CEPRS Working
Paper
WP #2016-1
Are Higher-Need School Districts Disproportionately
Impacted by State Funding Cuts?
School Finance Equity in Texas Following the Great
Recession
David S. Knight
Center for Education Research and Policy Studies
University of Texas at El Paso
Center for Education Research and Policy Studies
University of Texas at El Paso
500 W. University Ave. Ste. 105 El Paso, TX 79968
(915)–747–5949
http://volt.utep.edu/collegeofeducation/home/index.php/research/research-2
Acknowledgements
The work is supported through funding from the College of Education and from the University of Texas
at El Paso Office of the Provost.
CERPS working papers have not undergone final formal peer review and should be cited as working
papers. They are intended to encourage discussion and suggestions for revision before final publication.
The views expressed in this paper do not necessarily reflect those of the University of Texas at El Paso or
the College of Education. The authors are responsible for any errors.
Suggested Citation:
Knight, D. S. (2016). Are higher-need school districts disproportionately impacted by state funding cuts?
School finance equity in Texas following the Great Recession. CERPS Working Paper 2016-1. University of
Texas at El Paso, El Paso, TX.
Abstract
The Great Recession forced states around the country to make substantial budget cuts to public
education. State funding for school districts is typically designed to counter balance the
otherwise inequitable system that results from local property taxation. As a result, districts that
rely more heavily on state funding – those with greater concentrations of students in poverty–
may be disproportionately impacted by state education funding cuts; however, little prior
research examines this issue. This study finds that (a) high-poverty districts in Texas and
nationally experienced an inequitable share of funding and staffing cuts following the Great
Recession; (b) the funding gap increased in Texas by more than in 43 other states; (c)
idiosyncrasies within the Texas school finance system prevented high-poverty districts from
maintaining equitable funding levels, despite increasing tax rates at a faster rate than otherwise
similar wealthier districts; and (d) leveling up funding for high-poverty districts to that of lowpoverty districts would cost the state $9.1 billion, a 17% increase in education spending. Like
many states around the country, Texas is beginning to restore education funding back to prerecession levels, while at the same time considering restructuring its funding mechanisms. The
study provides important evidence on how students were impacted by recessionary spending cuts
and offers alternative strategies for restoring state education budgets.
SCHOOL FINANCE EQUITY IN TEXAS
1
Are Higher-Need School Districts Disproportionately Impacted by State Funding Cuts?
School Finance Equity in Texas Following the Great Recession
States made substantial cuts to public education funding following the Great Recession.
Although federal stimulus money helped alleviate some of the spending cuts, almost every state
lowered its total K-12 funding from the 2007-08 school year to 2012-13 (Leachman, Albares,
Masterson & Wallace, 2016). Only recently have states begun to build back budgets and very
few have restored funding to pre-recessions levels (Leachman et al., 2016). When states reduce
education funding, the burden of these cuts often falls most heavily on the districts that serve
greater proportions of students in poverty and emergent bilingual students (Baker, 2014). At the
same time, these “higher-need” districts face additional costs to provide compensatory
educational programs for low-income students and bilingual instructional programs for emergent
bilingual students (Darling-Hammond, 2013; Duncombe & Yinger, 2008; Ladd, 2012).
Facing serious budget shortfalls following the Great Recession, Texas relied on federal
stimulus aid to fill gaps in state funding during the 2009-10 school year. In 2011, when stimulus
funding diminished, the 82nd Texas Legislature cut K-12 public education by $4 billion for
school year 2011-12 (Barta, 2011). The following year, over 600 school districts sued the state
for violating the state constitutional mandate of providing an adequate education for all students
(Collier, 2016). Ultimately, the Texas Supreme Court ruled the finance system constitutional in
May of 2016; however, the court’s opinion labeled the Texas educational finance system
antiquated and urged the legislator to overhaul the state’s school funding mechanism (Texas
Taxpayer and Student Fairness Coalition, et al. v. Scott, Combs, and the State Board of
Education, 2016). While the state moved to restore the budget in 2012-13, legal battles and court
mandates spanning over three decades have resulted in an overly complex and multilayered
school finance system (Barton, 2013; Imazeki & Reschovsky, 2004).
SCHOOL FINANCE EQUITY IN TEXAS
2
The Legislature has recently called for additional studies of the Texas school finance
system in advance of the next Legislative session (Collier, 2016). Comprehensive school finance
reform may thus depend on the Texas legislature’s assessment of the potentially negative impact
of the Great Recession on the school finance system and whether its recent efforts to restore the
education budget in the 2012-13 school year were sufficient. The situation in Texas is reflective
of national trends, as many states are assessing the impact of the recession on their school
finance systems and considering strategies for restoring budgets and reforming school finance
systems (Bunting, Kueneman, Louttit, Park & Parker, 2014).
Prior research shows that school finance reforms – either court mandated or those
initiated solely through legislative action – lead to increases in spending for low-income districts,
thereby closing gaps in resources and increasing state school finance equity (Murray, Evans &
Schwab, 1998). More importantly, several studies link these increases in spending to greater
educational and labor market outcomes for low-income students (e.g., Card & Payne, 2002;
Jackson, Johnson & Perscio, 2014). Few studies, however, examine the impact of recessions and
state budget cuts on school finance systems, especially school resource equity and no prior
research looks specifically at the case of Texas. Moreover, the recent Texas Supreme Court
opinion and the upcoming Legislative session in Texas make an analysis of Texas school finance
particularly timely. This study examines the following research questions: (a) to what extent are
school districts in Texas and nationally compensated for higher rates of student poverty, and
how did resource and achievement gaps change during the Great Recession? And (b) to what
extent did high- and low-poverty districts in Texas differ in their response to state funding cuts, if
at all?
The following section synthesizes prior research that informs this study and shows how
SCHOOL FINANCE EQUITY IN TEXAS
3
the current analyses address an important gap in the literature. I then provide additional policy
context for Texas and nationally, describe the data, analytic approach, and findings and conclude
with discussion and state policy recommendations.
Background Literature
Three board areas of research inform the current study. The first assesses equity and
efficiency of state school finance systems and district resource allocation. A second set of studies
focuses on lessons learned from budgetary cuts associated with the recent recession and the third
area measures the impact of state school finance reforms on student outcomes.
Assessment of State School Finance Systems
States rely on the best available evidence to improve their school finance systems in
ways that promote adequacy and equity. Adequate finance systems provide sufficient school
resources to meet state standards, while school finance equity is defined as the allocation of
resources, broadly defined, that meets diverse student needs (Baker & Green, 2015). Early
analyses of school finance equity used measures of dispersion of per-pupil funding across
districts and assessed fiscal neutrality by testing the correlation between property values and
funding levels (e.g., Berne & Stiefel, 1994; Goldhaber & Calhoun, 2001; Rolle & Liu, 2007).
These measures do not account for differences in costs outside the control of districts such as
higher labor costs or higher student poverty rates (Chambers & Levin, 2009). More sophisticated
analyses take into account differences in cost and attempt to measure and control for inefficiency
of districts (Duncombe & Yinger, 2008; Imazeki & Reschovsky, 2001). Although scholars
debate the validity of cost and efficiency estimates (Hanushek, 1997; 2007), there is consensus
that comparisons of district spending should take into account differences in cost factors outside
the control of districts (Duncombe, 2006) and that effective state school finance systems
SCHOOL FINANCE EQUITY IN TEXAS
4
compensate districts with higher cost factors (Odden & Picus, 2013; Verstegen, 2011).
Most studies of state school finance systems focus specifically on state and local
revenues and total expenditures. Very few studies examine changes in these outcomes over time,
and in particular, how the Great Recession impacted resource allocation across school districts
(Baker, 2014 is one exception; also see Freelon, Bertrand & Rogers, 2012 for qualitative analysis
and practitioner survey data on the effects of the recession). The current study adds to literature
on the assessment of state school finance systems by evaluating the Texas system before, during,
and after the recent recession, incorporating multiple outcome measures, and exploring the
underlying mechanisms. The topic is particularly timely for Texas given the State Supreme
Court’s recent critique of the school finance system.
Effects of the Great Recession on Schools
A small number of studies analyze the impact of the recent recession on schools
(Chakrabarti & Setren, 2011; Goldhaber, Strunk, Brown & Knight, Forthcoming; Knight &
Strunk, 2015). The purpose of these studies broadly is to assess the negative impacts associated
with recession-induced state budget cuts, and to generate knowledge around how to lessen the
detrimental effects of future austerity measures. State education budget cuts associated with the
recession resulted in teacher layoffs that were disproportionately concentrated in districts serving
lower-income students, suggesting that states should consider taking measures to protect the
neediest districts during times of fiscal austerity (Estrada, 2012; Plecki, Elfers & Finster, 2010).
Several national policy scans find that state funding cuts disproportionately affected low-income
districts (Baker, 2014; Baker, Sciarra & Farrie, 2015). While informative, these national studies
do not focus on or offer policy implications specific to one state. The current study builds on
these past studies of the effects of the Great Recession both by focusing closely on how the
SCHOOL FINANCE EQUITY IN TEXAS
5
recessionary budget cuts impacted one state school finance system and by exploring features of
the Texas school finance system that led to a disproportionate impact for low-income districts.
Other research looks more broadly at the impact of recessions. Not surprisingly, these
studies find that lower-income and less-educated workers and people of color experience greater
negative impacts of recessions (Farber, 2011; Kochhar, Fry & Taylor, 2011; Verick, 2009;
Hines, Hoynes, & Krueger, 2001). Over a 30-year period, Hoynes, Miller, and Schaller (2012)
found that labor market outcomes during recessions were substantially and consistently worse for
disadvantaged groups, resulting in greater declines in wages and longer spells of unemployment.
The analyses described in this study complement these prior studies by focusing on how the
recent recession affected individuals still in K-12 schools.
State School Finance Reform
Evidence from studies of school finance reforms suggest that school funding cuts for
lower-income students may lead to negative longer-term outcomes. While less research formally
evaluates state school finance systems, numerous studies show how finance systems respond to
legislative reforms and how those reforms impact students’ educational achievement and longterm labor market outcomes (e.g., Card & Payne, 2002; Murray et al., 1998; Springer, Lui &
Guthrie, 2009).
Many of these studies are correlational and therefore, may confound changes in
educational spending with other changes that influence student outcomes (see Figlio, 2004 and
Krueger, 1999 for more discussion). A few studies use strong research designs to isolate the
impact of changes in funding. For example, Guryan (2001) uses regression-discontinuity based
on distinct eligibility-based increases in state aid created by a 1993 school finance reform that
equalized spending across districts in Massachusetts. Guryan finds that increased spending
SCHOOL FINANCE EQUITY IN TEXAS
6
improved grade 4 reading and math scores, primarily for lower achieving students. Another
study follows school finance reforms nationally over a 30-year window, using differential timing
of reforms to identify exogenous changes in spending, and tracks students’ outcomes into
adulthood (Jackson et al., 2014). The authors find that a 20% increase in educational spending
during all 12 years of public schooling reduced the incidence of poverty later in life by 20% and
increased adult wages by 25%, but only for students from lower-income families. Although the
data provide limited information around mechanisms, these positive effects appeared to result
from more teachers and counselors per student, leading to smaller class sizes and more adults per
student in schools.
