School Finance 101 provides data and thoughts on public and private school funding in the U.S. Bruce Baker is a Professor at Rutgers University specializing in school finance, education policy & quantitative analysis.
Resources, when considering school size, are positively associated with growth;
The productivity of large charter operators in Newark – TEAM and North Star in particular – depends on how we treat school size in our models;
Jumps in student growth percentiles across the board between 2014 and 2015 are hard to explain as a function of substantive policy change – where policy and contextual changes had been happening gradually prior to and throughout the period.
From any study of the effects of changes in policy and practices on student outcomes, what we really want to know – where positive outcome effects are observed – is what can be done to distribute those positive effects across more children and settings, and/or yield even stronger positive effects.
The conclusion offered in the reports is that shifting students to higher value-added schools has yielded positive growth in language arts. And thus, the logical policy conclusion is that more students should be shifted to high value-added schools. The larger the share of students placed in these schools, the higher the overall system performance will be. This may be an oversimplification, but is certainly the message that some are taking home from the reports.[i]
Figure C1 shows the present distribution of students across district and charter schools within the city of Newark. One might characterize the system as housing 3 separate K-12 school districts with a handful of smaller operators of select grade-level schools. The three comprehensive districts in question are NPS, TEAM and North Star. Analyses in the previous section (setting aside the scale question) suggest that TEAM and NPS perform similarly and that North Star tends to be the higher producer of student growth. Thus, the assertion would be that if we shift more students into North Star, more students should be better off and the system as a whole should produce better outcomes on average.
Thus the “between-school” treatment here is essentially defined as “North Starring” more students. But what exactly does that mean? Here, we attempt to provide some relevant context. Our intent is to separate the treatment of “North Starring” into those actions district leaders and policymakers might take which are desirable and scalable, versus those practices and conditions that are likely to be influencing measured outcomes but may not be scalable or desirable.
Distribution of District and Charter School Enrollments in Newark 2017
Unfortunately, a consistent feature of North Star Academy over time has been the tendency to serve and retain less needy student populations than the broader population in the district as well as other charter operators including TEAM. Neither TEAM nor North Star serve many children with severe disabilities, but North Star serves very few with disabilities of any degree of severity. The reports’ analysis fails to parse severity of disability – its influence on individual student growth, the potential peer effects of the presence of children with severe disabilities, or the extent to which larger shares of children with severe disabilities create resource allocation constraints and pressures in schools. This is a substantial omission, but one which could not be remedied given the lack of data precision.
North Star has also consistently served proportionally fewer of the lowest income children. Again, the reports’ analysis fails to parse income levels across children, using only indicators of children qualified for either free or reduced priced lunch. We provide illustrations in this section demonstrating why this matters.
North Star serves effectively no children with limited English language proficiency, in part because North Star caters to a predominantly black student population from Newark’s black neighborhoods, which remain geographically segregated from the city’s Hispanic and other ethnic neighborhoods and are home to non-English speaking families.
Special education rates
We start with disability rates based on 2016 data, which are actually more similar across the three Newark districts than prior years during the period studied. Figure C2 shows the overall percent classified and percent with mild specific learning disability, other health impairment, or speech/language disability. Newark Public Schools has an overall rate higher than either of the other two and more than double that of North Star. The vast majority of children with disabilities in North Star have relatively mild and less-costly disabilities. The case is similar for TEAM. Notably, TEAM and NPS have similar rates of mild disability students, but NPS has far more severe disability students.
This finding actually serves to rebut a common argument of charter advocates regarding their lower disability classification rates. Charter advocates frequently assert that effective early grades interventions reduce their need to classify students with disabilities.[ii] But even the most effective interventions would only be successful at reducing the number of children identified as having mild specific learning disabilities – children on the margins of classification. Interventions would be far less likely to reduce classification of children with traumatic brain injury, intellectual disability, emotional disturbance, or autism. It is those more severe and costly disabilities which are more prevalent in the NPS schools. Whether valid in other settings or not, this argument is unlikely to hold for differences in special education classification rates between NPS and TEAM Academy.
Figure C3 provides a more detailed breakdown, revealing that a very large share of North Star’s disability population are children with Speech/Language impairment, and no particular cognitive, behavioral, or other severe impairment which would either divert more substantial shares of resources or directly influence student achievement growth.
