure Differences with the Chingos-Blagg Measure of 
Expenditure Progressivity  
 

Chingos and Blagg (2017), in addressing the issue of equity in the financing of ED students, 
described the calculation of the CBI as follows: 

Specifically, for each state, we calculate a weighted average of each district’s per-
student funding, where the weights are the number of poor kids in each district. We 
then calculate the same figure weighted by the number of nonpoor kids. 

Our progressivity measure for each state is the difference between the 
average funding for poor and nonpoor kids. For example, an estimate of $100 would 
imply that, on average, poor students attend districts that receive $100 more in per-
student funding than the districts attended by nonpoor students. (p. 3)  

The CBI for a state is based on the relevant data aggregated to the district level; applied to 
districts, as is the case here, the data will instead consist of school-level data. The CBIs were 
calculated for the 90 districts included in this study, based on the expenditures of their elementary 
and middle schools. In Figure 6 the differences in spending between schools in the high-poverty and 
low-poverty groups in each district are plotted on the vertical axis, and the districts’ CBIs, as 
calculated, are plotted on the horizontal axis. Note that the difference form of the CBI is used here.  
 

Figure 6 
District differences in per pupil expenditures in high-poverty and low-poverty schools versus Chingos-Blagg Progressivity 
Index: Texas’ large districts (2018) 
 

 
 
 
 



Education Policy Analysis Archives, Vol. 29, No. 36  19 

 

 

There is an obvious positive relationship between the HILO expenditure differences and the 
CBIs, and it appears somewhat non-linear. Ignoring the non-linearity, the Pearson coefficient of 
correlation between the two variables is 0.84. There are a number of districts with CBI values in the 
zero range, which would mean that per pupil expenditures are approximately the same in the high 
poverty and low poverty groups of schools within their respective districts.  

Chingos and Blagg noted the tendency for greater economic segregation among districts 
associated with higher levels of their index (Chingos & Blagg, 2017) They suggested that one reason 
for this was the possibility that states are in fact better able to direct funds to poor students if such 
students are more segregated by school districts. They described the difference between New York 
State and Florida in this regard. Florida has only 67 countywide districts. With so few districts, 
regions with extreme levels of ED percentages are averaged out, so to speak. Therefore, only 
relatively small variations in the percentages of ED students among those large districts are 
observed. The opposite, they argue, is true in New York. With 675 schools, less averaging of the ED 
rates among different localities occurs. 

Others have also observed that districts with greater variation in economically disadvantaged 
(ED) students among their schools exhibit greater per pupil spending levels in their higher poverty 
schools than in the lower poverty schools. For example, Shores and Ejdemyr (2017) stated: 
“Conversely, socioeconomic segregation positively correlates with the poor to nonpoor spending 
gap, meaning that in districts with greater concentrations of poor students in schools, those poor 
students receive a greater district share of resources” (p. 17). Note that “socioeconomic  
segregation” refers to different “concentrations of poor students.” The standard deviation of the 
schools’ ED rates in a district is used as a measure of socioeconomic segregation, herein. Likewise, 
Lane, Linden and Stange (2018) state, “We find that both economic and racial segregation are 
positively correlated with the extent of progressive within-district funding…” (p. 4), which is 
another confirmation of the tendencies for higher values of the CBI to be associated with greater 
degrees of economic segregation. 

Knight and Mendoza (2019) used Baker’s regression approach as well as the Chingos and 
Blagg weighted averages approach in their attempt at evaluating a major change to California’s 
school funding formula. In their comparison of results using the two methods they stated the 
following: “The weighted average approach is more sensitive to funding differences in more 
segregated states (and less sensitive in more integrated states) and thus disproportionately ‘rewards’ 
segregation” (Knight & Mendoza, p. 15). Keeping in mind that this use of the term “segregation” 
corresponds to either the variance or standard deviation of the mean levels of ED among the 
schools within districts focused on herein, this is one more observation that the CBI tends to be 
greater with greater levels of variance in ED among districts, if using district level data. It seems 
reasonable to assume that the same relationship between the values of the CBI indexes and 
variances of ED for the schools within individual districts would also be observed.   

In contrast, an analysis of school expenditures among Texas elementary schools by Ajwad 
(2006) found that spending is “…higher in neighborhoods where income is low and where the 
minority (in particular black) population is high. However, the magnitude of these spending biases is 
small.” (p. 563). Ajwad used a regression-based analysis, and made no mention of a relationship 
between expenditures and intra-district or inter-district variances of levels of economically 
disadvantaged students. This would reinforce observations by Knight and Mendoza that there can 
be significant differences in the two measures of progressivity. However, further analysis of those 
differences is beyond the scope of this paper. There is further analysis of the CBI index below, 
however, since that is the one chosen for comparison purposes here. 



Education Policy Analysis Archives, Vol. 29, No. 36  20 

 

 

The basic observation included in the four papers referenced above, that greater within-
district segregation is associated with more funds being allocated to schools with greater percentages 
of ED students, may be valid. That possibility is explored in the following, and a possible 
explanation for the observed relationship between greater segregation and greater values of the CBI 
will be offered. 

Figure 7 contains two graphs, with the graph on the left the same as that in Figure 6. The 
same two groups of districts are highlighted in both graphs, with distinctive shading for each group. 
The graph on the right hand side of Figure 7 shows points that represent combinations of district 
ED percentages (horizontal axis) and the standard deviations of the ED percentages (vertical axis) 
for the schools in each district, a repeat of the lower right-hand plot in Figure 5. The districts 
represented by the darker/red circles are some of those with CBIs near to or below zero in the 
graph on the left. The pattern is easy to visualize and to describe:  districts with very low average 
levels of ED, of necessity, consist primarily of schools with low ED levels, hence a low standard 
deviation. The same is true for districts with very high average levels of ED, with most of their 
schools having very high levels of ED and hence low standard deviations. Particularly in the latter, 
high ED case, such districts would have very little opportunity or motivation to attempt to move 
funds from one school to another. It is hard to imagine Harlandale ISD, for example, scraping 
significant amounts of funds from its lowest poverty school, with 85% ED students, in an attempt at 
improving conditions at another school in that district. All of the schools in Harlandale ISD would 
benefit from more funds and it is unlikely that any could escape negative consequences if funds were 
reduced. 

 
Figure 7 
 

Texas Large 90 Districts’ Differences in Expenditures per Pupil between High and Low Poverty Schools, and 
Relationship between Districts' Means and Standard Deviations among Schools in each District  
 

 



Education Policy Analysis Archives, Vol. 29, No. 36  21 

 

 

In contrast, the districts represented by the light gray/green circles in Figure 7 all had 
relatively high values of the CBI. The pictures tell the story very well, and the data beneath the 
graphs reinforce it. Districts in the highlighted low CBI group have a negative $112 per pupil 
average difference in expenditures between their higher poverty and lower poverty schools, with a 
standard deviation of ED percentages of 11 and a mean ED percentage of 77 (see Row 1/SEQ 1). 
In contrast, the districts selected in the high CBI group spend, on average, $1,085 per pupil more in 
their higher poverty schools. The standard deviation of the EDs for this group is 26, and the average 
ED rate is just 40%. 

The argument above is that in districts where all schools have uniformly high levels of ED, 
with little variation, there is little opportunity for districts to move resources from their lower 
poverty schools to their higher poverty schools—all have high rates of ED students. Therefore, such 
districts will be assigned low values of a progressivity index, such as the CBI. Thus far this argument 
has been based solely on the data for the elementary and middle schools in the 90 districts in Texas 
which have at least 14 such schools. An obvious question is, does the same pattern shown for Texas 
in Figures 5 and 7, namely the inverted u-shaped relationship between the standard deviations of the 
average levels of ED and the mean levels of ED among schools in various districts exist in other 
states? The answer is yes. 

The percentages of students qualifying for the free or reduced-price lunch program were 
obtained for all schools in all U.S. states, downloaded from the National Center for Education 
Statistics website.3 The results for six states, including Texas for reference purposes, are shown in 
Figure 8. The data used in creating the graphs in Figure 8 were restricted to regular, non-charter 
elementary and middle schools, and to districts that contained at least 10 such schools. 