Based on the Jackson et al. (2014) results, a student in a high-poverty district who
experiences a decline in spending of around 10% would see a meaningful impact on their life
outcomes. If exposed to this decline in funding at the time of entering school, and this lower
funding level was in place for all 12 years of schooling, the results suggest that a student would
experience a 15% decline in their likelihood graduating high school, an increase in their
likelihood of living in poverty of about 11%, and an decrease in their adult earnings of about 9%
or $3,500 per year.1 The extent to which districts in Texas are compensated for higher poverty
rates – and the specific effects of the Great Recession – thus have real consequences for students.
Policy Context
History of School Finance in Texas
Texas has a long history of court battles that has shaped the state’s school finance system.
The first school finance case, filed in July 1968 by the Mexican American Legal Defense and
Educational Fund (MALDEF) argued that the primary reliance on local property taxes prevented
1
This dollar figure is based on the descriptive statistics shown in Table 1 of Jackson et al. (2014). Note that the
Jackson et al. study is based on court-ordered increases in spending rather than decreases in spending.
SCHOOL FINANCE EQUITY IN TEXAS
7
students from receiving equal protection, as required under the Equal Protection Clause of the
Fourteenth Amendment. Although the plaintiffs were successful, the decision was ultimately
reversed in an appeal to the U.S. Supreme Court case Rodriguez v. San Antonio Independent
School District (ISD) in 1973. Over the past four decades, school districts have been challenging
the Texas school finance system based on two state constitutional provisions. The state must
“establish and make suitable provision for the state support and maintenance of an efficient
system of free public schools,” (Article 7, Section 1) and the state may not levy a statewide
property tax (Article 8, Section 1e).
A total of four cases, referred to as Edgewood I - Edgewood IV, were argued in Texas
courts over the next 15 years. The first two Edgewood decisions – both finding the finance
system unconstitutional – led to increased funding and the creation of County Education
Districts, but did not substantially change the finance system. In response to a similar decision in
Edgewood III, Texas legislators passed Senate Bill 7 in 1993, which established three separate
elements of funding designed to create fiscal neutrality and equalize funding. Tier I provided a
specific, uniform amount of funding per-pupil (i.e., a “foundation system,” Verstegen, 2011).
Districts would choose a Maintenance and Operation (“M&O”) property tax rate that local
residents would pay, and the state would equalize the tax base upon which M&O taxes were
levied by paying the difference between M&O tax revenues and an amount agreed upon each
year (e.g., $4,950 for school year 2012-13). The second funding element, Tier II funding, came
in the form of tax base equalization for additional M&O tax rate increases. Districts could
supplement the Tier I funding with “enrichment” services by increasing local M&O taxes up to a
maximum of $1.50 per $100 of assessed value (a 1.5% property tax).
The third element capped the amount of revenue that high-property wealth districts could
SCHOOL FINANCE EQUITY IN TEXAS
8
raise to $280,000 per pupil. Districts that exceed this revenue cap (“Chapter 41 districts”) could
purchase “attendance credits” from the state to increase (synthetic) enrollment and lower the perpupil property wealth below the revenue cap.2 These Chapter 41 recapture payments would be
redistributed to low-wealth districts. Often referred to as the “Robin Hood” plan, this still-active
provision is unique to the Texas school finance system (Hoxby & Kuziemko, 2004).
Two years after the passing of Senate Bill 7, in 1995, the Texas Supreme court ruled in
Edgewood IV that the finance system, now largely shaped by Senate Bill 7, was constitutional.3
In its opinion the court called on legislatures to address inequities in the financing of school
facilities, and the state later established policies that provide some state aid for debt financing
and instructional facilities. Districts later argued in a 1998 suit that some high wealth districts
were able to avoid Chapter 41 payments (Edgewood V); however, legislation passed in 1999 that
increased Tier I and II funding, and the case was never heard. In April 2001, four wealthy
districts filed suit charging that the 1.5% maximum tax rate ($1.50 per $100 of assessed property
value) that was established through Senate Bill 7 constituted a statewide property tax because
many districts had increased their local property taxes to this cap and were unable to increase
funding beyond their current level (West Orange Cove ISD v. Neeley). After an additional 300
lower-wealth districts joined the suit, arguing that the finance system was neither equitable nor
2
Districts have five options for meeting the revenue cap:1) consolidate with a neighboring district; 2) reassign highwealth property to a neighboring district; 3) purchase “attendance credits” from the state to increase (synthetic)
enrollment and lower the per-pupil property wealth below the revenue cap (i.e., Chapter 41 payments); 4) contract
with neighboring districts to educate a sufficient number of students to lower the per-pupil property wealth below
the revenue cap; or 5) consolidate tax bases with a neighboring district. However, all districts have chosen the third
or fourth options, or some combination thereof. In addition to highly-valued homes, high property wealth districts in
Texas may also include within their boundaries a nuclear power plant, oil and gas, or industrial property. Some lowenrollment districts in rural area have extremely high per-pupil tax bases because of local power plants and oil
deposits (Reschovsky & Imazeki, 2001).
3
The Edgewood III case is formally called Carrollton-Farmers Branch School District v. Edgewood Independent
School District (1992, see Picus and Hertert, 1993 for discussion of the plaintiff’s arguments). Edgewood IV is
formally referred to as Edgewood Independent School District v. Meno (1995).
SCHOOL FINANCE EQUITY IN TEXAS
adequate, the court ultimately declared the system unconstitutional once again.
In response, the legislature passed House Bill 1, which lowered (or “compressed”) the
maximum tax rate from $1.50 to $1.00,4 but allowed all districts to raise local tax revenues to as
much as $0.17 above $1.00. Additional “hold harmless” provisions ensured that districts would
not lose funding as a result of having their local taxes compressed. This change provided the
constitutionally mandated “meaningful discretion” over property taxes by allowing districts
previously at the maximum tax rate to further increase local property taxes to as much as $1.17.
The most recent case, Texas Taxpayer and Student Fairness Coalition (TTSFC) et al. v.
Scott, Combs, and the State Board of Education, alleged that the finance system is
unconstitutional for a number of reasons. Districts argued that the system is inequitable because
property wealthy districts can raise higher tax revenues at lower tax rates, that the system
provides an inadequate level of funding to meet the state’s accountability standards, and that the
$1.17 tax rate cap constitutes a state property tax because it does not provide districts with
meaningful discretion over tax rates. The suit was combined with six other agencies bringing
similar suits including two additional groups of school districts, MALDEF, the Charter School
Association, the Texas Association of Business, and the Texans for Real Efficiency and Equity
in Education. Travis County District Judge John Dietz ruled in February 2013 that the finance
system was unconstitutional; however, the Texas Supreme Court ultimately overturned Judge
Dietz’s decision, finding that the Texas school finance system is constitutional, but severely in
need of reform, referring to the system as outdated and “byzantine” (Collier, 2016).
The Texas School Finance System
The long history of court decisions and legislative responses created the complex system
4
All local tax rates were compressed by one-third such that districts with 1.50% tax rates ($1.50 tax for each $100
of assessed value) had tax rates lowered to 1.0% and those with tax rates of 1.25% had tax rates lowered to 0.83%.
9
SCHOOL FINANCE EQUITY IN TEXAS
10
that is currently in place. The underlying mechanisms of the finance system provide a rationale
for the hypotheses tested in this study. Most of the provisions set forth in Senate Bill 7 (1993)
and House Bill 1 (2006) are still currently active. Senate Bill 7 attempted to remove the link
between a district’s ability to raise revenues through local taxation and its property wealth using
a combination of a foundation formula (Tier 1, the Foundation School Program, FSP), a
guaranteed tax base formula for additional tax increases (Tier 1I), and a cap on the tax capacity
of the wealthiest districts (resulting in Chapter 41 recapture payments). House Bill 1 compressed
tax rates by one-third and also created the “target revenue” system. The result was a two-layered
system, formula funding, comprised of Tier 1 and 2 funding, and the target revenue system.
Districts can also raise revenues for facilities by issuing bonds, and the state provides separate
equalizing aid for these tax revenues. These three elements are discussed in turn.
Formula funding. The first layer of the Texas school finance system, the FSP, consists
of two Tiers of funding. Tier 1funding includes $4,950 per pupil for districts with tax rates of
$1.00 (for the 2012-13 school year, and reduced proportionally for districts with tax rates less
than $1.00), multiplied by a cost of education index. The index adjusts the basic allotment for
cost factors beyond the district’s control, including geographic differences in cost, the percent of
FRL students in the district, and the district’s size and urbanicity; however, the index has not
been updated legislatively since 1991, despite the research showing that some urban areas have
seen substantial growth in the Texas cost of education index, while other areas have seen
declines (Taylor, 2010). Districts with fewer than 1,600 students and those with between 1,600
and 5,000 students also receive small district and mid-size district adjustments, respectively,
providing additional funding to compensate for the higher cost of operating smaller districts.
After making the cost of education index and size adjustments to the $4,950 base amount,
SCHOOL FINANCE EQUITY IN TEXAS
11
the per-pupil Tier 1 funding allocation is multiplied by the district’s weighted average daily
attendance (WADA) to calculate the total Tier 1 funding for each district. WADA is based on the
district’s average daily attendance, giving additional weights to students in special categories.
Students enrolled in the regular program receive a weight of 1.0, student eligible for free or
reduced price meals (FRL) receive a weight of 1.2, and those classified as having limited English
proficiency receive a weight of 1.1. Thus a district in which all students were eligible for FRL
would receive 20% greater Tier 1 funding than an otherwise identical district with zero student
eligible for FRL. A total of 16 student weights or other add-ons exist within Tier 1 funding (see
Barton, 2013 or TTARA, 2014 for a listing of student weights in the FSP). Tier 1 also includes
add-on funding for transportation and for each staff member on the minimum salary schedule.
The majority of state and local funding for Texas school districts is allocated through Tier
1 funding. School districts use the revenues raised through their local M&O property taxes to pay
for their Tier 1 allotment and the state pays any remaining share of the Tier 1 allocation. If a
district’s property values increase (and it becomes “wealthier”) than the cost to the state for that
district decreases. Conversely, when property values decrease, the district pays a smaller amount,
and the cost to the state for that district increases. For high-wealth districts that are able to raise
local property tax revenues above the Tier 1 allotment (“budget-balanced” districts), the district
pays the entire Tier 1 funding and any additional revenues are recaptured as part of Chapter 41
provisions.5 In summary, while Tier 1 funding is designed to provide a fiscally neutral school
finance system, a general decline in property values increases the total cost burden to the state,
and state education funding would need to increase in order to maintain equity in Tier 1 funding.
5
As part of Chapter 41 recapture, districts must remit local tax revenues that exceed the Tier 1 allotment, which in
2012-13 was $4,950 (thus any Tier 1 funding generated by property wealth that exceeds $495,000 per WADA as of
2012-13 is recaptured). These districts reduce their wealth per WADA by purchasing attendance credits or agreeing
to educate students in a neighboring district.