Most analyses of Newark district and charter school performance, matching on or controlling for disability status in the aggregate, presume that these children in North Star are equivalent to children with far more severe disabilities in NPS. Some studies specifically find that children with disabilities in charter schools show greater gains than children with disabilities in district schools.[iii] In this case (and most other contexts we’ve studied), such a finding – applying a single measure of “disability” – would be spurious, in that obviously children with only speech language impairment on average would achieve greater growth on standardized assessments than children with multiple and severe learning disabilities.
To summarize, these disability population differences alone, which go unmeasured when using a single “has disability” dummy variable, affect:
relative growth between charter and district school students,
the nature of peer groups (proportions of marginal vs. more severe disability students integrated into regular classrooms could affect the pace of the curriculum and disruptions in classroom time, which likely would affect growth),
the extent to which higher need student populations create resource pressures and drive reallocation away from “general education” students.
While on the one hand these population differences raise questions regarding assumptions about the effectiveness of North Star Academy, they also raise questions about the scalability of “North Starring” and its effects on the system as a whole, even if North Star is particularly effective with the students that it does serve and retain. The more non-disabled students a single large district in the city enrolls, the more disabled students the other districts will have to serve.
Low income concentrations
During the “reform” period under study, substantive differences in the shares of children qualified for “free” lunch existed. These gaps have been closing in recent years; however, North Star continues to serve a smaller share of children who fall below the 130% income threshold for poverty than either TEAM or NPS.
The Chin et al. study compares students only on the basis of “free + reduced” priced lunch. Single dummy variables on free and reduced-price lunch are relatively meaningless in a context where nearly all children fall below the higher threshold (less than 185 percent of the income poverty level). In fact, those qualified for reduced price lunch are among the more relatively “advantaged” students in the district and schools with higher shares of those students tend to have higher average scale scores.
Table C1 shows the correlations between percent free lunch, percent reduced-price lunch, percent free and reduced-price lunch, and growth and scale score outcome measures across Newark Schools, including district and charter schools. To summarize:
Percent free lunch has a small negative correlation with growth percentiles and a large negative correlation with scale scores.
Percent reduced lunch is positively correlated with growth and strongly positively correlated with scale scores.
Percent free and reduced-priced lunch is only modestly negatively correlated with scale scores.
This is because those students from families between the 130% and 185% income threshold for poverty happen to be the more “advantaged” students in this high-poverty, urban setting. That is, at the school level, percent free and reduced-priced lunch tells us little about the “risk” of low performance largely because nearly all children in Newark fall below the 185% income threshold for poverty. In addition, it is likely that a substantial number of those who are not identified as qualifying for either in fact do qualify, yet are not listed as such because their families did not apply.
By extension, using a single dummy indicator as a covariate in student (or school) level analysis that assumes nearly all Newark students are socioeconomically identical to one another will lead to specious findings. Because shares of lower income children vary systematically by sector – between NPS and charters – those conclusions will be biased in favor of charters generally, and North Star specifically. While North Star has shown impressive unconditional growth, it has continued to serve fewer of the poorest children in the city. TEAM also served fewer of the poorest children throughout the period studied.
Correlations between Growth, Achievement Level and Low Income Populations in Newark (2016)
PARCC Math 8
PARCC ELA 8
PARCC Math 8
PARCC ELA 8
% Free or Reduced
In addition to compromising validity of high versus low value-added findings, the tendency of between-school mobility to sort students by income status raises scalability concerns. Put bluntly: as one school/district in a high poverty “choice” space serves more of the less-poor (among the poor) students, others must pick up the difference. Concentrating higher-poverty populations in specific schools potentially creates negative peer effects that are not picked up when using test score histories as measures of peer characteristics.
English Language Learners
Figure C5 shows that among the three districts in Newark, only NPS serves any children with limited English language proficiency. As about 10% of the NPS population is LEP/ELL, this, again, raises questions about scalability. The more that charters in the space serve non-LEP/ELL children, the more LEP/ELL children are concentrated in the district schools. As with poverty and disability, it is also desirable to have access to more fine-grained data on the level of language proficiency.
There remain large differences in shares of English Language Learners Served
Figure C6 and Figure C7 track cohort attrition rates for three sequential cohorts attending TEAM and North Star. Figure C6 shows the total cohort enrollments and Figure C7 shows the cohort enrollments for black male students. Figure C8 shows the average ratio of the 12th grade enrollment to the 7th grade enrollment of the same cohort of students.
Seventh Grade Cohorts, year after year, are reduced by 25 to 40% as they matriculate to 12th grade
Seventh Grade Cohorts of Black Boys, year after year, are reduced by 28 to 65% as they matriculate to 12th grade
In Part A, we argue that those studying school reforms must give more thorough consideration to history and context. In Newark, that context includes:
The importance of the Abbott rulings, which brought resource advantages to Newark and similar New Jersey school districts that have effects even in the present.