The mere existence of similar patterns in the two variables plotted does not, of course, mean 
that the same association of the two values will result in the same levels of progressivity in other 
states as in Texas. That is, will combinations of very high values of mean ED and corresponding low 
values of the standard deviation of ED be associated with a low level of progressivity in other states? 
And will combinations of high standard deviations of ED and corresponding moderate levels of 
mean ED tend to result in higher progressivity measures, as they did for the 90 Texas districts? This 
is an empirical question that can only be answered by obtaining and analyzing the school level 
expenditure and poverty data for other states. 

 

                                                        
3 A single URL address for the data files could not be determined. To get to the free or reduced-price lunch 
school data, the steps are: (1) go to  http://nces.ezd.gov/ccd/ ; (2) select CCD Data Files; (3) select CCD 
Data File Tool; (4) in the three select boxes at the bottom of the page, select nonfiscal, school, and  year (of 
choice); (5) on the new  page which will be presented,  under Lunch Program Eligibility select Flat and SAS 
Files. The files, in zipped format, should then begin downloading. 

http://nces.ed.gov/ccd


Education Policy Analysis Archives, Vol. 29, No. 36  22 

 

 

Figure 8 
 

Relationship between Standard Deviations and Means of Poverty Percentages of Students in Schools in Districts in 
Six States 

Note: Graphs show only non-charter districts with at least 10 elementary and middle schools. Data Source: 
National Center for Education Statistics. See footnote 3 herein. 

 

Critique of Chingos and Blagg Progressivity Index 

Chingos and Blagg (2017) wondered if it might be possible that their “…progressivity 
measure is mechanically driven by student sorting across districts.” (, p. 13). They answered their 
own musing with the following: “We argue that, to the extent this is true, it largely reflects the 
degree to which segregation enables targeting of resources.” This goes back to their suggestion that 
New York, with its socioeconomically segregated districts, could better target funds to poor students 
than could a state such as Florida, with its county-wide districts. 

The possibility that the CBI might substantially be a function of “student sorting” is now 
examined. This is accomplished with a very simple model, as follows. Assume that an amount E is 
allocated to districts for each enrollee, and that an additional grant equal to rE,  with r>0, is awarded 
to districts for each economically disadvantaged or poor student enrolled. Equation 2 is a 
reproduction of Equation B10 in Appendix B, where it is derived, from the model based on these 
premises: 

 

         CBI = NNP [  NP(1 + rp)   +  r(nd)Var(pi)]  .                                                               (2) 
        NP   [ NNP(1 + rp)  –   r(nd)Var(pi)] 



Education Policy Analysis Archives, Vol. 29, No. 36  23 

 

 

Since the focus in this paper is upon the degree of progressivity in individual districts, 
Equation 2 will be interpreted from this perspective. Note, incidentally, that Equation 2 defines the 
CBI in the ratio form.  

In Equation 2, NNP is the total number of nonpoor students in all schools, NP the total 
number of poor students, p the average number of poor students in all schools in the district being 
considered, (nd) the number of schools in the district, and Var(pi) the variance of the proportions of 
poor students in the schools in the district.  

Note the following about Equation 2. (a) If r=0, there is no add-on grant to poor students, 
and CBI = 1; (b) if the average percent of economically disadvantaged students is the same in all 
schools in a district, Var(pi) would be zero, and the CBI would again be unity; (c) if r > 0 and Var(pi) 
is > 0, then the CBI for that district would be greater than unity. For a given average p across all 
schools, greater variance or “segregation” results in a greater CBI.4 The factor r, resulting in a grant 
of rE, represents the additional effort being made by a state to benefit poor students.  

Applied to the districts in a given state, it seems problematical to say that a state with greater 
variance in poor students among its districts, and hence a greater CBI, is thereby making a greater 
effort than a state with a lesser variance if both states provide the same additional amount, rE, for 
each poor student and both have the same average number of poor students, p, in their districts.  

It is hypothesized that a district with a moderate overall percentage of ED students, but 
which includes a large range of average levels of ED among its individual schools, will have more 
opportunity to allocate additional revenue per pupil to its high poverty schools than to its low 
poverty schools. The relationship between the means and the standard deviations of the percentages 
of ED students in the schools in the 90 Texas districts is displayed in the right-hand graph in Figure 
7. In particular, the lower SDs, measuring “socioeconomic segregation”, that is associated with 
higher district percentages of ED students reduces the flexibility of districts to direct larger amounts 
of resources to their higher need students. These districts will display minimal socioeconomic 
segregation, and hence minimal additional spending in their higher poverty schools. They will also 
tend to have a low measure of the CBI index. 

Conclusions 

Based on the data being analyzed here, it appears that the degree of progressivity in spending 
by schools within districts is the result of two factors, possibly a third. First, for school year 2017-
2018, the State of Texas not only used an add-on poverty factor of 0.2 in its funding formula, but it 
also imposed the requirement that at least 52% of the additional funds were to be used for the 
benefit of the students that generated those funds. Second, some school districts are better 
positioned than others to allocate more funds to their higher poverty schools. Many districts 
appeared to meet or exceed not just the 52% rule, but appeared to meet or exceed putting 100% of 
the additional compensatory education funds into their higher poverty schools. Several districts 
seemed to do the opposite, apparently taking advantage of lax state oversight. These results were 
depicted in Figure 2 above, where districts above the diagonal line were actually spending more in 
their higher poverty schools than was allocated by the state for compensatory education purposes. 
But the districts with the most progressive records also tend to have modest levels of ED students 
and relatively high standard deviations of ED students among their schools, as represented in Figure 

                                                        
4 A greater number of schools (nd), given the variance, would also increase the CBI. And of course, with a 
non-zero variance, any increase (decrease) in r would increase (decrease) the CBI.  



Education Policy Analysis Archives, Vol. 29, No. 36  24 

 

 

7. As described above, these characteristics are structural in nature, not specifically dependent on 
improved targeting of funds to students in poverty by state or district administrators. 

The possible third factor in explaining the degree of progressivity exhibited by an individual 
district is the district’s response to pressures imposed by the state’s academic accountability system. 
If the district administration perceives a possibility that increased resources to its higher poverty 
schools will improve their academic performance, and that reduced resources to its lower poverty 
schools will have negligible adverse impact on those schools’ performances, then it may undertake 
such a reallocation. This would also assume that any objections from stakeholders in the nonpoor 
schools could be managed.5  

It is clear, from Figure 2 above, that some districts spend less in their higher poverty schools 
than in their lower poverty schools. For districts with extremely high average levels of ED, 
comparisons of spending between those schools with very high rates of ED students and those with 
even higher rates may be moot. Such comparisons, and their degree of progressivity based on the 
CBI, may be irrelevant. This may also illustrate a shortcoming, or perhaps a misuse, of this index. 
The assigning of such indexes to districts, and even to states, carries with it the suggestion that some 
districts or states are doing better than others, in some sense, in addressing the needs of their more 
disadvantaged students. However, such a conclusion may not always be justified, as suggested by 
Equation 2 above. Instead of greater socioeconomic segregation allowing better targeting, the 
greater segregation, along with modest average levels of poor students, provides the opportunity for 
more progressive intra-district allocation of funds. Therefore, it appears that what is measured by 
the CBI reflects some combination of the following: (a) effort by the state, as measured by r; (b) 
segregation, or the variance of the percentages of economically disadvantaged students, among 
districts; and (c) genuine effort by many districts to allocate resources to schools with greater 
proportions of disadvantaged students. 