SCHOOL FINANCE EQUITY IN TEXAS
12
To meet budgets deficits for the 2011-12 school year, the legislature could not fund the
entire Tier 1 allocation. Legislators were divided over whether to cut budgets across the board, or
whether to protect lower-wealth districts. The 82nd Legislature reached a compromise that it
would fund 92.4% of Tier 1 funding for 2011-12 for all districts and 98.0% in 2012-13, but for
2012-13, the state would implement greater funding reductions for higher-wealth districts
(Davis, Dawn-Fisher, McKenzie, Rainey & Wall, 2014).
Tier 2 funding allows districts to provide “enrichment” programs through additional
taxes. School boards have discretion to increase tax rates above $1.00 up to $1.04 (or 0.04%
additional property tax).6 Any remaining tax increases up to the maximum $1.17 must receive
voter approval. The state equalizes tax bases on tax increases up to $1.06. The equalization is
based on the assessed property value of Austin ISD, which when House Bill 1 was enacted in
2006 was the district at the 95th percentile of assessed property value per pupil. Given the
assessed property value per pupil in Austin ISD in 2013-14, each $0.01 increase in the tax rate in
Austin ISD increased revenues by $59.97 per WADA. Thus for districts that generate, for
example, $40.00 for each one cent of tax per WADA, the state provides an additional $19.97 per
WADA as part of Tier 2 funding. Because the state equalizes the tax base for the first $0.06
beyond $1.00, these six “pennies” of tax levy are referred to as “golden pennies.” Golden
pennies are also not subject to recapture. Additional tax increases from $1.07 up to the maximum
of $1.17 are called copper pennies because the state only equalizes tax bases to the extent that
6
Although most of the discussion here and elsewhere (see TTARA, 2014) focuses on districts with $1.00
compressed M&O tax rate, not all districts are at this rate. Districts with compressed tax rates below $1.00 (i.e.,
those that had tax rates below $1.50 when tax rates were compressed in 2006) can levy more than $0.04, in
proportion to the amount their tax rate falls below the $1.00 cap. A total of 473 of the 1,020 independent school
districts that are governed by elected school boards are below the $1.00 compressed tax limit and 541 have exactly
$1.00 compressed rates. The remaining 6 districts are “special law” districts that, because of a 1953 law providing
districts in counties greater than 700,000 with the authority to levy tax rates up to $2.00 if approved by voters, have
compressed tax rates between $1.01 and $1.09.
SCHOOL FINANCE EQUITY IN TEXAS
13
each one cent of tax per WADA generates $31.95 of funding (instead of $59.97). Copper pennies
are also subject to recapture for property wealthy districts. By the 2013-14 school year, 1,006
districts (98%) set local property taxes for Tier 2 funding at least to the $1.04 level, and of those
351 (34% of all districts) had tax rates beyond that amount through voter approval (TEA, 2016).
Target revenue funding. The finance system is further complicated by a second layer of
tax policies, called the Target Revenue System. The Target Revenue System is a “hold harmless”
clause (preventing districts from losing funding as a result of a policy change), established
through House Bill 1 in response to the ruling in West Orange Cove v. Neeley. When tax rates
were compressed in 2006 by one-third, the state reimbursed all districts for the lost revenues
using tax revenues from other sources.7 To ensure that no district lost funding as a result of the
2006 tax relief, House Bill 1 guaranteed that districts would receive the greatest of three amounts
for the 2006-07 school year: a) 2005-06 funding; b) 2006-07 expected funding given the
district’s previous tax rate; or c) 2006-07 expected funding given the district’s new, compressed
tax rate (i.e., the formula funding for that year). This amount was termed the target revenue. The
legislature defined the adjusted target revenue as the target revenue plus an additional $275 for
each high school student and $2,500 for each employee on the state salary schedule (through a
legislature-mandated salary increase). Districts with adjusted targeted revenue that differs from
their formula funding allocation receive funding called Additional State Aid for Tax Reduction
(ASATR). Some districts receive ASATR funding only to offset Chapter 41 recapture payments
that would put them below their adjusted target revenue. In 2013-14, 303 districts (30% of
districts, 16% of ADA) received ASATR funding (TTARA, 2014).
7
The legislature used tax revenues generated from new tobacco, motor vehicle, and franchise taxes, which were
deposited into the Property Tax Relieve Fund and used to reimburse districts for revenue lost through the tax rate
compression.
SCHOOL FINANCE EQUITY IN TEXAS
14
School district bonds. A final component pertains to local school bonds that are used to
pay for school buses and facility updates and construction. Districts issue bonds by gaining local
voter approval to charge residents an additional local property tax – an Interest and Sinking
(“I&S”) tax – to repay a bond (Gamkhar & Koerner, 2002). In 2013-14, Texas school districts
raised $4.9 billion through I&S tax revenues (19.9% of all state and local tax revenues, TTARA,
2014). The revenues raised through I&S taxes are not subject to Chapter 41 recapture and the
funding base on which I&S taxes are levied are not necessarily equalized across high- and lowpoverty districts. The state equalizes tax bases for I&S taxes only up to $35 per student for each
penny (0.01%) of property tax. Districts must apply for tax base equalization and the state
provides only a limited amount funding up until $250 per student has been allocated (funding the
lowest-property wealth districts first).8
School Funding in Texas and Nationally
The Texas school system currently educates over 5 million students under a budget of
approximately $54 billion for the 2012-13 school year (TEA, 2016). Tables 1 and 2 present
summary statistics of the Texas school finance system. The first two columns of Table 1 report
the unadjusted and adjusted federal per pupil revenues in Texas from 1994-95 to 2012-13. The
next two columns show the unadjusted and adjusted state and local per-pupil revenues. These
figures are adjusted for geographic and other cost differences specific to districts in Texas
(described further in the methods section). The final four columns present the same data for all
U.S. districts over the same time period. For both Texas and nationally, the 2009-10 school year
was the first time in at least 15 years (i.e., as far back as data are available) that nominal state and
8
This foundation program is called the Instructional Facilities Allotment. The Legislature also authorized the
Existing Debt Allotment (EDA) foundation program in 1999 to assist low-wealth districts in repaying existing bonds
(TEA, 2016). The EDA provides $35 dollars per student per penny of tax rate up to $0.29.
SCHOOL FINANCE EQUITY IN TEXAS
15
local funding decreased from the prior year. Federal funding saw its largest increase that year,
when stimulus funding was distributed, but federal funding declines in subsequent years for both
Texas and the nation. In the years prior to 1998-99, Texas districts received approximately equal
to or greater than the national average state and local funding per pupil. Since the 1999-00 school
year, Texas has provided districts with less state and local funding than national average, even as
the average poverty rate has been 5-6 percentage points above the national average. Given the
higher poverty rates in Texas compared to the rest of the country, the state has historically
received more federal dollars per pupil than the average U.S. district (before applying cost
adjustment to Texas districts).9
Table 2 shows differences in average student demographics and resources in high- and
low-poverty districts, in 2007-08 and 2012-13, for Texas districts and for all other districts in the
country. In both Texas and the rest of the country, poverty rates and eligibility for the FRL
program increased from before to after the recession. For example, from 2007-08 to 2012-13, the
average poverty rate for districts in Texas at or above the 75th percentile of poverty rose from
33% to 36%. Nationwide, the average poverty rate for the highest poverty districts rose from
25% to 30%. The bottom panel of Table 2 shows that in both Texas and nationally, resource
advantages for higher poverty districts narrowed and resource gaps for higher poverty districts
increased. For example, in 2007-08, districts in the bottom quartile of the poverty distribution
(wealthier districts) received total per-pupil revenues (PPR) of $11,343 per student, whereas
those in the top poverty quartile received $12,142, a difference of $799. By 2012-13, wealthier
districts received $213 more in total revenues. Nationally, higher poverty districts received $640
more dollars per-pupil before the recession, but $410 more after the recession. Finally, from
9
Note that the adjusted funding levels for the United States are the same as the unadjusted funding levels. Methods
for cost-related adjusted are described in the methods sections below.
SCHOOL FINANCE EQUITY IN TEXAS
16
2007-08 to 2012-13, Texas districts in the bottom quartile of poverty saw a 0.2 FTE increase in
the number of teachers per 100 students, while districts in the bottom quartile experienced a 0.4
FTE decline in the number of teachers per 100 students; similar trends existed nationally. These
numbers provide cursory evidence that higher poverty districts incurred a disproportionate
impact of recessionary budget cuts. These differences may also be due to changes in other costrelated factors such enrollment, other student demographics, or the cost of living. In the
following section, I describe my analytic approach to exploring this issue further.
Data and Analytic Approach
Data
The analyses combine district-level student demographic data from the National Center
of Education Statistics Common Core of Data, finance and child poverty rates from the U.S.
Census Bureau Small Area Income and Poverty Estimates, and data provided by the Texas
Education Agency, Public Education Information Management System (PEIMS) that include
information on property wealth and assessed value per pupil, local tax rates, Chapter 41
recapture payments, and the number of students in Texas enrolled in special programs. Although
these data span school years 1994-95 to 2012-13, my primary interest is in school years 2007-08
to 2012-13.10 I also use the Education Comparable Wage Index (Taylor & Fowler, 2006) to
account for differences in the cost of personnel across labor markets using the. Finally, for
school years 2008-09 to 2012-13, I combine these data with district-level grade 3-8 achievement
data provided by the Stanford Education Data Archive (Reardon et al., 2016).
The analytic dataset includes a total of 248,331 district-year observations over 19 years
(19,318 in Texas) including 12,723 districts observations in 2012-13 nationally and 1,004 in
10
In the four years prior to 1998-99, the proportion of students classified as limited English proficient is not
available and I backwards impute these variables for districts with non-missing values in 1998-99.
SCHOOL FINANCE EQUITY IN TEXAS
17
Texas. The 2012-13 sample for Texas districts excludes 8% of all educational locations because
20 are regional education service centers and 42 are charter or special enrollment districts (which
do not have reliable data). I also exclude outlier district-observations that have more than
$70,000 in total per-pupil revenues in any particular year (a total of 23 in 2012-13 and 0.2% of
all districts that would otherwise have been in the sample). Eight of these cases were school
districts in Texas and each of those involved districts with extremely high assessed property
values. For example, Rankin ISD had an assessed property value of $11.3 million per WADA for
school year 2012-13, far above the average of $562,000 for that school year. As a result, the
district’s local funding amounted to $87,532 per pupil, representing 89% of its total funding. The
preferred model excludes these outliers because they distort the general relationship between
funding and poverty rates. In various sensitivity analyses, I rerun all analyses including these
districts and find that the results are similar, except that the magnitudes of resource disparities
are greater because most outlier districts have low rates of poverty and higher per-pupil funding.