The proliferation of charter schools – specific to Newark, charters with significant resource advantages over the public district schools.
The stabilization of poverty rates in Newark, even as poverty increased in surrounding districts.
All of these factors have influenced Newark’s schools, even if they are rarely discussed.
In Part B, we argue that analyses of the relative effectiveness of Newark’s schools over time should make efforts to consider variations and changes in resources available and should also consider factors that constrain those resources. Analyses should also consider how changes to outcome measures might compromise model estimates and eventual conclusions. We undertake such an analysis and find:
Much of the “growth” of Newark’s test scores, relative to the state, can be explained by the transition from one form of the state test (NJASK) to another (PARCC) in 2014-15. There is no evidence Newark enacted any particular reform to get those gains, which are actually quite modest.
The fact that other high-poverty districts close to Newark showed similar small gains in growth also suggests those gains are not unique to Newark.
Newark’s high-profile charter schools are not exceptionally efficient producers of test score gains when judged by statistical models that account for resource differences.
In Part C, we explore some of the substantive differences that exist between Newark’s high “value-added” charter schools and district schools (and other charter schools) yielding less “positive” outcomes. Those differences include:
Newark’s high-profile charters enroll substantially fewer special needs students proportionally. The special needs students those charters do enroll tend to have less severe and lower-cost learning disabilities.
North Star Academy, one of Newark’s highest-profile charters, enrolls substantially fewer students in the greatest economic disadvantage. Recent studies, however, do not acknowledge this difference, leading to unwarranted conclusions about North Star’s relative productivity.
Newark’s charters enroll very few Limited English Proficient (LEP) students.
Newark’s high-profile charters show substantial cohort attrition: many students leave between grades 7 and 12 and are not replaced. As those students leave, the relative test scores of those school rise.
Newark’s high-profile charters have very high student suspension rates.
There exist a handful commonly cited bodies of evidence and deceitful smokescreens intended to undermine the importance of equitable and adequate financing for schools. Here’s my abbreviated rebuttal sheet to what I call the School Money Myths & Misdirects:
First, many including Eric Hanushek assert that school spending has climbed for decades but test scores have remained “virtually flat.” Others have countered, however, that in fact test scores have not remained flat, especially when accounting for changes to the student population. Still others have pointed out the fallacious logic of this argument noting that “between 1960 and 2000 the rate of cigarette smoking for females decreased by more than 30 percent while the rate of deaths by lung cancer increased by more than 50 percent over the same time period” seemingly implying that smoking cessation increases lung cancer, if one applies the same flawed reasoning.I rebut the overall trends for student outcomes and spending in a recent blog post.
Second, many point to (supposed) high spending of the United States and relatively low scores on international assessments as evidence that spending in the U.S. in particular seems unrelated to school quality. Like the long term trend argument, this argument mischaracterizes U.S. students’ performance. It also relies on very poor, not cross-nationally comparable school spending figures, while failing to consider a host of intervening factors.The most thorough rebuttal of this claim can be found in a recent report I wrote with Mark Weber.
Third, in 1986, Eric Hanushek produced the first in a series of “vote count” meta-analyses wherein he tallied the cases in which research studies found positive, negative or non-significant correlations between school resource measures and student outcomes. Finding mixed results, Hanushek concluded “There appears to be no strong or systematic relationship between school expenditures and student performance.”(p. 1162) This claim became a mantra for those denying the connection between spending and school quality. Soon thereafter other researchers applied quality standards to filter existing studies, finding that the preponderance of higher quality studies in fact did find positive correlations.  But these studies pale in comparison in both methodological rigor and relevance to more recent longitudinal studies which consistently find positive effects of school finance reforms on student outcomes.A thorough review of this literature is available in my Shanker Institute report – Does Money Matter in Education.
Fourth, Hanushek and others also continue to rely on anecdotal claims of massive spending increases in Kansas City, Missouri and in the state of New Jersey which failed to lead to any substantive improvement in student outcomes. The Kansas City claims most often mischaracterize the amount, duration and context (desegregation order) of the funding. The New Jersey claims are most conveniently rebutted by Hanushek himself. While in the context of several recent school funding legal challenges Hanushek has asserted “Compared to the rest of the nation, performance in New Jersey has not increased across most grades and racial groups,”  his own more recent work has found: “The other seven states that rank among the top-10 improvers, all of which outpaced the United States as a whole, are Massachusetts, Louisiana, South Carolina, New Jersey, Kentucky, Arkansas, and Virginia.”