Some of the largest school districts in Texas appear to be making commendable efforts on 
their own initiative of directing more of their available resources towards their higher poverty, 
academically lower performing schools. These are the districts that lie at or near the top of the 
district plot in Figure 3. Many of the districts that fall near the lower levels depicted in Figure 3 tend 
to have (a) very high levels of economically disadvantaged students and, (b) correspondingly low 
differences in the levels of ED between the higher and lower poverty groupings of their schools, i.e., 
less socioeconomic segregation. For these districts, attempts to reallocate resources from schools 
with high levels of ED students to those with even higher levels of ED students is not a meaningful 
or practical approach to dealing with the problem of the need for more scarce resources in high 
poverty schools which are embedded within high poverty school districts. 

Related to the issue of large percentages of ED students is the fact that, on average, ED 
students are less prepared for school when they begin, and find it very difficult, if not impossible, to 
catch up, to erase the “gap.” That is why school districts that have lower average levels of ED 
students, and simultaneously a wide spread in ED averages among their schools can get by with 
skimping, to some degree, on schools with low levels of ED students and shifting more resources to 
the higher poverty schools. Non-poor students who attend schools with greater proportions of 
students with higher income, better-educated parents can likely endure somewhat larger class sizes, 

                                                        
5 Baker, studying what works in improving educational outcomes, concluded, “The available evidence 
suggests that appropriate combinations of more adequate funding with more accountability for its use may be 
most promising” (2019, p. i). One must be cautious, of course, about giving all the credit for positive efforts 
by school districts to the accountability system, as opposed to the professional expertise and personal values 
of the district administrators themselves. 



Education Policy Analysis Archives, Vol. 29, No. 36  25 

 

 

for example, without significant loss in academic performance. And even the ED students in these 
schools may benefit from peer effects of attending classes with generally higher performing students. 
But such opportunities to shift resources around just do not exist in many school districts. 

The inter-district differences in expenditure levels among schools with at least 70% of their 
students classified as economically disadvantaged is nothing less than astonishing. Whether the 
reasons for such differences are due to decisions made at the local level, the state level, or both, 
these differences need to be acknowledged, understood, and steps taken to reduce if not eliminate 
them. If it is the goal of the State of Texas to diminish the achievement gap between ED students 
and other students, it is highly inequitable and counter-productive if 75% more funds are directed to 
those students in one district than in another. These levels of inter-district variations are reminiscent 
of those that existed between districts before states began distributing revenues to offset large 
variations in property tax bases. That issue was acted upon by states after many years of litigation. 
Maybe more such litigation is required in this situation, also.6 

Much has been written about the debilitating effects of poverty upon educational 
performance. The Poverty & Race Research Action Council (undated) published an extensive 
bibliography that gives an excellent over-view of the literature on the topic. The direct, indirect and 
peer factors that negatively impact student learning in high poverty schools are widely recognized. 
Districts consisting entirely of high poverty schools are at a distinct disadvantage in coping with the 
effects of poverty upon student performance as compared with districts with more moderate levels 
of poverty. In 2019 the Texas Legislature modified the extra-funding weight to be applied to 
students classified as economically disadvantaged. The original weight, which was used in generating 
the compensatory education funding amounts that were in place during the 2017-2018 school year, 
upon which this study is based, was 0.2. This provided, essentially, an additional 20% funding for 
each qualified student. The single weight of 0.2 has been replaced by a set of five weights, varying 
from 0.225 to 0.275. These weights depend upon several socio-economic factors obtained from the 
U.S. Census. In addition, as previously noted, the 52% rule was changed to a 55% rule. As described 
by TEA in an information release:  

The statute requires that at least 55 percent of the SCE [State Compensatory 
Education] funds allocated must be used to fund supplemental programs and 
services designed to eliminate any disparity in performance on assessment 
instruments administered under the TEC, Subchapter B, Chapter 39, or disparity in 
the rates of high school completion between: (1) students who are educationally 
disadvantaged and students who are not educationally disadvantaged; and (2) 
students at risk of dropping out of school, as defined by the TEC, (sec.)29.081, and 
all other students.” (Texas Education Agency, 2020e) 

These are certainly laudable goals. However, an increase of $1 billion in compensatory 
education funds, spread over 3.6 million such students, with the option for districts to use 45% of 
those new funds for overhead purposes, leaves approximately $150 for each ED student in new 
funding to accomplish those goals. It is difficult to imagine that this amount of additional funding 
will result in a perceptible impact upon the achievement gap of economically disadvantaged students 
in Texas anytime soon. 

                                                        
6“Students, families, and other parties harmed by intradistrict funding disparities should use state courts and 
state constitutions’ education clauses to extend previous inter-district school funding victories and to force 
policymakers to implement more equitable intradistrict funding.” (Webb, 2017, p. 2169). 



Education Policy Analysis Archives, Vol. 29, No. 36  26 

 

 

Recommendations 

Recommendation 1: Student poverty weighting factors for districts with very high poverty 
levels should be increased.  

The relationship between the means of the percentages of economically disadvantaged 
students in the schools within districts, and the segregation or variance of those percentages within 
districts, seems helpful in understanding why expenditure progressivity varies among districts. 
Recognizing that not all districts have the same opportunity to reallocate resources to their schools 
with the highest proportions of economically disadvantaged students lends weight to arguments that 
districts with much higher proportions of ED students should benefit in an increasing, non-linear 
fashion. That is, greater weighting factors should be used in funding ED students in districts with 
very large proportions of such students.7 

Recommendation 2:  The Texas Education Agency (TEA) should monitor and report on the 
intra-district distribution of compensatory education allotments. 

In response to a public information request for copies of reports by TEA of school district 
utilization of compensatory education grants, the agency indicated that no such reports existed. The 
comptroller of a local district, one of the 90 districts included in this study, said that he could not 
recall his district’s use of compensatory education funds ever being audited during his 20 years with 
the district. 

Even a very cursory look at the expenditure patterns between high poverty and low poverty 
schools in some districts should set off alarm bells. An example is that of Richardson ISD. The 
outlier status of Richardson ISD is plainly visible in Figure 2. Richardson ISD has an overall rate of 
economically disadvantaged students slightly below the statewide average, and has a relatively high 
degree of segregation of poor students among its 48 elementary and middle schools, two 
characteristics of districts demonstrating more expenditure progressivity. Yet the lower poverty 
schools in Richardson ISD show expenditures of $841 per student greater than its higher poverty 
schools.  

Recommendation 3: The Texas Education Agency should specifically monitor the 
disparities in resources going to schools with at least 70% economically disadvantaged 
students. 

The extreme ranges in expenditure levels in schools with very high rates of economically 
disadvantaged students are just plainly beyond comprehension. The state cannot pretend to be 
serious about improving the relative performance of educationally disadvantaged students when high 
poverty schools in one district exhibit expenditure levels nearly $4,000 per student below those in 
another district. High poverty schools in all districts should be at or near the higher level. 

Recommendation 4: Additional analysis of progressivity indexes needs to occur.  

The analysis by Knight and Mendoza comparing two measures of funding progressivity, the 
regression approach by Baker, and the weighted sums approach by Chingos and Blagg, should be 
extended. Perhaps an examination of the regression approach, similar to that presented in this paper 

                                                        
7 Whether to recommend a non-linear poverty weight for the school finance formula for the State of 
Maryland was studied by Wool, Fermanich, and Reichardt  (2015). An annotated bibliography on the topic 
can be found in Poverty & Race Research Action Council (undated). 



Education Policy Analysis Archives, Vol. 29, No. 36  27 

 

 

for the Chingos and Blagg method, could be developed that would permit a more analytical 
comparison of the two indexes.  

Recommendation 5: Extend the analysis of the impact of intra-district poverty distributions  
upon district expenditure progressivity to other states. 

The same pattern of percentages of economically disadvantaged students within districts, 
plotted against the standard deviations of those percentages for the schools within districts, 
exhibited in Figures 5 and 7 above, are also exhibited in a number of other states. The patterns for 
five of those other states were presented in Figure 8. It would be interesting, using school-level 
expenditure data for states other than Texas, to determine if the same relationship between those 
factors helps explain the different degrees of school district expenditure progressivity in those states 
as well. 

Acknowledgements 

I would like to thank Dr. Catherine Clark, Texas Association of School Boards (retired), for helpful 
comments on an earlier draft of this paper. I would also like to thank my wife, Jean Garst, for her 
many hours of proof reading and helpful comments. 