Analytic Approach
Assessing differences in resources and outcomes across otherwise similar high- and
low-poverty districts. For the analyses that address research question 1, I adjust per-pupil
revenues in order to assess how much funding each district receives, relative to other districts
with similar cost factors. Prior research suggests districts with lower total enrollment have higher
production costs because of diseconomies of scale (Adams & Foster, 2010; Gronberg, Jansen,
Taylor & Booker, 2005). Similarly, greater population sparsity increases the cost of
transportation and other expenses that are out of the control of school districts (Duncombe &
Yinger, 2010). Districts in labor markets with higher average salaries also face higher costs
because they must pay higher salaries to attract the same quality of workforce, compared to
SCHOOL FINANCE EQUITY IN TEXAS
18
otherwise similar districts in lower labor cost areas (Taylor & Fowler, 2005). Finally, districts
with greater proportions of students enrolled in special education, classified as limited English
proficient (LEP), or from low-income families face greater costs (Ladd, 2012). Districts with
very high concentrations of poverty face added challenges associated with peer interactions
(Hanushek, Kain, Markman & Rivkin, 2003).
I use two approaches to examine how average funding in otherwise similar high- and
low-poverty districts changed during the recession. In the first approach, described in equation 1
below, I estimate separate regressions for each year. The model includes state fixed effects, φds,
the district poverty rate and its square, and interactions between poverty variables and the state
fixed effects (labeled φds * f (POVERTYds) in equation 1). This modeling strategy allows the
relationship between poverty rates and outcomes to vary by state and year. I first estimate perpupil state and local revenue (PPRds) across all districts and states, in each school year from
1994-95 to 2012-13, indexing for districts (d) and states (s):
PPRds = β0 + φds + f (POVERTYds) + φds * f (POVERTYds) +  COST_FACTORSds + εds
(1)
The vector labeled COST_FACTORSds includes controls for geographic differences in the
cost of labor (Taylor & Fowler, 2005), the percent of students in the district with individualized
education plans (IEPs), the percent classified as LEP, district enrollment size (dummy variables
for whether the districts has between 2,000 and 500 students and less than 500), and population
density as measured by a set of 6 dummy variables indicating the degree of the district’s
urbanicity in a particular year. I do not adjust for inflation as my interest lies in funding gaps
across districts. Student demographics are only weakly correlated with student poverty rates (less
than 0.4 in most cases) and therefore capture additional unique variation in local cost factors.
The error term, εds, captures differences in the per-pupil revenues across districts within
SCHOOL FINANCE EQUITY IN TEXAS
19
states in the level of funding state school finance systems provide to districts with otherwise
similar observable cost factors. These differences may arise if state finance systems are
compensating districts for unobserved cost factors such as career and technical education
programs or for higher proportions of low-incidence (high cost) special education students (the
data only permit controlling for the percent of students with IEPs, but not specific special
education categories). Differences may also arise simply from idiosyncrasies in state school
finance systems that allow two otherwise similar districts to receive different levels of funding in
the same year, which prior research suggests may happen in some cases (this issue is referred to
as horizontal equity; see Rose & Weston, 2013 and Rolle & Lui, 2007 for examples).
I then compute the predicted value of per-pupil revenues for districts in Texas at census
poverty rates of 10%, 20%, and 30%.11 These values translate roughly to the 10th, 50th, and 90th
percentiles, respectively. The preferred model includes all districts in the U.S. because my goal is
to compare districts in Texas to otherwise similar districts nationally. The state-by-poverty rate
fixed effects allow the relationship between poverty rate and funding level to vary by state.
Calculating the post-estimation predicted values provides adjusted funding rates at particular
points in the poverty distribution (rather than just a coefficient for the poverty rate). In various
extensions, I replace poverty rate with poverty rate percentile (within each state and year) and the
percent FRL, run models on Texas districts only, and add various Texas-specific covariates that
align with student weights in the Texas school finance system. Because districts with otherwise
similar cost factors may receive different levels of funding, I use the standard error of the
predicted values to determine if differences in funding levels between high and low-poverty
11
Adjusted per-pupil reviews are estimated using the margins command in STATA, which computes the predicted
value of the outcome measure at specified values (i.e., at particular poverty rates and for particular states), holding
all other variables constant at their observed levels (see Cameron & Trivedi, 2009 for more information on marginal
predictions. The approach described here is similar to the one used in Baker (2014).
SCHOOL FINANCE EQUITY IN TEXAS
20
districts are statistically significant. Prior literature suggests that differences in funding of 5% are
educationally significant and an increase in funding of 20% can close two-thirds of the gaps in
outcomes between children from high- and low-income families (Jackson et al., 2014).
In the second approach to examining how the relationship between funding and poverty
rates changed over time, I explicitly test the statistical significance (and assess the magnitude) of
how funding gaps changed from 2007-08 to 2012-13. I estimate a model similar to equation 1,
this time pooling years and interacting the poverty variables with state-by-year fixed effects. In
order to compare the pre-recession time period (2007-08) and the post-recession time period
(2012-13), I limit the sample to a six-year period, from 2007-08 to 2012-13. The null hypothesis
in these models is that the funding gap did not change from 2007-08 to 2012-13 in Texas. I test
the null hypothesis by examining whether the interaction between the poverty variable and the
2012-13 year fixed effect is statistically different from zero (because the Texas-by-2007-08 year
fixed effect is the reference group for all other state-by-year fixed effects). All other poverty rate
and state-by-year fixed effects interactions show how the change in the funding gap across states
in particular years differed from Texas.
The model is similar to a difference-in-difference (DID) framework (Bertrand et al.,
2004), except that both high- and low-poverty groups were “treated” (by recessionary funding
cuts) and the alternate hypothesis being tested is that the treatment effects differed from one
group (high-poverty districts), compared to the other (low-poverty districts). For this reason, I
explicitly examine the assumptions necessary for causal interpretation under a DID framework.
For both approaches described above, I exchange the outcome measure, state and local
per-pupil revenues, with a number of alternate funding and resource variables, including total
funding per pupil, average staff salaries, and the number of teachers, counselors, support staff,
SCHOOL FINANCE EQUITY IN TEXAS
21
and total staff per 100 students. For the second portion of research question 1, I examine how the
achievement gap changed over this time period by replacing the dependent variable with
measures of achievement on norm-referenced standardized exams (from Reardon et al., 2016).
Exploring the underlying mechanisms of changes in resource gaps. To address the
second research question, I use two approaches to examine potential underlying causes of
changes in funding disparities during and after the recession. First, I examine whether changes in
local tax revenues varied across high-, middle-, and low-income districts in Texas. On the one
hand, prior research suggests lower-poverty, higher-wealth districts may have greater capacity
than high-poverty districts to increase local tax revenues in response to state funding declines
(Hoxby, 1998; Odden & Clune, 2009; Picus, 1991). On the other hand, because high-poverty
districts typically receive a greater proportion of funding from state revenues (Kirst, Goertz &
Odden, 2007), high-poverty districts may feel more pressure to increase local tax rates and local
tax revenues following a decline in state funding.
Second, I explore the underlying determinants of local tax revenues by comparing
changes in local property values and tax rates across the district poverty distribution. Highpoverty districts in Texas may rely especially on M&O tax revenues, since the state equalizes
these tax bases up to the level of the 95th percentile of district property wealth (for the first
0.04% of additional taxes) and because of Chapter 41 recapture, all districts receive $31.95 per
student for each additional tax increases beyond 0.04% and up to the maximum of 0.17%. In
contrast, lower-poverty districts may rely on I&S tax increases because these districts do not
need the property wealth equalization associated with M&O taxes and because I&S tax revenues
are not subject to Chapter 41 recapture.
In order to compare otherwise similar high- and low-poverty districts, I examine changes
SCHOOL FINANCE EQUITY IN TEXAS
22
in revenue by funding source and changes in local property values and tax rates employing
similar methods used to address research question 1. As before, I regress these outcomes on
poverty rate and its square, controlling for district size, urbanicity, labor costs, and other student
demographics. For models predicting tax rates, I include Texas districts only. I then generate
predicted values at specified points in the poverty distribution (10%, 20%, and 30%) and I test
how results differ when using poverty percentiles and FRL. I also run a pooled-year model to
explicitly test in a single regression how relationships between poverty rates and outcomes
change over time, and include these results in Appendix Table A1.
Findings
Changes in Resources and Outcomes across Districts
Results for research question 1 are shown in Table 3 and Figure 1. Each column in Table
3 is a separate regression predicting different outcomes (only relevant covariates are displayed).
The models pool years 2007-08 to 2012-13 and include interactions between the poverty rate
variables and state-by-year fixed effects, using Texas and 2007-08 as the base year and state.
Because Texas and the year 2007-08 are the base year and state for all other state-by-year
poverty rate interactions, the main effect of the poverty rate represents the relationship between
poverty rate and funding level in 2007-08 in Texas. Thus the coefficient in the first row of the
first column shows that in 2007-08, Texas school districts received about $9 per pupil less for
each one percentage point increase in the district’s poverty rate, other observable district cost
factors being equal. That this coefficient is statistically insignificant for Texas suggests that in
2007-08 there was not a systematic relationship between funding levels and the poverty rate. The
interaction with poverty rate and the state-by-year fixed effect (for 2012-13), shown in the third
row demonstrates how funding differences across the poverty distribution changed in 2012-13,
SCHOOL FINANCE EQUITY IN TEXAS
23
compared to 2007-08. Otherwise similar districts in Texas received $57 per pupil less in state
and local funding for each one percentage point increase in the poverty rate, compared to the
2007-08 school year.
Figure 1 helps put these estimates in perspective by plotting total funding and state and
local funding over time, for districts in Texas with 10% poverty rate (roughly the 10th percentile)
and districts with 30% poverty (the 90th percentile). During the 2012-13 school year, the average
district with 10% poverty received $12,279 per student, whereas an otherwise similar district
with 30% of students in poverty received $10,945 ($1,352 or 11% fewer dollars per pupil). When
federal dollars are included (the graph on left side of Figure 1), higher poverty districts received
$725 fewer dollars per student (5.5% less) in 2012-13, compared to otherwise similar lowerpoverty districts. These differences are both statistically and educationally significant. As Figure
1 shows, this funding gap began to emerge in 2008-09, at the onset of the Great Recession. As
discussed earlier, a persistent funding gap of 6% will likely have tangible consequences if that
funding gap remains over the lifespan of a student’s K-12 experience (Jackson et al., 2014).
All other interactions between the poverty rate and state-by-year fixed effects for school
year 2012-13 show how the relationship between poverty rates and funding levels (i.e., the
funding progressiveness) changed in other states, relative to Texas. The interaction between the
poverty rate and the state-by-year fixed effect for 2012-13 is positive for 41 states, implying that
the Texas school finance system experienced a greater decline in progressiveness than did 41
other states following the Great Recession (from 2007-08 to 2012-13), although this difference is
statistically significant for only eight of those states. Conversely, only seven states experienced a
greater decline in progressiveness than did Texas and only one state, New Mexico, declined by a
statistically significant amount more than Texas (specific results available from the author upon
SCHOOL FINANCE EQUITY IN TEXAS
24
request). Although the funding gap increased in Texas by more than in most other states, across
the country, high-poverty districts, on average, experienced a disproportionate share of the
funding cuts associated with the Great Recession (not shown but available upon request).
Results for total per-pupil funding in Texas (local, state, and federal revenues), shown in
column 2 of Table 3, are similar to the results for state and local funding, except that prior to the
recession, otherwise similar districts in Texas received about $13 more in funding for each
additional percentage point of students in poverty (row 1 of column 2). As shown in columns 3-8
of Table 3, these relative declines in funding for high-poverty districts were accompanied by
relative decreases in spending, average salaries, and staff per student. Like total per-pupil
funding, per-pupil expenditures and average staff salaries were both positively related to poverty
rates in Texas in 2007-08; however, those relationships were reversed by 2012-13. Prior to the
recession, higher-poverty districts also had more staff and more teachers per student; however,
by 2012-13, they had significantly fewer per student than lower-poverty districts.
Whether the Great Recession spending cuts caused the funding gap depends on two
underlying assumptions common to a difference-in-difference framework (Bertrand et al., 2004;
Lovenheim & Willén, 2015). The first is that the treatment and comparison groups followed
similar trends prior to treatment. As Figure 1 shows, during the three years leading up to the
recession, from 2005-06 to 2007-08, high- and low-poverty districts followed similar trends in
both total revenues and state and local funding, as well as in other outcomes (not shown, but
available upon request).12 The second assumption is that the treatment was “unanticipated” or
12
Although not the main focus of this study, funding between high- and low-poverty districts also diverged
following the 2000-01 recession, though not as severally (shown in Figure 1). Starting two years after the 1993
passing of Senate Bill 7 (which established Tier I and II funding and Chapter 41 recapture payments), and for every
school year from 1994-95 to 1999-00, high- and low-poverty districts in Texas received approximately equal total
and state/local per-pupil funding, after adjusting for local cost factors based on districts nationally. From 2000-01 to
2005-06, per-pupil funding in high-income districts grew at a faster pace than lower-income districts, resulting in a
funding gap that persisted until 2005-06. Then in 2005-06 (when House Bill 1 was passed, which compressed tax
SCHOOL FINANCE EQUITY IN TEXAS
25
exogenous and no other factors differentially impacted treatment or “control” groups (high- and
low-poverty districts) at the same time as the treatment took place (Angrist & Pischke, 2009).13
Although the Texas finance system was being reviewed in the State Supreme Court, and a lower
court had declared it unconstitutional, no policy changes were made to the finance system, other
than the recession-induced budget cuts. Descriptive statistics show that the proportion of special
education students and students classified as LEP did not change significantly in either high- or
low-poverty districts.14 In short, evidence that these two assumptions are tenable suggests that
the trends in the outcomes measures would have continued to be parallel if not for the Great
Recession and therefore changes in trends can be attributed to the recessionary funding cuts.
The state also experienced an increase in the achievement gap between high- and lowpoverty districts from 2008-09 to 2012-13 (data are only available for this five-year period).
These results are shown in Table 4. The top panel of Table 4 shows that in 2008-09, low-poverty
districts scored between 0.049 and 0.054 standard deviations (SD) lower in English Language
Arts on standardized statewide assessments (the Texas Assessment of Knowledge and Skills and
the State of Texas Assessments of Academic Readiness) and between 0.044 and 0.062 SD lower
in Math. By 2012-13, the gap for English Language Arts had increased by between 0.005 and
rates and set up the Target Revenue System), the funding gap closed and high and low-poverty districts again
received roughly equal funding for three years (and followed similar trends) from 2005-06 to 2007-08.
13
The model being tested differs from a traditional difference-in-difference (DID) framework. In a traditional DID,
a policy is implemented, one group is affected by the policy, and another group (the control group) is not. The null
hypothesis is that the difference in the outcome measure between groups before and after policy implementation will
not change significantly (Bertrand et al., 2004). In this study, both groups receive treatment, but the null hypothesis
is that recessionary-budget cuts impacted high- and low-poverty districts equally. The poverty rate variable thus acts
as the treatment group indicator. In various extensions and sensitivity analyses, I use time-invariant dummy
variables for high and low poverty districts, the percentile of poverty rate with state and year, and the percent of
students eligible for FRL, results are generally consistent (see Table 6)
14
Other changes that may have taken place, such as increases in average poverty rates or decreases in property
values are considered part of the treatment effects. For example, prior research suggests the Great Recession
disproportionately impacted lower-income and less educated families, potentially pushing them into poverty or
forcing a change in residential locations (e.g., Hendey et al., 2012). Table 2 bears this out. The percent of students in
FRL increased by 6.6 percentage points for districts below the 25th percentile of poverty from 2007-08 to 2012-13,
and by 17.6 percentage points for districts above the 75th percentile of poverty).
SCHOOL FINANCE EQUITY IN TEXAS
26
0.018 SD. The changes in the achievement gap from 2008-09 to 2012-13 in Math are less
consistent. The gap for grade 4 increased by 0.006, though that difference is not statistically
significant and in grades 7 and 8, the gap decreased by 0.008 and 0.009 SD (although those
differences are only significant at p<0.1).
Changes in achievement gaps over time likely increased for a multitude of reasons, many
of which could be related to changes in socioeconomic conditions associated with the Great
Recession. For example, lower-income families experienced greater increases in unemployment
compared to higher-income families (Hoynes et al., 2012). Similarly, states may have reduced
the availability of social services available in neighborhoods where 30% of families live in
poverty, whereas families in neighborhoods with 10% poverty rates rely less on these services.
However, because achievement data are only available from 2008-09 forward, establishing
parallel trends prior to the recession, and identifying a causal inference associated with the
recession, is not straightforward. That said, the results in Table 4 confirm that income-based
achievement gaps increased in Texas during and immediately following the Great Recession.
These results are important given that high-poverty districts also saw decreases in the level of
resources available in their neighborhood schools, compared to lower-poverty districts.
Exploring Mechanisms for Funding Changes
The second research question explores changes in revenues by funding source associated
with recession-based budget cuts and changes in local property values and taxes. These results
are shown in Figure 2 and in Table 5. Figure 2 shows that low-, middle-, and high-poverty
districts all experienced decreases in state funding, but managed to increase their local funding
from 2007-08 to 2012-13 (the years displayed in Figure 2 and in this discussion refer to the
spring semester, so 2008 refers to 2007-08). In 2010, federal funding increased because of the
SCHOOL FINANCE EQUITY IN TEXAS
27
stimulus funding and Texas used this money in place of state funding. From 2009 to 2010, state
funding decreased by 4.1% ($192 per pupil) in low-poverty districts and 8.7% ($526 per pupil)
in otherwise similar high-poverty districts. The decline in state funding in 2012 and 2013
resulted from the budget cuts established in the 82nd Texas Legislative session, but the state
protected high-poverty districts in these years, particularly in 2013. Specifically, while lowpoverty districts lost 1.2% ($54 per pupil) and 12.5% ($568 per pupil) of state funding in 2012
and 2013, respectively, compared to 2011 funding, high-poverty districts saw a decline of only
0.3% ($18) and 0.5% ($27) in those years, relative to 2011 funding. Over the course of the
recessionary period, from 2008 to 2013, state funding decreased by 20.9% ($1,052 per pupil) in
low-poverty districts and by 10.8% ($670 per pupil) in high poverty districts.
However, over that same period, low-poverty districts increased local per-pupil funding
by $2,000 (31.6%), whereas high-poverty districts increased local funding by only $576 per
pupil (11.9%). Thus over the course of the recession, from 2008 to 2013, the poorest districts
saw a modest decline in nominal state and local funding of $94 per pupil (0.8%), while the
wealthiest districts experienced an increase in state and local funding of $947 per pupil (8.3%).
Because federal funding was relatively stable (increasing by $1 per pupil for low-poverty
districts and $97 per pupil in high-poverty districts), the increase in total per-pupil funding from
2008 to 2013 was $948 (7.8%) for low-poverty districts and only $4 (0.3%) for high-poverty
districts, leaving a $725 gap for school year 2012-13. As noted earlier, these relative decreases in
funding for high-poverty districts had real effects on resources, resulting in relative declines in
teacher salaries and the number of total staff and teachers per student (as shown in Table 3).
The results shown in Figure 2 suggest that low-poverty districts were unable to increase
local revenues to make up for declines in state tax revenues. Table 5 provides explanation for
SCHOOL FINANCE EQUITY IN TEXAS
28
why this may have occurred. First, Panel A shows that in 2008, 12.0% of low-poverty districts
were assessing the maximum M&O tax rate of $1.17, whereas 16.8% of otherwise similar highpoverty districts had reached the maximum M&O rate, a difference of 4.8 percentage points
(differences are shown in Panel E). By 2013, 22% of low-poverty districts and 32% of highpoverty districts were assessing the maximum local tax rates. The final column of Table 5 shows
how this outcome changed from 2008 to 2013. While the percent of low-poverty districts levying
the maximum M&O tax rate increased by 9.6 percentage points, the percent for high-poverty
districts increased by 15.5 (as shown in the first row of Panel E, final column, the difference in
these two numbers, 5.8 percentage points, is not statistically significant).15 Similarly, Panel B
shows that high-poverty districts increased local tax rates at a faster rate than low-poverty
districts. High-poverty districts increased average M&O tax rates by $0.024 (from $1.056 in
2008 to $1.080 in 2013), while low-poverty, wealthier districts increased local tax rates from
$1.050 to $1.065, an increase of $0.015 (the difference in these increases of 0.009 is not
statistically significant).
Recall that the state does not equalize tax bases for I&S taxes (used to repay bonds) to the
same extent as M&O taxes. Perhaps not surprisingly, low-poverty districts increased I&S tax
rates at a faster rate than high-poverty districts. As shown in the final column of Panel C, lowpoverty districts increased I&S taxes by an average of $0.054, whereas high-poverty districts
increased I&S taxes by an average of $0.015. Finally, low-poverty districts experienced slower
rates of growth in per-pupil property value over this same time period.16 These results are shown
15
The outcomes shown in Table 5 are based on regressions that include the covariates listed in equation 1. As with
other outcomes, I also ran models that pool years and interact year fixed effects with poverty rate variables, similar
to a traditional difference-in-difference framework. These results are shown in Appendix Table A1.
16
Although not shown, enrollment rates were relatively constant over the time period, on average, for both high- and
low-poverty districts.
SCHOOL FINANCE EQUITY IN TEXAS
29
in Panel D. Altogether, the results shown in Table 5 suggest that while high-poverty districts
increased their local tax rates, many to the maximum amount possible, the amount of funding
generated from these taxes was limited both by their relatively slower property value growth and
by the states’ funding cuts that lowered the equalization of local tax bases. At the same time,
low-poverty districts successfully compensated for decreases in state funding by increasing their
I&S tax rates (which are not subject to recapture and are not equalized to the same extent as
M&O taxes) and by experiencing significant growth in their local property values.