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Education Policy Analysis Archives, Vol. 29, No. 36  30 

 

 

Appendix A 

 

Data for Texas School Districts with at Least 14 Elementary and Middle 

Schools, School Year 2017-2018 

The data column headings for Table A1, which begins on the following page, have the 
following definitions: 

N: District sequence number, districts in descending order by the amounts shown in the 
column designated as DIFFHILO 

DISTNAME: Official district name. 

GFOPSPP HIGH POV:  The average dollar amount of general fund operating expenditures 
per pupil for the higher poverty group of elementary and middle schools in each district. 

GFOPSPP LOW POV: The average dollar amount of general fund operating expenditures 
per pupil for the lower poverty group of elementary and middle schools in each district. 

DIFFHILO: These amounts are the differences of GFOPSPP_HP minus GFOPSPP_LP. 

EST. DIFF: These figures represent the estimated amounts by which general fund operating 
expenditures per pupil would be greater in the higher poverty schools in each district. 
Equation 1 in the text was used in calculating these estimates. 

EXPP GE70:  The figures in this column are the average amounts of general fund operating 
expenditures per pupil observed in the elementary and middle schools in each district in 
which at least 70% of their students were classified as economically disadvantaged. 

N_SCHS GE70: The numbers in this column represent the numbers of elementary and 
middle schools in each district with percentages of economically disadvantaged students of 
at least 70%. 

NA indicates the district had no elementary or middles schools with 70% of their students classified 
as economically disadvantaged, hence the data is missing. 
 
 
 
 
 
 
 
 
 
 
 
 



Education Policy Analysis Archives, Vol. 29, No. 36  31 

 

 

Table A1 

Expenditures per Pupil for High and Low Poverty Groups, and Schools with at Least 70% Economically 
Disadvantaged Students 

 

N DISTNAME 
GFOPSPP 

HIGH POV 
GFOPSPP 
LOW POV 

DIFF 
HILO 

EST. 
DIFF 

EXPP 
GE70 

N_SCHS 
GE70 

1 ROUND ROCK ISD 7,121 5,739 1,382 409 8,710 4 
2 KATY ISD 7,469 6,167 1,302 481 8,135 3 
3 LEWISVILLE ISD 7,843 6,643 1,200 556 8,072 6 
4 KLEIN ISD 7,078 6,002 1,075 355 8,457 6 
5 AUSTIN ISD 7,197 6,124 1,074 708 7,289 54 
6 FORT BEND ISD 7,149 6,083 1,066 499 7,728 15 
7 SPRING BRANCH ISD 6,695 5,672 1,023 659 6,633 17 
8 MCKINNEY ISD 7,533 6,542 991 414 8,515 4 
9 DENTON ISD 7,333 6,409 924 433 7,363 6 
10 PLANO ISD 7,436 6,544 892 426 8,848 6 
11 ALVIN ISD 7,157 6,271 886 350 7,075 1 
12 VICTORIA ISD 6,002 5,161 841 312 5,994 12 
13 NORTH EAST ISD 7,137 6,296 841 543 7,282 22 
14 LEANDER ISD 6,747 5,921 826 272 NA NA 
15 BRAZOSPORT ISD 6,828 6,006 822 342 7,072 4 
16 EL PASO ISD 7,182 6,379 803 394 7,068 52 
17 LUBBOCK ISD 6,575 5,782 793 450 6,548 25 
18 GRAND PRAIRIE ISD 7,065 6,273 791 243 6,674 22 
19 LAMAR CISD 7,069 6,281 787 535 7,795 9 
20 TOMBALL ISD 6,739 5,984 755 233 NA NA 
21 KILLEEN ISD 6,903 6,176 727 365 6,854 20 
22 SAN ANGELO ISD 5,280 4,583 697 325 5,409 9 
23 ALLEN ISD 7,107 6,421 685 208 NA NA 
24 HUMBLE ISD 6,814 6,135 678 412 7,296 4 

25 
EAGLE MT-SAGINAW 
ISD 6,621 5,979 641 203 7,052 1 

26 PFLUGERVILLE ISD 7,030 6,396 633 356 7,446 1 
27 YSLETA ISD 7,016 6,390 626 197 6,783 40 
28 JUDSON ISD 6,590 6,014 575 237 6,575 12 
29 SOCORRO ISD 6,216 5,648 568 191 6,140 24 
30 CONROE ISD 6,199 5,670 528 566 6,502 13 
31 AMARILLO ISD 6,562 6,051 512 455 6,454 32 

32 
CYPRESS-FAIRBANKS 
ISD 5,421 4,923 498 436 5,439 21 

33 GARLAND ISD 6,600 6,130 469 287 6,607 26 
34 FRISCO ISD 6,186 5,733 454 132 NA NA 
35 MCALLEN ISD 6,190 5,746 443 374 6,194 19 
36 NORTHSIDE ISD 6,646 6,204 442 457 6,772 31 
37 WYLIE ISD 6,399 5,965 433 163 NA NA 
38 KELLER ISD 6,792 6,361 431 314 NA NA 
39 HARLINGEN CISD 6,682 6,251 431 188 6,623 20 
40 CORPUS CHRISTI ISD 5,893 5,465 428 327 5,854 38 
41 GOOSE CREEK CISD 6,195 5,782 413 244 6,195 10 
42 MIDLAND ISD 5,821 5,428 393 346 5,558 8 



Education Policy Analysis Archives, Vol. 29, No. 36  32 

 

 

Table A1 (Cont’d) 
Expenditures per Pupil for High and Low Poverty Groups, and Schools with at Least 70% Economically 

Disadvantaged Students  

N DISTNAME 
GFOPSPP 

HIGH POV 
GFOPSPP 
LOW POV 

DIFF 
HILO 

EST. 
DIFF 

EXPP 
GE70 

N_SCHS 
GE70 

43 SAN BENITO CISD 6,150 5,773 377 95 5,958 14 
44 CLEAR CREEK ISD 6,147 5,772 375 310 6,810 2 
45 MANSFIELD ISD 6,280 5,934 346 353 5,817 2 
46 WICHITA FALLS ISD 5,984 5,650 334 350 5,984 10 
47 BROWNSVILLE ISD 6,242 5,918 325 69 6,065 47 
48 BRYAN ISD 6,573 6,256 317 250 6,482 13 
49 ABILENE ISD 5,670 5,364 307 193 5,618 14 
50 ALIEF ISD 6,555 6,248 306 103 6,434 35 
51 LAREDO ISD 6,381 6,082 299 53 6,231 24 
52 COMAL ISD 5,787 5,500 287 350 NA NA 

53 
PHARR-SAN JUAN-
ALAMO ISD 6,495 6,212 283 100 6,351 33 

54 
CARROLLTON-
FARMERS BRANCH IS 6,810 6,531 279 377 6,780 17 

55 NORTHWEST ISD 6,140 5,911 229 189 NA NA 
56 UNITED ISD 6,617 6,391 226 318 6,602 29 
57 SAN ANTONIO ISD 7,359 7,145 214 88 7,252 64 
58 WACO ISD 6,434 6,224 210 133 6,322 19 
59 COLLEGE STA. ISD 5,784 5,575 209 270 NA NA 
60 PEARLAND ISD 5,952 5,745 207 235 NA NA 
61 HAYS CISD 6,143 5,936 207 415 6,376 4 
62 NEW CANEY ISD 6,334 6,144 190 236 6,147 6 
63 HOUSTON ISD 6,078 5,889 189 345 6,077 166 
64 DALLAS ISD 6,650 6,496 154 180 6,548 175 
65 IRVING ISD 6,700 6,590 110 105 6,643 26 
66 BIRDVILLE ISD 6,321 6,228 93 388 6,265 13 
67 SOUTHWEST ISD 6,147 6,056 90 210 6,095 15 
68 TYLER ISD 5,831 5,766 65 383 5,798 15 
69 SPRING ISD 6,133 6,068 64 113 6,060 24 

70 
GRAPEVINE-
COLLEYVILLE ISD 6,750 6,686 64 348 7,539 1 

71 MISSION CISD 6,260 6,198 62 179 6,322 16 
72 CROWLEY ISD 6,560 6,521 39 202 6,323 6 
73 BEAUMONT ISD 5,069 5,060 9 261 5,058 16 
74 FORT WORTH ISD 6,063 6,075 -13 245 6,086 86 
75 GALENA PARK ISD 6,559 6,580 -21 96 6,578 19 
76 ROCKWALL ISD 5,755 5,800 -45 294 NA NA 
77 ARLINGTON ISD 6,565 6,617 -52 361 6,507 36 
78 LA JOYA ISD 6,373 6,432 -59 109 6,403 31 

79 
HURST-EULESS-
BEDFORD ISD 5,689 5,770 -82 305 5,755 4 

80 ALDINE ISD 6,315 6,423 -107 68 6,371 54 
81 PASADENA ISD 7,039 7,181 -142 180 7,174 45 
82 HARLANDALE ISD 6,551 6,795 -244 70 6,708 17 
83 EDINBURG CISD 6,632 6,876 -244 164 6,784 33 



Education Policy Analysis Archives, Vol. 29, No. 36  33 

 

 

Table A1 (Cont’d) 

Expenditures per Pupil for High and Low Poverty Groups, and Schools with at Least 70% Economically 
Disadvantaged Students  

N DISTNAME 
GFOPSPP 

HIGH POV 
GFOPSPP 
LOW POV 

DIFF 
HILO 

EST. 
DIFF 

EXPP 
GE70 

N_SCHS 
GE70 

84 ECTOR COUNTY ISD 5,358 5,641 -283 249 5,561 7 
85 MESQUITE ISD 5,793 6,143 -350 191 5,914 32 
86 DUNCANVILLE ISD 5,493 5,846 -353 118 5,603 14 
87 WESLACO ISD 5,887 6,255 -368 147 6,119 14 
88 EAGLE PASS ISD 5,812 6,225 -414 173 6,018 14 
89 RICHARDSON ISD 6,581 7,107 -526 492 6,594 17 
90 DONNA ISD 5,897 6,699 -802 63 6,322 18 

Note:  Figures in columns 3 – 7 in dollars. 



Education Policy Analysis Archives, Vol. 29, No. 36  34 

 

 

Appendix B 

Properties of the Chingos-Blagg Progressivity Index (CBI) 

The basic notion of the CBI, as stated by Chingos and Blagg, is quite straightforward: 

Specifically, for each state, we calculate a weighted average of each district’s per-
student funding, where the weights are the number of poor kids in each district. We 
then calculate the same figure weighted by the number of nonpoor kids. 

Our progressivity measure for each state is the difference between the 
average funding for poor and nonpoor kids. For example, an estimate of $100 would 
imply that, on average, poor students attend districts that receive $100 more in per-
student funding than the districts attended by nonpoor students. (Chingos and Blagg, 
2017, p. 3.) 

 
The CBI will first be discussed and applied at the state/district level, focusing on the state’s 

distribution of allotments to individual school districts. The district/school distribution, and the 
associated CBI, will also be briefly discussed. 

For ease of exposition it will be assumed that all of the funds a district receives come from 

the state. This would also apply to a system utilizing local resources if those resources are fully 

equalized by the state’s school funding formula. 

It is assumed that there are just two types of allotments. The first, in amount E, is paid to 
each district for each student in attendance. In addition, an add-on weight of r, with r  greater than 
0, is applied to each student classified as economically disadvantaged, or poor. The additional 
amount the district receives for each poor student is rE. The following notation and definitions are 
used: 

E:  Flat grant allotment for each enrolled student. 
rE: Additional grant for each poor student, with r greater than zero. 
ni: Number of students enrolled in the i

th district.  
pi: The proportion of students in district i classified as poor. 
Xi: Total grant to district i. 
xi:  Per pupil grant to district i (i.e. xi = Xi/ni). 
NT: Total number of students in all districts. 
NP: Total number of poor students in all districts. 
NNP: Total number of nonpoor students in all districts. 
nd: Number of districts. 

SP: Total of grants to districts for their poor students. 
SNP: Total of grants to districts for their nonpoor students. 

The total amount of grants to district i can be written as 

          Xi = piniE(1 + r) + (1 – pi)niE.                                                                                 (B1)     

            xi = piE(1 + r) + (1 – pi)E     
               = E[pi + pir + 1 – pi] . 
 
The per pupil grant for each student is therefore described by Equation B2.  
 



Education Policy Analysis Archives, Vol. 29, No. 36  35 

 

 

           xi = E(1 + pir).                                                                                                         (B2) 

The indicated summations which follow include all of the districts in a given state. The 

weighted sum of amounts going to districts for poor students is 

         SP = Σ piniE(1 + pir) = E Σ [pini + rpi
2ni], or 

                SP = E[NP + rΣpi
2ni].                                                                                           (B3) 

The average weighted amount to poor students is 

           SP/NP  = E[N
P + rΣpi

2ni]/N
P .                                                                                  (B4)    

Similarly, the student-weighted sum to all districts for their nonpoor students is 

   S
NP = Σ (1 – pi)niE(1 + pir) = E Σ[(1 – pi)ni(1 + pir) ] 

                   = E Σ[ni(1 + pir – pi - pi
2r)] 

                   = E Σ[ni + rpini – pini – rpi
2ni] 

                   = E[NT + rNP – NP – rΣpi
2ni] 

                   = E[NNP + rNP – rΣpi
2ni], or 

             SNP = E[NNP + rpNT – rΣpi
2ni].                                                                             (B5) 

The average amount of the weighted sum for nonpoor students is 

               SNP/NNP = (E/NNP)[NNP + rpNT – rΣpi
2ni].                                                          (B6) 

The ratio form of the CBI index is (SP/NP)/(SNP/NNP), or 

              CBI = (E/NP)[NP + rΣpi
2ni]_______ .                                                                  (B7) 

                       (E/NNP)[NNP + rpNT – rΣpi
2ni]  

Chingos and Blagg observed a positive correlation between their measure of progressivity 
and a measure of segregation within districts, which they termed a dissimilarity index. Though 
Chingos and Blagg did not define the dissimilarity index, it appeared to be the standard deviation or 
the variance of the pis for districts in each state. The variance of the pis for the districts in a state, 
weighted by each district’s enrollment, would be 

        Var(pi) = (1/nd) Σ(pi – µp)
2ni ,                                                                                      (B8) 

where µp refers to the mean of the pis and nd represents the number of districts in the state. Solving 
Equation B8 for Σpi

2ni results in 

        Σpi
2ni = (nd)Var(pi) + µpN

P .                                                                                       (B9) 

Substituting p for µp, the enrollment-weighted mean proportion of poor students in all districts, this 
expression can be substituted into Equation B7 for Σpi

2ni, and after simplification becomes  

          CBI = NNP [  NP(1 + rp)   +  r(nd)Var(pi)]   . 
B1                                                            (B10) 

                     NP   [ NNP(1 + rp)  –   r(nd)Var(pi)] 

Given that the add-on weight of r for poor students, resulting in an additional grant of rE, 
was the only source of additional funds in support of poor students, it is not surprising that if r is set 
to zero that the value of the CBI becomes exactly 1.0. Further, if the Var(pi) is set to zero, which 

                                                        
B1 The validity of Equation B10 was confirmed with spreadsheet simulations, calculating the CBI with this 
equation, and also by applying the direct definition as per Chingos and Blagg (2017), with the same results.  