Extensions and Sensitivity Analyses
Table 6 shows specification checks for the preferred model predicting state and local
funding per pupil. The first model shown in column 1 is identical to equation 1 for school year
2012-13. As before, Texas is the reference category for all other state fixed effects, and only the
poverty rate coefficients that correspond to Texas are reported (results from the full model are
available upon request). The coefficient for poverty rate of -70.91 suggests that otherwise similar
districts in Texas receive $70.91 less per student for each one-percent increase in the district
poverty rate.17
Model 2 of Table 6 omits controls for the percent of students in SPED and with LEP and
the coefficient for poverty rate declines slightly, suggesting that students in these programs may
generate additional funding, since poverty rates are weakly, but positively correlated with these
student demographic variables. Next, Model 3 includes 23 outlier districts (8 of which are in
Texas) with extremely high per-pupil revenues (above $70,000). Because these districts are
lower-poverty on average, the coefficient on district poverty increases when outlier districts are
included. The next model replaces the census poverty rate variable with the percent of students
17
This coefficient is for districts with average poverty rates because the poverty rate variable is mean-centered
within states and years and I include the square of poverty rates as an additional covariate.
SCHOOL FINANCE EQUITY IN TEXAS
30
eligible for FRL. The FRL coefficient decreases relative to the preferred model, which uses
census based poverty, most likely because FRL rates understate the level of poverty at high
levels of poverty (the slope of the line showing the relationship between poverty rate and FRL
rate flattens out as poverty rates increase). Model 5 uses the percentile of poverty rate, rather
than the percent. The coefficient again is lower than in the preferred model, this time because a
one-percentile increase represents a larger change in poverty rates (which range from about 2%
to 40%, excluding outliers).
The final two models are both run on Texas districts only. Model 6 repeats Model 1, this
time for just Texas districts. Then Model 7 replaces control variables with Texas-specific
covariates provided by the TEA that correspond to specific student weights in the Texas funding
formula.18 The consistency of the poverty rate variable between Models 6 and 7 suggests that
even when adjusting funding levels with variables specific to the Texas school finance system,
high-poverty districts still received substantially less state and local funding per pupil in 2012-13
than did low-poverty districts. In both models, the coefficient for district poverty is larger than in
the preferred model and both the cost of labor index and % SPED variables change direction
(other covariates are similar to the preferred model).19 Thus contrary to national averages, the
Texas school finance system actually provides less state and local funding for districts with
18
The variables include the percent of students in career and technical education, in high, middle, and low cost
special education categories, in English as a Second Language programs, in Bilingual programs, and the percent
deemed “at risk.” Correlations among these variables are all below 0.40.
19
Model 6 shows the % of high-cost SPED students in the row labeled % SPED. The model also includes controls
for the % of students in middle- and low-cost SPED categories. Based on cost studies of SPED categories (e.g.,
Duncombe & Yinger, 2008) and the student weights in the Texas school finance system, low-cost SPED categories
include: learning disability, intellectual disability, and emotional disturbance; middle-cost SPED categories include:
orthopedic impairment, other health impairment, auditory impairment, visual impairment, speech impairment, and
non-categorical early childhood; and high-cost categories are: deaf-blindness, autism, and traumatic brain injury.
The correlations between the federally reported percent of students with IEPs in Texas in 2012-13 (from the
Common Core of Data) and the percent of students with low-, middle- and high-cost special education categories
(from TEA) are 0.187, 0.523, and 0.790, respectively.
SCHOOL FINANCE EQUITY IN TEXAS
31
higher costs of labor and, after taking into account other local cost factors, less funding for
districts with higher rates of both IEPs (based on the Common Core of Data, Model 6) and highcost special education students (based on TEA data, Model 7). Because these variables are
negatively correlated with poverty rates in Texas, but not in other states, the Texas only models
have larger (more negative) coefficients for poverty rate, compared to models that include all
districts nationally. Despite these differences, Table 6 demonstrates that regardless of the model
specification, after taking into account local cost factors, there is a strong and significant
negative relationship between poverty rate and funding levels across Texas school districts.
Discussion
This study finds that the Great Recession inequitably impacted higher-need districts in
Texas, and these disproportionate impacts were greater than in most other states across the
country. Like most states, the Texas legislature faced a substantial budget deficit and elected to
cut funding for public education, using federal dollars to fill gaps in 2009-10 and reducing
funding by over $4 billion in 2010-11. Although legislatures reached a compromise between
cutting funding evenly for all districts protecting high-poverty districts, given the complexities
and multiple layers embedded in the Texas school finance system, the highest-need districts were
inequitably impacted by the funding cuts.
The study uncovers some of the specific mechanisms that contributed to the growing
funding gaps in Texas over time. While high-poverty districts increased their tax rates at a faster
rate than low-poverty districts, their relative decline in property values, coupled with the
decreasing tax base equalization provided by the state, limited the benefits of these tax rate
increases. At the same time, low-poverty districts were able to raise local revenues at a faster rate
than high-poverty districts by issuing bonds (through I&S tax increases), which are not subject to
SCHOOL FINANCE EQUITY IN TEXAS
32
recapture and redistribution, and for which the state does not equalize tax bases for high-poverty
districts. Finally, federal stimulus funding was evenly distributed across high and low poverty
districts in Texas and was therefore insufficient in preventing disadvantaged students from
bearing a disproportionate impact of state funding cuts.
One of the key takeaways from this study is that high-poverty districts in Texas levy
higher local taxes than otherwise similar low-poverty districts, but receive less state and local
funding, and these gaps expanded following the Great Recession. Thus a challenge facing Texas
policy makers is to reform the school finance system such that high-poverty districts receive at
least as much funding as otherwise similar low-poverty districts. To estimate the cost of this
policy and show which regions in Texas would benefit, I simulate a budget policy that equalizes
state and local funding across the poverty distribution. I do this by estimating the predicted state
and local revenues at each point in the poverty distribution from 0% to 40%, in 2.5% increments
for the 2012-13 school year. The results are shown in Panel A of Figure 3, which displays the
relationship between the adjusted state and local per-pupil funding and district poverty rate. The
estimated per-pupil funding for districts with poverty rate of 2.5% is $13,247, whereas otherwise
similar districts with 30% of students in poverty receive $10,945 and districts with a 40%
poverty rate receive $10,914. Thus for districts with between 30% and 32.5% poverty rates, I add
$2,302 to their simulated per-pupil funding and $2,333 for districts with poverty rate between
40% and 42.5%. After imputing the simulated per-pupil funding variable for each range of
poverty rates, I re-estimate the relationship between poverty rates and funding level and display
the results of this model in Panel B of Figure 3. As is clear, this simulated policy would provide,
on average, approximately $13,247 to otherwise similar districts, across the poverty distribution.
Figure 4 shows that each of the 20 educational service regions in Texas would experience
SCHOOL FINANCE EQUITY IN TEXAS
33
increases in their average per-pupil funding across districts, but some would benefit more than
others. On average, districts in Fort Worth would receive an additional $1,523 per student,
whereas the 12 districts in El Paso would receive $2,258 in additional funding per pupil on
average. As shown in Appendix Table A2, this policy would cost the state $9.1 billion,
representing a 16.7% increasing in state and local funding. This policy simulation highlights
which districts and regions are underfunded in Texas given their local cost factors and sheds
light on the difficult choices facing the Texas legislature. Whether the current or simulated
funding levels represent an adequate level of funding is beyond the scope of this study.
Conclusion
Although the Texas Supreme Court’s recent decision declared the finance system
constitutional, the court’s opinion made clear that substantial reforms were needed to fix the
outdated and otherwise “byzantine” system. This study shows that in addition to distributing
state and local funding inequitably, the funding system is not recession-proof. The combination
of the foundation formula, guaranteed tax base, and Chapter 41 recapture did not successfully
protect high-poverty districts from experiencing a disproportionate impact of the recessionary
budget cuts, despite their relatively greater effort to increase local tax rates. As the state
considers reforming its school finance system, it may benefit from considering how the highestneed districts will be protected from the next major state budget cut.
The failure of the Texas school finance system to protect high-need districts is not
specific to the state. The analyses described here found that across the country, state funding cuts
disproportionately harmed high-poverty districts. Other states may therefore look to Texas as a
leader in designing a new school finance system designed to both provide an equitable level of
funding and to withstand the negative impacts of future economic recessions.
SCHOOL FINANCE EQUITY IN TEXAS
34
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SCHOOL FINANCE EQUITY IN TEXAS
37
FIGURE 1
Adjusted total revenue per pupil (federal, state, and local) and state and local revenue per pupil
in Texas, for low-poverty school districts (10% poverty rate) and high poverty school districts
(30% poverty rate), 1994-95 to 2012-13
Note: Adjusted revenue per pupil is adjusted for a geographical cost of wage index (Taylor, 2005), districts size,
population density, and the proportion of students enrolled in special education and with limited English proficiency.
SCHOOL FINANCE EQUITY IN TEXAS
38
FIGURE 2
Per-pupil revenues by funding source for low- (Panel A) middle- (Panel B) and high-poverty
districts (Panel C)
Panel A: Low-poverty (10%)
Panel B: Mid-poverty (20%)
Panel C: High-poverty (30%)
FIGURE 3
Marginal effect of poverty rate on state and local per-pupil funding, holding constant other
district cost factors, actual (Panel A) and simulated policy (Panel B)
9000
0
0
3000
6000
Linear Prediction
12000
12000
9000
6000
3000
Linear Prediction
Panel B: Simulated state and local adjusted PPR
15000
15000
Panel A: State and local adjusted revenue per-pupil (PPR)
0 .025 .05 .075 .1 .125 .15 .175 .2 .225 .25 .275 .3 .325 .35 .375 .4
Poverty Rate
0 .025 .05 .075 .1 .125 .15 .175 .2 .225 .25 .275 .3 .325 .35 .375 .4
Poverty Rate
SCHOOL FINANCE EQUITY IN TEXAS
39
FIGURE 4
Change in average per-pupil funding under a simulated policy that provides all districts with the
average amount received by districts with 0% poverty
SCHOOL FINANCE EQUITY IN TEXAS
40
TABLE 1
Federal and state / local revenue per pupil in Texas and the United States, 1994-95 to 2012-13
Texas
Year
Federal rev.
per pupil
Unadj
Adj.
United States
State and local rev.
per pupil
Unadj
Adj.