Education Policy Analysis Archives, Vol. 29, No. 36  36 

 

 

would happen, for example, if all districts had the same proportions of poor students, the CBI 
would again equal unity. B2 

However, if r is greater than zero, then any amount of variance in the pis would result in a 
CBI greater than unity, and the greater the variance, the greater the CBI, assuming a constant 
average p. Increases in Var(pi) increase the numerator and decrease the denominator of B10. This is 
consistent with the observations by Chingos and Blagg. However, they attributed this relationship to 
the ability and opportunity of “ how states with more segregated school districts can more readily 
target poor students because those students are more concentrated within certain districts” (Chingos 
& Blagg, 2017, p. 11). But Equation B10 shows this relationship to be a mathematical function of 
the variance of the proportions of poor students among the various districts within a state. 

These results are consistent also with assertions by Shores and Ejdemyr who found that 
“poor, black, and Hispanic shares of school resources are greater in more segregated districts.” 
(Shores & Ejdemyr, 2017, p. 22). In present terms, the CBI is, indeed, greater in districts with greater 
Var(pi). 

Lane, Linden and Stange arrived at the same conclusion, stating the following: 
“Furthermore, funding is more progressive in states that have more across-district economic 
segregation.” (Lane, Linden & Stange, 2018, p. 24). 

In general, given two states that apply the same r>0 in determining rE, the state with greater 
Var(pi)  will appear the most progressive. Yet, in some sense, states making the same effort to direct 
more funds to poor students, measured by r, are equally progressive.  

There is another approach to the distribution of additional grants, rE, for the benefit of 

economically disadvantaged students. Specifically, let the additional funds be channeled directly to 

the entitled students, not just to the districts and the schools that they attend. Under these 

conditions, Equation B1 is no longer appropriate. Instead, the share of all funds going to poor 

students would become  

 
 SP =  ΣpiniE(1+r) = E(1 + r)N

P .                                                                           (B11)    

This reflects the assumption that the extra funds, rE, benefit only the poor students in the districts, 

and in the schools within the districts which those students attend. The average amount to poor students 

would become 

            (SP/NP) = (1/NP) E(1 + r)NP = E(1 + r) .                                                              (B12) 

The grants to nonpoor students are now solely based on E. Therefore, the total to nonpoor 
students would be NNP x E, and the mean amount to nonpoor students is:  

            (SNP/NNP) = E.                                                                                                        (B13) 

                                                        
B2 Shores and Ejdemyr very nearly stated these same conditions for the index to reflect perfect equality. 
Referring to an equation describing the calculation of the index, they concluded as follows: “First, perfect 
equality will arise if there is no segregation between the two groups—that is, if the group shares are uniformly 
distributed across schools. Second, perfect equality also arises from equal spending on each school within the 
district (regardless of the across-school distribution of the two groups). Thus, both segregation and spending 
variation are necessary for the measure to deviate from 0.” (Shores & Ejdemyr, 2017, p. 8). 
Instead of requiring “equal spending on each school” they probably meant to say “equal spending [per pupil] 
on each school.” But most importantly, they did not arrive at the conclusion that values of the index would 
increase solely due to increases in segregation, i.e., the variance of p. It is this that brings into question the 
applicability of this index. 



Education Policy Analysis Archives, Vol. 29, No. 36  37 

 

 

The CBI thus becomes merely 

            CBI = E(1 + r)/E  = 1 + r.                                                                                                 (B14) 

The CBI measure of progressivity no longer depends on Var(pi). All districts that distribute 
add-on grants of rE to poor kids would have a CBI of 1 + r. The same effort, as measured by r, 
results in the same index value. However, as currently applied, two districts with the same Var(pi) 
and making the same effort as measured by r would receive the same CBI value, even if one district 
makes extra effort to actually focus the additional resources on their poor students who generate  
the additional funding.  

Currently, in Texas, the law permits districts to intercept up to 45% of the additional 

compensatory education grants for overhead purposes. In school year 2017-18, the year for which 

the data used in this paper derived, the permissive rate for overhead was 0.48. With an r of 0.2 for 

that year, the CBI, all else equal, would have been expected to be 1.096 instead of 1.2, assuming that 

all of the funds not intercepted by the district were exclusively applied to the low income students in 

each school. 

Since the additional allotments for poor students are effectively paid in full to districts in 
Texas, but the districts are permitted to siphon off nearly half for overhead purposes, and that there 
is no guarantee that districts actually allocate the remainder to the schools which the poor students 
actually attend, it is not surprising that Lane, Linden and Stange (2018) reached the following 
conclusion:   

We find that spending progressivity has a higher correlation with economic 
segregation at a state-level than it does at a district level within Texas:  the 
dissimilarity index explains nearly three times as much of the cross-state variation in 
funding progressivity as it does at the district-level in Texas. While the state and 
district-level progressivity and segregation are not directly comparable, we view this 
analysis as suggestive evidence that states are able to better target resources to needy 
districts than districts are able to target needy schools. (p. 24) 

In view of the relationship between the CBI index and the variance in school districts’ 

proportions of poor students, described above, and the nearly total lack of evidence regarding the 

extent to which school districts and schools specifically target their poor students with the additional 

grants which those students generate, it seems very difficult to compare the spending progressivity 

between the different levels involved—state, districts, and schools—using the CBI. 

The difference in calculating the CBI for a given state using district-level data, as opposed to 

school level data, can be demonstrated with the data used in this paper using two different methods 

for calculating a “state” CBI. The basic data were school level general funds expenditures, from 

which expenditures for building maintenance were removed. Method 1 will consist of aggregating 

the school data to the district level—enrollments, numbers of poor kids, and expenditures. Method 

2 will consist of first calculating subtotals of expenditures by poor or nonpoor, at the school level, 

by applying the proportion of poor students to the school level expenditures and enrollments. These 

poor-nonpoor subtotals will in turn be aggregated to the district levels. Each of these two sets of 

district subtotals, obtained with the two different procedures, will be used to calculate a “state” level 

CBI, where the 90 districts that were used in this study comprise the “state.” 

The resulting CBIs, using the two methods are: 

Method 1:  CBI = -$9. 



Education Policy Analysis Archives, Vol. 29, No. 36  38 

 

 

Method 2: CBI = +$203. 

The lower level of progressivity resulting from using Method 1 demonstrates clearly that the 
school level detail is obscured by aggregation to the district level. Utilizing the school level data with 
Method 2 indicates that, on average, more expenditures occurred to the benefit of poor kids, in the 
amount of $203 per pupil. The CBIs for the individual districts were plotted in Figures 4 and 5 
above. It is noted that a simple average of those 90 individual district CBIs is $213, quite close to the 
one just referred to as resulting from Method 2. 

Perhaps even more important is that the school level data reveals substantial differences in 
the degrees of progressivity among the 90 districts. One must be careful in making direct 
comparisons, for reasons noted previously. Districts with all schools at very high percentages of 
poor kids have little or no opportunity to specifically target poor schools—they are all poor. But 
when differences in district average levels of poor kids, and the variances in the percentages of poor 
kids among the schools are taken into account, comparisons can be quite useful in calling attention 
to low district progressivity due to circumstances, and low district progressivity due to an apparent 
choice. Certainly such comparisons provide a starting point for better understanding such 
differences. 



Education Policy Analysis Archives, Vol. 29, No. 36  39 

 

 

Appendix C 

Corporate Funded Foundations’ Support for Intra-district Funding Studies 

It was stated above (p. 3) that the four works cited by Baker and Welner (Baker & Welner, 
2010; Baker, 2017) are associated with or received funding from at least 20 foundations that have 
provided funds to organizational members of the State Policy Network (SPN). That assertion is 
documented in Table C1.  