Mean
pov.
rate
Districts
Fed.
rev. per
pupil
State and
local rev.
per pupil
Mean
pov.
rate
1994-95
$429
$338
$6,147
$6,083
24.0%
1,022
$369
$6,222
16.4%
1995-96
$432
$361
$6,560
$6,472
23.2%
1,023
$371
$6,453
16.9%
1996-97
$457
$375
$6,738
$6,641
23.2%
1,026
$389
$6,743
16.9%
1997-98
$487
$399
$7,055
$7,014
20.7%
1,027
$429
$7,045
14.5%
1998-99
$526
$434
$7,235
$7,161
20.7%
1,027
$467
$7,431
14.5%
1999-00
$573
$471
$7,723
$7,718
19.4%
1,025
$520
$7,862
13.4%
2000-01
$600
$492
$8,059
$8,082
19.6%
1,024
$560
$8,455
13.5%
2001-02
$697
$582
$8,432
$8,514
19.1%
1,022
$660
$8,758
13.8%
2002-03
$802
$658
$8,803
$8,953
20.0%
1,024
$753
$9,052
14.2%
2003-04
$896
$735
$8,810
$9,027
19.4%
1,022
$823
$9,403
14.1%
2004-05
$993
$786
$9,145
$9,423
21.6%
1,022
$872
$9,909
15.4%
2005-06 $1,079
$880
$9,791
$9,927
21.2%
1,014
$905
$10,457
15.7%
2006-07 $1,010
$829
$10,554 $10,758 20.4%
1,003
$884
$11,197
15.3%
2007-08 $1,007
$851
$11,005 $11,099 20.3%
1,008
$893
$11,772
15.6%
2008-09 $1,052
$896
$11,192 $11,282 21.4%
1,002
$1,079
$11,893
17.0%
2009-10 $1,693 $1,461
$10,995 $11,246 23.6%
1,009
$1,456
$11,638
18.5%
2010-11 $1,540 $1,357
$11,069 $11,224 23.7%
1,007
$1,368
$11,967
19.0%
2011-12 $1,316 $1,132
$10,993 $11,132 23.1%
1,007
$1,111
$12,286
19.1%
2012-13 $1,106
$911
$11,186 $11,353 23.6%
1,003
$1,016
$12,528
19.0%
Note: Adjusted revenue per pupil is adjusted for a geographical cost of wage index (Taylor, 2005), districts size,
population density, students enrolled in special education, students classified as limited English proficient and
students poverty concentration.
Districts
13,224
13,277
13,280
13,274
13,301
13,307
13,264
13,291
13,271
13,109
13,112
12,649
12,979
12,966
12,863
12,848
12,753
12,769
12,564
SCHOOL FINANCE EQUITY IN TEXAS
41
TABLE 2
Average characteristics for school districts with equal to or below the 25th percentile of poverty
rate and equal to or above the 75th percentile (within state and year), Texas and the United
States, 2007-08 and 2012-13
All US school districtsa
Texas school districts
2007-08
≤ 25th
≥ 75th
2012-13
≤ 25th
≥ 75th
2007-08
≤ 25th
≥ 75th
2012-13
≤ 25th
≥ 75th
Average district characteristics and student demographics / outcomes
% Poverty
9.4%
32.9%
12.5%
35.9%
7.2%
24.9%
10.8%
29.8%
% FRL
32.7%
56.7%
39.3%
74.3%
22.3%
56.6%
30.9%
62.5%
% LEP
2.9%
7.4%
4.9%
12.6%
2.6%
6.4%
2.8%
6.3%
% SPED
10.7%
11.5%
8.8%
9.5%
13.0%
15.1%
12.8%
15.0%
% URM
24.9%
63.9%
32.5%
66.8%
12.8%
32.3%
18.8%
36.3%
Grade 3 ELA
0.314
-0.761
0.112
-1.022
0.590
-0.418
0.549
-0.542
Grade 3 Math
0.330
-0.583
0.316
-0.594
0.529
-0.388
0.470
-0.503
Fresh. grad. rate
85.1%
74.4%
n/a
n/a
87.2%
76.6%
n/a
n/a
Dist. Enroll.
5,654
4,536
6,624
6,054
4,651
3,252
4,864
3,603
Cost of Wage
1.32
1.12
1.45
1.30
Num. of
248
265
247
262
districts
School inputs (unadjusted outcome measures)
1.34
1.21
1.43
1.33
3004
3074
3164
2924
Total PPR
11,343
12,142
12,420
12,206
12,653
13,293
13,617
14,027
St./local PPR
10,702
10,677
11,719
10,636
12,156
11,833
12,940
12,458
Per-pup. Exp.
8,792
10,195
9,230
10,041
10,463
11,336
11,448
12,003
Avg. salaries
39,095
36,804
42,362
39,963
50,466
46,069
52,398
49,576
Staff per 100 students
All Staff
12.8
14.6
13.0
14.2
14.9
17.7
14.3
16.3
Teachers
6.8
7.6
7.0
7.5
8.0
8.8
7.8
8.1
Guid. Coun.
0.3
0.3
0.3
0.3
0.3
0.4
0.3
0.3
Sup. Staff
0.5
0.6
0.6
0.7
0.4
0.5
0.4
0.6
all districts in the United States except for Texas. In order to match the analytic sample, I also omit Hawaii, the
District of Columbia, charter districts, and outlier districts that receive extremely high per-pupil revenues. The
figures are generally similar when I include these districts (available upon request).
Note: FRL stands for free and reduced price meals; LEP stands for limited English proficient, SPED stands for
special education, and URM stands for underrepresented minority. Grade 3 ELA and Math refer to the district
average grade 3 standardized exam scores, adjusted by NAEP to allow for national comparisons (Reardon et al.,
2016) The poverty rates at the 10th and 90th percentiles within Texas and nationally correspond approximately to
10% and 30% poverty rates, respectively (I use percent rather than percentiles throughout the analysis for reasons
explained in the text, but run all models using percentiles as specification check).
a
SCHOOL FINANCE EQUITY IN TEXAS
42
TABLE 3
Regression coefficients predicting various school inputs by poverty rate for Texas school
districts, adjusted for other cost factors, 2007-08 to 2012-13
Adj. state/ Adj. total
local PPR
PPR
Staff per 100 pupils
Adj. total
PPE
Avg.
Salaries
All Staff
Teachers Gd. Coun. Sup. Staff
-8.756
12.964
44.207***
62.482
0.091***
0.019**
-0.001
0.004+
(17.212)
(17.837)
(12.631)
(38.989)
(0.013)
(0.007)
(0.001)
(0.002)
-0.954
-0.647
-0.103
-3.049+
-0.001*
-0.001**
0.000+
0.000
(0.771)
(0.799)
(0.565)
(1.746)
(0.001)
(0.000)
(0.000)
(0.000)
Pov. rate x
2012-13
-57.179*
-47.808*
-25.885
-68.964
-0.044*
-0.023*
0.000
0.000
(23.280)
(24.124)
(17.083)
(52.759)
(0.017)
(0.010)
(0.001)
(0.003)
Pov. rate squ. x
2012-13
2.962**
2.534*
1.03
1.951
0.002*
0.001**
0.000
0.000
(1.049)
(1.087)
(0.770)
(2.377)
(0.001)
(0.000)
(0.000)
(0.000)
Poverty rate
Poverty rate
squ.
R-squared
0.563
0.545
0.623
0.77
0.651
0.576
0.437
0.394
Note: all school inputs are adjusted for cost factors including district size, population sparcity, the percent of
students classified as limited English proficient and in special education, and an educational geographic cost of wage
index.
SCHOOL FINANCE EQUITY IN TEXAS
43
TABLE 4
Regression coefficients predicting average achievement by poverty rate for Texas school
districts, adjusted for other educational cost factors, 2007-08 to 2012-13
Grade 3
Grade 4
Grade 5
Grade 6
Grade 7
Grade 8
English Language Arts
Poverty rate
Poverty rate sq.
Pov. rate x
2012-13
Pov. rate sq. x
2012-13
R-squared
-0.054***
-0.052***
-0.059***
-0.057***
-0.056***
-0.049***
(0.003)
(0.003)
(0.003)
(0.003)
(0.003)
(0.003)
0.001***
0.001***
0.001***
0.001***
0.001***
0.001***
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
-0.005
-0.013***
-0.008*
-0.014***
-0.010*
-0.018***
(0.004)
(0.004)
(0.004)
(0.004)
(0.004)
(0.004)
0.000
0.000
0.000*
0.000
0.000
0.001**
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
0.623
0.633
0.643
0.630
0.629
0.617
Math
Poverty rate
Poverty rate sq.
Pov. rate x
2012-13
Pov. rate sq. x
2012-13
R-squared
-0.048***
-0.044***
-0.044***
-0.055***
-0.062***
-0.058***
(0.003)
(0.003)
(0.003)
(0.003)
(0.003)
(0.003)
0.001***
0.001***
0.001***
0.001***
0.002***
0.001***
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
0.000
-0.006
-0.005
-0.002
0.008+
0.009+
(0.004)
(0.004)
(0.004)
(0.004)
(0.004)
(0.005)
0.000
0.000
0.000
0.000
0.000
0.000
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
0.559
0.559
0.559
0.552
0.572
0.562
SCHOOL FINANCE EQUITY IN TEXAS
44
TABLE 5
Determinants of per-pupil funding for high-, mid-, and low-poverty districts, 2007-08 to 2012-13
2007-08
2008-09
2009-10
2010-11
2011-12
Panel A: Districts with the highest possible local M & O property tax rate ($1.17)
12.0%
14.2%
13.6%
20.7%
18.7%
Low poverty (10%)
(0.018)
(0.022)
(0.023)
(0.030)
(0.027)
14.1%
17.3%
19.5%
21.6%
22.9%
Mid poverty (20%)
(0.013)
(0.014)
(0.014)
(0.015)
(0.015)
16.8%
20.8%
25.7%
26.5%
28.1%
High poverty (30%)
(0.021)
(0.021)
(0.021)
(0.020)
(0.022)
2012-13
Diff. 2007-08
to 2012-13
21.6%
(0.029)
27.2%
(0.016)
32.3%
(0.021)
0.096**
(0.034)
0.131***
(0.021)
0.155***
(0.030)
Panel B: Local district property tax rate for maintenance and operations (M & O, mostly equalized tax base)
1.050
1.054
1.053
1.065
1.062
1.065
0.015*
Low poverty (10%)
(0.004)
(0.004)
(0.005)
(0.005)
(0.005)
(0.005)
(0.006)
1.053
1.060
1.062
1.064
1.068
1.073
0.020***
Mid poverty (20%)
(0.002)
(0.002)
(0.002)
(0.003)
(0.002)
(0.002)
(0.003)
1.056
1.064
1.070
1.072
1.076
1.080
0.024***
High poverty (30%)
(0.003)
(0.003)
(0.003)
(0.003)
(0.003)
(0.003)
(0.004)
Panel C: Local district property tax rate for bond repayment (I & S, partially equalized tax base)
0.174
0.184
0.199
0.215
0.207
0.228
Low poverty (10%)
(0.006)
(0.007)
(0.008)
(0.009)
(0.009)
(0.009)
0.154
0.163
0.171
0.178
0.179
0.185
Mid poverty (20%)
(0.004)
(0.004)
(0.004)
(0.005)
(0.004)
(0.004)
0.148
0.153
0.159
0.160
0.165
0.162
High poverty (30%)
(0.006)
(0.006)
(0.005)
(0.005)
(0.006)
(0.005)
Panel D: Local district property value per pupil
413,888
489,346
533,268
Low poverty (10%)
(17822)
(25859)
(25503)
331,841
407,946
423,784
Mid poverty (20%)
(11424)
(17189)
(14893)
246,815
302,749
311,129
High poverty (30%)
(16418)
(23683)
(20340)
0.054***
(0.011)
0.031***
(0.006)
0.015+
(0.008)
671,796
(39964)
526,761
(19904)
409,542
659,922
(37955)
535,245
(18991)
409,205
684,936
(37952)
528,525
(18680)
390,788
271,047***
(41928)
196,684***
(21897)
143,973***
(24974)
(22643)
(24095)
(29156)
Panel E: Differences between low- and high-poverty districts
0.048+
0.065*
0.121***
0.058
Dist. with highest
poss. M & O rate
(0.027)
(0.030)
(0.031)
(0.036)
0.006
0.010+
0.017**
0.008
Average M & O
rate
(0.005)
(0.005)
(0.006)
(0.006)
-0.027
-0.031
-0.040
-0.055
Average I & S rate
(0.008)
(0.009)
(0.010)
(0.011)
-167,073 -186,597 -222,140 -262,254
Prop. value per
pupil
(24232)
(35065)
(32621)
(47125)
0.094**
(0.034)
0.014*
(0.006)
-0.043
(0.010)
-250,717
0.106**
(0.036)
0.015*
(0.006)
-0.066
(0.011)
-294,147
0.058
(0.046)
0.009
(0.008)
-0.039
(0.013)
-127,074
(44196)
(44954)
(51069)
Note: districts repay bonds by raising tax rates though Interest and Sinking (“I & S”) taxes. These taxes are not
subject to recapture for equalization purposes and the state does not necessarily provide an equal tax base for highpoverty districts (see text for more detail).
SCHOOL FINANCE EQUITY IN TEXAS
45
TABLE 6
Regression coefficients predicting the relationship between district child poverty rate and state
and local funding per pupil (specification checks of preferred model, school year 2012-13)
Poverty rate x
Texas FE
Poverty rate sq.
x Texas FE
% SPED
% LEP
(1)
(2)
(3)
(4)
(5)
(6)
(7)
-70.91***
-61.15***
-122.39***
-41.32***
-15.39***
-114.77***
-110.18***
(16.01)
(15.98)
(21.04)
(6.82)
(3.77)
(25.96)
(28.63)
2.15**
2.05**
2.94**
0.01
0.14
3.29**
3.01**
(0.72)
(0.72)
(0.95)
(0.23)
(0.14)
(1.10)
(1.14)
82.75***
69.91***
100.82***
83.94***
-228.71**
732.56*
(8.58)
(11.34)
(8.71)
(8.56)
(70.64)
(287.05)
15.79***
(4.61)
23.75***
(6.10)
22.66***
(5.03)
14.79**
(4.57)
-10.95
(21.77)
1.50
(34.15)
Enrollment less
than 500
1016.25***
(85.37)
973.21***
(85.33)
1065.13***
(112.95)
927.91***
(84.47)
1015.66***
(85.52)
1103.84*
(480.71)
1260.34*
(497.60)
Enrollment 500
to 2000
3463.88***
(112.70)
3437.75***
(112.67)
3888.50***
(148.91)
3363.50***
(112.15)
3458.64***
(112.81)
2986.74***
(609.38)
3097.76***
(644.09)
Cost of wage
index
2715.69***
(265.71)
2897.41***
(262.55)
2016.45***
(351.02)
2563.75***
(259.20)
2623.95***
(267.16)
-4767.6***
(1049.79)
-5066.3***
(1054.96)
N
12,723
12,723
12,746
12,723
12,723
1,004
1,004
R-squared
Preferred modela
Excl. % LEP and
% SPED
Including
outliers (n=23)
FRL instead of
census poverty
Pov. percentile
w/in states
Texas only
0.597
X
0.594
0.474
0.605
0.596
0.171
0.172
X
X
X
X
X
TX only, w/ TX
X
specific cov.
a
The preferred model is described in Equation 1 and is similar to Column 1 of Table 4 (except that models in Table
4 pool years and the models in this table are for 2012-13 only). These models interact the poverty rate variables with
a set of state fixed effects, using Texas as the reference category. The interactions between poverty rate and all other
state fixed effects, as well as the controls for urbanicity are not shown. The first row is the main effects of poverty
and its square (i.e., the effects of poverty for Texas). Model 2 is identical to the preferred model, except that controls
for the percent of students classified as limited English proficient (LEP) and enrolled in special education (SPED)
are excluded. Model 3 repeats the preferred model, this time including 23 outliers with total per-pupil expenditures
greater than $70,000 (most of which are small and have low poverty rates). Model 4 uses the percent of student
eligible for free and reduced price meals (FRL) in place of the census poverty rate. Model 5 is identical to the
preferred model, except it is run on Texas districts only and Model 6 is also run on Texas districts only, but it uses
Texas specific variables including the percent of students in career and technical education, in high, middle, and low
cost special education categories, in English as a Second Language programs, in Bilingual programs, and deemed at
risk. Correlations among these variables are all below 0.40. I also tested models that weight districts by enrollment
and results were within the range of those shown here (the coefficient for poverty rate is -49.66).
SCHOOL FINANCE EQUITY IN TEXAS
46
Appendix Tables
APPENDIX TABLE A1
Regression coefficients predicting local tax rates, whether districts are levying the highest
possible tax rate, and the local property values, adjusted for other educational cost factors,
2007-08 to 2012-13
Poverty rate
Poverty rate squ.
Pov. rate x 2012-13
Pov. rate squ. x 201213
Highest possible
tax rate
M & O tax rate
(fully equalized)
I & S tax rate
(partially equalized)
Property value per
pupil ($1,000s)
0.239
(0.214)
0.135
(0.957)
0.319
(0.283)
-0.507
(1.282)
0.037
(0.035)
-0.0080
(0.154)
0.037
(0.045)
0.026
(0.205)
-0.231***
(0.061)
0.767**
(0.270)
-0.075
(0.080)
0.112
(0.360)
-852.955***
(253.193)
-1181.8330
(1124.202)
-675.571*
(331.809)
3970.792**
(1497.812)
Covariates for cost differences
1.079***
0.203***
-0.221***
-2861.307***
% SPED
(0.212)
(0.034)
(0.060)
(248.242)
-0.211**
-0.036**
-0.028
-6.670
% LEP
(0.079)
(0.012)
(0.022)
(91.280)
0.071***
0.008**
-0.054***
48.588**
Enrollment less than
500
(0.016)
(0.003)
(0.004)
(18.648)
0.133***
0.015***
-0.127***
216.798***
Enrollment 500 to
2000
(0.020)
(0.003)
(0.006)
(23.296)
0.078*
0.019**
0.200***
-343.303***
Cost of wage index
(0.038)
(0.006)
(0.011)
(43.809)
Year fixed effects
0.043*
0.009**
0.000
-4.690
2009
(0.021)
(0.003)
(0.006)
(24.098)
0.076***
0.013***
0.002
73.277**
2010
(0.021)
(0.003)
(0.006)
(24.400)
0.101***
0.016***
0.008
64.420**
2011
(0.021)
(0.003)
(0.006)
(24.706)
0.117***
0.020***
0.006
53.902*
2012
(0.021)
(0.003)
(0.006)
(24.426)
0.161***
0.025***
0.012*
20.607
2013
(0.021)
(0.003)
(0.006)
(24.744)
-0.002
1.035***
0.255***
731.791***
Constant
(0.044)
(0.007)
(0.012)
(51.558)
0.044
0.043
0.370
0.147
R-squared
Note: models also include covariates for urbanicity (a set of six dummy variables with urban districts as the
reference category) and interactions with the poverty rate variables and other year fixed effects. Because continuous
variables are mean centered, the constant is the predicted value for urban districts with enrollment over 2,000 and
average labor costs, poverty rate, and percent of students in special education (SPED) and with limited English
proficiency (LEP).
SCHOOL FINANCE EQUITY IN TEXAS
47
APPENDIX TABLE A2
Summary statistics by region and results of a policy simulation that equalizes state and local funding across poverty rates
State and Local Funding
Region
Edinburg
Corpus Christi
Victoria
Houston
Beaumont
Huntsville
Kilgore
Mount Pleasant
Wichita Falls
Richardson
Fort Worth
Waco
Austin
Abilene
San Angelo
Amarillo
Lubbock
Midland
El Paso
San Antonio
Total
Number of
districts
Avg.
enroll.
Total
enroll.
Total state &
local funding
Total funded
added
Funding
increase
(%)
$2,133
$2,052
$1,911
$1,729
$1,869
$2,031
$2,042
$2,101
$1,874
$1,554
$1,523
$1,962
$1,543
$2,005
$1,998
$1,676
$2,055
$1,628
$2,258
$1,958
11,011
2,666
1,407
21,416
2,531
3,135
1,751
1,209
1,036
9,237
7,194
2,011
6,716
1,076
1,123
1,456
1,559
2,993
14,871
8,058
396,407
103,956
53,459
1,070,803
80,977
175,570
168,048
56,824
38,330
738,982
546,734
154,811
369,404
45,201
47,185
85,906
81,087
80,824
178,447
410,975
$4,025,504,130
$1,081,076,715
$586,728,674
$11,857,282,169
$907,036,108
$1,958,033,558
$1,787,708,572
$632,467,554
$461,675,477
$8,325,970,526
$6,174,782,067
$1,755,953,394
$4,156,715,577
$520,808,505
$555,747,723
$1,052,698,568
$918,535,868
$917,027,089
$1,927,168,134
$4,318,035,400
$845,382,042
$213,325,564
$102,151,997
$1,851,436,484
$151,374,180
$356,614,364
$343,124,003
$119,385,425
$71,849,394
$1,148,563,660
$832,788,179
$303,778,555
$570,105,703
$90,627,898
$94,254,894
$143,992,906
$166,600,646
$131,578,935
$402,953,553
$804,616,058
21.0%
19.7%
17.4%
15.6%
16.7%
18.2%
19.2%
18.9%
15.6%
13.8%
13.5%
17.3%
13.7%
17.4%
17.0%
13.7%
18.1%
14.3%
20.9%
18.6%
$1,867
4,864
4,883,930
$54,624,427,286
$9,117,825,012
16.69%
Current
(unadj)
Current
(adjusted)
Sim.
Diff. (adj.
- sim.)
36
39
38
50
32
56
96
47
37
80
76
77
55
42
42
59
52
27
12
51
$10,012
$11,489
$13,800
$9,498
$10,253
$10,714
$9,652
$9,357
$11,889
$9,542
$10,924
$10,379
$10,497
$12,661
$12,878
$12,871
$14,074
$19,497
$9,892
$9,961
$10,155
$10,399
$10,975
$11,073
$11,201
$11,152
$10,638
$11,130
$12,045
$11,267
$11,294
$11,343
$11,252
$11,522
$11,778
$12,254
$11,328
$11,346
$10,800
$10,507
$12,392
$12,458
$12,887
$12,805
$13,069
$13,187
$12,683
$13,237
$13,915
$12,822
$12,818
$13,303
$12,795
$13,524
$13,784
$13,928
$13,393
$12,995
$13,117
$12,476
1,004
$11,185
$11,185
$13,059
Note: Sim. refers to the state and local funding under a policy simulation in which all districts in Texas would receive funding rates
equal to the lowest-poverty districts, after adjusting for local cost differences. The policy simulation is demonstrated in Figure 3 of the
main text.