One of the original goals of the SPN was to establish policy think tanks in every state to 
produce studies that would promote the interests of the large number of substantially conservative 
corporate-funded foundations that supported the network (Mayer, 2017, p. 425). The purpose of a 
related organization, the American Legislative Exchange Council (ALEC) was, and is, to meet with 
conservative legislators from all states, with corporate donors also in attendance, and draft model 
legislation that would promote the interests of the corporate sponsors, and to get those model bills 
passed in as many states as possible. Many of the legislators who attend the ALEC meetings receive 
political contributions from those same corporate representatives with whom they meet. For 
example, Texas Representative Tom Craddick, who at one time was the Chairman of the ALEC 
Board of Directors, “received $878,000 in campaign contributions from ALEC corporate members 
from 2004 to 2011.” C1 These ALEC-sponsored meetings received little public notice for many years. 
Eventually ALEC’s activities were publicized by the website ALEC Exposed and by a series of 
important books that detailed the development of the SPN, ALEC, and their financial supporters 
(Anderson, 2020; Fang, 2013; Mayer, 2017); 

A partial list of items that have been on the legislative agenda of ALEC and SPN includes 
the following: 

Opposition to tax increases—Super Majority Act;C2 

Living Wage Mandate Preemption Act;C3 

Opposition to Global Warming reduction legislation;C4 

Scholarship tax credits;C5 

Voter ID Act;C6 

Right to Work Act. C7 

                                                        
C1 Taken from “Texas ALEC Politicians,” 
Sourcewatch, http://www.sourcewatch.org/index.php/Texas_ALEC_Politicians , accessed on 10-21-2016 
C2 http://www.alecexposed.org/w/images/5/5c/8G1-Super-Majority_Act_Exposed.pdf, accessed 10-22-
2016. 
C3 http://www.alecexposed.org/w/images/b/ba/1E6-
Living_Wage_Mandate_Preemption_Act_Exposed.pdf , accessed 10-22-2016. 
C4 http://alecexposed.org/w/images/9/9d/6D0-
Against_US_Participation_in_International_Agreement_in_Copenhagen_Exposed.pdf , accessed 10-22-2016. 
C5 https://www.alec.org/model-policy/the-great-schools-tax-credit-program-act-scholarship-tax-credits , 
accessed 10-22-2016. 
C6 http://alecexposed.org/w/images/d/d9/7G16-VOTER_ID_ACT_Exposed.pdf , accessed 10-22-2016. 
C7 http://alecexposed.org/w/images/c/c8/1R10-Right_to_Work_Act_Exposed.pdf , accessed 10-22-2016. 

http://www.sourcewatch.org/index.php/Texas_ALEC_Politicians
http://www.alecexposed.org/w/images/5/5c/8G1-Super-Majority_Act_Exposed.pdf
http://www.alecexposed.org/w/images/b/ba/1E6-Living_Wage_Mandate_Preemption_Act_Exposed.pdf
http://www.alecexposed.org/w/images/b/ba/1E6-Living_Wage_Mandate_Preemption_Act_Exposed.pdf
http://alecexposed.org/w/images/9/9d/6D0-Against_US_Participation_in_International_Agreement_in_Copenhagen_Exposed.pdf
http://alecexposed.org/w/images/9/9d/6D0-Against_US_Participation_in_International_Agreement_in_Copenhagen_Exposed.pdf
https://www.alec.org/model-policy/the-great-schools-tax-credit-program-act-scholarship-tax-credits
http://alecexposed.org/w/images/d/d9/7G16-VOTER_ID_ACT_Exposed.pdf
http://alecexposed.org/w/images/c/c8/1R10-Right_to_Work_Act_Exposed.pdf


Education Policy Analysis Archives, Vol. 29, No. 36  40 

 

 

Table C1 

Foundations’ Support for Intra-district Funding Studies 

Authors and organizations cited in Baker 
& Welner (2010) and Baker (2017) 

Related 
Organizationsa 

Donor 
Foundationsb 

Contributionsc ($) 
(2004 – 2018) 

(McClure, Wiener, Roza, 
& Hill, 2008) 

Edvance 
Research 
 
Center on 
Reinven- 
ting Public 
Education 

Broad 
Dell 
Bradley 
Broad 
Dell 
Hume 
Joyce 
Kauffman 
Kern 
Walton 

255,426 
5,500,000 

335,000 
129.000 

1,950,500 
60,000 

250,000 
350,039 
141,000 
624,750 

(Public Impact et al. (2008) Fordham 
Institute 

Arnold 
Bradley 
Broad 
Carnegie 
Dell 
Fordham F 
Gates 
Hertog 
Joyce 
Kauffman 
Kern 
Koch 
Koret 
Kovner 
Scaife 
Searle 
Silicon 
Simon 
Walton 

52,500 
767,500 
456,250 
919,700 
67,000 

325,000 
7,013,823 

110,000 
993,500 
425,250 

1,310,185 
25,000 

130,000 
1,225,000 

160,000 
567,000 
350,000 
342,500 

6,922,780 
(Cary, Gray & Holley, 2007) Buckeye 

Institute 
Badley 
Hume 
Koch 
Roe 
Scaife 
Searle 

350,000 
90,000 

275,281 
175,000 
300,000 
331,500 

(Families for Excellent 
Schools, 2013) 

Families for 
Excellent 
 Schools 

Broad 
Hertog 
Walton 

3,029,000 
2,300,000 

27,036,000 

  Total 65,645,484 
a The organizations and funding sources listed as related to authors McClure, Wiener, Roza, and Hill were 
identified on Edunomics Lab’s website https://edunomicslab.org/about-us and the Building State Capacity 
& Productivity Center’s website: http://www.bscpcenter.org/aboutus/. Contribution amounts from the 
Arnold, Gates, and Walton foundations, to Edunomics Lab, if any, could not be determined. It is noted that 

https://edunomicslab.org/about-us
http://www.bscpcenter.org/aboutus/


Education Policy Analysis Archives, Vol. 29, No. 36  41 

 

 

Margarite Roza, the Director of the Edunomics Lab, was at one time a Senior Economic Advisor to the Bill 
& Melinda Gates Foundation (See https://www.educationnext.org/author/maroza/ ). 
bThe full names of the donor foundations are listed in Table C2. 
cThe dollar amounts of contributions by the donor foundations were obtained from the website of 
Foundation Directory Online (https://fconline.foundationcenter.org). A paid subscription is required to 
access their data.  
 

Table C2 

Full Names of Donor Foundations Listed in Table C1 

Short name Official namea 

Arnold Laura and John Arnold Foundation 
Bradley The Lynde and Harry Bradley Foundation, Inc. 
Broad Eli & Edyth Broad Foundation 
Carnegie Carnegie Corporation of New York 
Dell Michael & Susan Dell Foundation 
Fordham Thomas B. Fordham Foundation 
Gates Bill & Melinda Gates Foundation 
Hertog Hertog Foundation, Inc. 
Hume Jaquelin Hume Foundation 
Joyce The Joyce Foundation 
Kauffman Ewing Marion Kauffman Foundation 
Kern The Kern Family Foundation, Inc. 
Koch Charles Koch Foundation 
Koret Koret Foundation 
Kovner The Kovner Foundation 
Roe The Row Foundation 
Scaife Sarah Scaife Foundation, Inc. 
Searle Searle Freedom Trust 
Silicon Silicon Valley Community Foundation 
Simon William E. Simon Foundation, Inc. 
Walton Walton Family Foundation 

aFull names of foundations as used on Foundation Directory Online 

(https://fconline.foundationcenter.org/)    

As can be seen from the bottom of Table C1, the total amount of identified contributions 
from the 20 donor foundations to the organizations related to the authors cited by Baker is 
$65,645,484. Elsewhere, this author tallied the contributions from these same foundations to the 
State Policy Networks members and associate members, for the years 2003 to 2013, also based on 
data obtained from Foundation Directory Online https://fconline.foundationcenter.org/). Those 
amounts totaled $276,068,000 (Toenjes, 2016). These amounts strongly suggest that many of the 
goals that those funding organization might pursue in their promotion of the State Policy Network 
and the American Legislative Exchange Council--lower taxes, lower government regulation of 
industry, more constraints on unions-- would also be influential in their pursuit of school choice, 
vouchers, and privatization of the U.S. education system. 

https://www.educationnext.org/author/maroza/
https://fconline.foundationcenter.org/


Education Policy Analysis Archives, Vol. 29, No. 36  42 

 

 

About the Author  

Laurence A. Toenjes 
University of Houston (Retired) 
ltoenjes@aol.com 
Laurence A. Toenjes served as a Research Associate Professor at the University o f Houston 
until his retirement. Toenjes was previously employed by the Bureau of the Budget and the 
Office of the Comptroller in Illinois as a fiscal and educational finance analyst and by the Texas 
Association of School Boards. His papers have appeared in Education Policy Analysis Archives, 
The American Economist, Journal of Education Finance and other publications. Laurence 
received his BA and MA degrees in economics from the University of California at Berkeley, 
and his PhD in economics from Southern Illinois University at Carbondale. 
 

 
 

education policy analysis archives 
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education policy analysis archives 

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University of Kentucky 

Kyo Yamashiro  
Claremont Graduate University 

Rachael Gabriel 
University of Connecticut 

Susan L. Robertson 
Bristol University 

Miri Yemini 
Tel Aviv University, Israel 

 
 
 



Education Policy Analysis Archives, Vol. 29, No. 36  44 

 

 

 
archivos analíticos de políticas educativas 

consejo editorial 

Editor Consultor: Gustavo E. Fischman (Arizona State University) 
 Coordinador (Español/Latinoamérica): Ignacio Barrenechea (Universidad de San Andrés), Ezequiel Gomez Caride 

(Universidad de San Andres/ Pontificia Universidad Católica Argentina) 
Editor Coordinador (Español/Norteamérica): Armando Alcántara Santuario (Universidad Nacional Autónoma de México) 

Editor Coordinador (Español/España): Antonio Luzon (Universidad de Granada) 
Editores Asociados: Jason Beech (Monash University), Angelica Buendia, (Metropolitan Autonomous University), 

Gabriela de la Cruz Flores (Universidad Nacional Autónoma de Mëxico), Alejandra Falabella (Universidad Alberto 
Hurtado, Chile), Carmuca Gómez-Bueno (Universidad de Granada), Carolina Guzmán-Valenzuela (Universidade de 
Chile), Cesar Lorenzo Rodriguez Uribe (Universidad Marista de Guadalajara), Antonia Lozano-Díaz (University of 
Almería), Sergio Gerardo Málaga Villegas (Instituto de Investigación y Desarrollo Educativo, Universidad Autónoma 
de Baja California (IIDE-UABC)), María Teresa Martín Palomo (University of Almería), María Fernández Mellizo-

Soto (Universidad Complutense de Madrid), Tiburcio Moreno (Autonomous Metropolitan University-Cuajimalpa 
Unit), José Luis Ramírez, (Universidad de Sonora), Axel Rivas (Universidad de San Andrés), Maria Veronica 

Santelices (Pontificia Universidad Católica de Chile) 
 

Claudio Almonacid 
Universidad Metropolitana de 
Ciencias de la Educación, Chile 

Ana María García de Fanelli  
Centro de Estudios de Estado y 
Sociedad (CEDES) CONICET, 
Argentina 

Miriam Rodríguez Vargas 
Universidad Autónoma de 
Tamaulipas, México 

Miguel Ángel Arias Ortega 
Universidad Autónoma de la 
Ciudad de México 

Juan Carlos González Faraco 
Universidad de Huelva, España 

José Gregorio Rodríguez 
Universidad Nacional de Colombia, 
Colombia 

Xavier Besalú Costa  
Universitat de Girona, España 

María Clemente Linuesa 
Universidad de Salamanca, España 

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

Xavier Bonal Sarro Universidad 
Autónoma de Barcelona, España   

 

Jaume Martínez Bonafé 
 Universitat de València, España 

José Luis San Fabián Maroto  
Universidad de Oviedo,  
España 
 

Antonio Bolívar Boitia 
Universidad de Granada, España 

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

Jurjo Torres Santomé, Universidad 
de la Coruña, España 

José Joaquín Brunner Universidad 
Diego Portales, Chile  

María Guadalupe Olivier Tellez, 
Universidad Pedagógica Nacional, 
México 

Yengny Marisol Silva Laya 
Universidad Iberoamericana, 
México 

Damián Canales Sánchez 
Instituto Nacional para la 
Evaluación de la Educación, 
México  
 

Miguel Pereyra Universidad de 
Granada, España 

Ernesto Treviño Ronzón 
Universidad Veracruzana, México 

Gabriela de la Cruz Flores 
Universidad Nacional Autónoma de 
México 

Mónica Pini Universidad Nacional 
de San Martín, Argentina 

Ernesto Treviño Villarreal 
Universidad Diego Portales 
Santiago, Chile 

Marco Antonio Delgado Fuentes 
Universidad Iberoamericana, 
México 

Omar Orlando Pulido Chaves 
Instituto para la Investigación 
Educativa y el Desarrollo 
Pedagógico (IDEP) 

Antoni Verger Planells 
Universidad Autónoma de 
Barcelona, España 

Inés Dussel, DIE-CINVESTAV, 
México 
 

José Ignacio Rivas Flores 
Universidad de Málaga, España 

Catalina Wainerman  
Universidad de San Andrés, 
Argentina 

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Education Policy Analysis Archives, Vol. 29, No. 36  45 

 

 

Pedro Flores Crespo Universidad 
Iberoamericana, México 

 Juan Carlos Yáñez Velazco 
Universidad de Colima, México 
 

 
arquivos analíticos de políticas educativas 

conselho editorial 

Editor Consultor:  Gustavo E. Fischman (Arizona State University) 
Editoras Coordenadores: Marcia Pletsch, Sandra Regina Sales (Universidade Federal Rural do Rio de Janeiro) 

Editores Associadas: Andréa Barbosa Gouveia (Universidade Federal do Paraná), Kaizo Iwakami Beltrao, 
(EBAPE/FGVl), Sheizi Calheira de Freitas (Federal University of Bahia), Maria Margarida Machado (Federal 

University of Goiás / Universidade Federal de Goiás), Gilberto José Miranda (Universidade Federal de Uberlândia) 
 

Almerindo Afonso 

Universidade do Minho  

Portugal 

 

Alexandre Fernandez Vaz  

Universidade Federal de Santa 

Catarina, Brasil 

José Augusto Pacheco 

Universidade do Minho, Portugal 

Rosanna Maria Barros Sá  

Universidade do Algarve 

Portugal 

 

Regina Célia Linhares Hostins 

Universidade do Vale do Itajaí, 

 Brasil 

Jane Paiva 

Universidade do Estado do Rio de 

Janeiro, Brasil 

Maria Helena Bonilla  

Universidade Federal da Bahia  

Brasil 

 

Alfredo Macedo Gomes  

Universidade Federal de Pernambuco 

Brasil 

Paulo Alberto Santos Vieira  

Universidade do Estado de Mato 

Grosso, Brasil 

Rosa Maria Bueno Fischer  

Universidade Federal do Rio Grande 

do Sul, Brasil 

 

Jefferson Mainardes  

Universidade Estadual de Ponta 

Grossa, Brasil 

Fabiany de Cássia Tavares Silva 

Universidade Federal do Mato 

Grosso do Sul, Brasil 

Alice Casimiro Lopes  

Universidade do Estado do Rio de 

Janeiro, Brasil 

Jader Janer Moreira Lopes  

Universidade Federal Fluminense e 

Universidade Federal de Juiz de Fora, 

Brasil 

António Teodoro  

Universidade Lusófona 

Portugal 

Suzana Feldens Schwertner 

Centro Universitário Univates  

Brasil 

 

 Debora Nunes 

 Universidade Federal do Rio Grande 

do Norte, Brasil 

Lílian do Valle 

Universidade do Estado do Rio de 

Janeiro, Brasil 

Geovana Mendonça Lunardi 

Mendes Universidade do Estado de 

Santa Catarina 

 

Alda Junqueira Marin 

 Pontifícia Universidade Católica de 

São Paulo, Brasil 

Alfredo Veiga-Neto 

 Universidade Federal do Rio Grande 

do Sul, Brasil 

Flávia Miller Naethe Motta 

Universidade Federal Rural do Rio de 

Janeiro, Brasil 

Dalila Andrade Oliveira 

Universidade Federal de Minas 

Gerais, Brasil 

 

  

 

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