Fifth and finally, two arguments that frequently resurface are that:
a) how money is spent matters more than how much; and
b) student backgrounds matter much more than schools and money.
While the assertion that “how money is spent is important” is certainly valid, one cannot reasonably make the leap to assert that how money is spent is necessarily more important than how much money is available. Yes, how money is spent matters, but if you don’t have it, you can’t spend it. Further, those who have more of it, have more latitude in determining how to use it.
The second assertion misses the point entirely. The assertion that student background is more strongly associated with student outcomes than are school resource measures is valid. That finding can either be used as a misdirect, to convince the public that there’s no sense trying to leverage resources to mitigate these disparities or that statement can be viewed as a challenge to be overcome in part through well-crafted state school finance policy and resource allocation. In fact it is precisely because student backgrounds matter so much in determining outcomes that we must figure out how to leverage resources best to offset disadvantages created disparities in backgrounds. Because disparities in student backgrounds are so substantial, the costs of offsetting those disparities can be substantial.
 Carnoy, M., & Rothstein, R. (2013). What do international tests really show about US student performance. Economic Policy Institute, 28.
 Baker, B. D., & Weber, M. (2016). Deconstructing the Myth of American Public Schooling Inefficiency.
E. A. Hanushek, “Economics of Schooling: Production and Efficiency in Public Schools,” Journal of Economic Literature 24, no. 3 (1986): 1141-1177. A few years later, Hanushek paraphrased this conclusion in another widely cited article as “Variations in school expenditures are not systematically related to variations in student performance.” E. A. Hanushek, “The Impact of Differential Expenditures on School Performance,” Educational Researcher 18, no. 4 (1989): 45-62. Hanushek describes the collection of studies relating spending and outcomes as follows: “The studies are almost evenly divided between studies of individual student performance and aggregate performance in schools or districts. Ninety-six of the 147 studies measure output by score on some standardized test. Approximately 40 percent are based upon variations in performance within single districts while the remainder look across districts. Three-fifths look at secondary performance (grades 7-12) with the rest concentrating on elementary student performance” (fn #25).
Greenwald and colleagues explain: “Studies in the universe Hanushek (1989) constructed were assessed for quality. Of the 38 studies, 9 were discarded due to weaknesses identified in the decision rules for inclusion described below. While the remaining 29 studies were retained, many equations and coefficients failed to satisfy the decision rules we employed. Thus, while more than three quarters of the studies were retained, the number of coefficients from Hanushek’s universe was reduced by two thirds” (p. 363). Greenwald and colleagues further explain that: “Hanushek’s synthesis method, vote counting, consists of categorizing, by significance and direction, the relationships between school resource inputs and student outcomes (including but not limited to achievement). Unfortunately, vote-counting is known to be a rather insensitive procedure for summarizing results. It is now rarely used in areas of empirical research where sophisticated synthesis of research is expected” (p. 362).
Hanushek (1997) provides his rebuttal to some of these arguments, and Hanushek returns to his “uncertainty” position: “The close to 400 studies of student achievement demonstrate that there is not a strong or consistent relationship between student performance and school resources, at least after variations in family inputs are taken into account” (p. 141). E. A. Hanushek, “Assessing the Effects of School Resources on Student Performance: An Update,” Educational Evaluation and Policy Analysis 19, no. 2 (1997): 141-164. See also E. A. Hanushek, “Money Might Matter Somewhere: A Response to Hedges, Laine and Greenwald,” Educational Researcher 23 (May 1994): 5-8.
 Jackson, C. K., Johnson, R. C., & Persico, C. (2015a). The effects of school spending on educational and economic outcomes: Evidence from school finance reforms (No. w20847). National Bureau of Economic Research.
In 2011, the Obama administration formed a national equity commission to explore fiscal inequities across U.S. Schools. In one meeting of that commission, participant Eric Hanushek introduced the following table (A-36-1, in Figure 44) from the National Center for Education Statistics to assert that, on average, U.S. States had already raised levels of spending in high poverty districts to the point where, on average, high poverty districts spend more than low poverty districts. This statement is factually correct, based on Table A-36-1 of the 2010 Condition of Education Report, of the National Center for Education Statistics. The implication being that school funding equity is not the problem, but rather, the problem lies with inefficiency in high poverty districts.
There are a few problems with using this table to draw these implications, setting aside that the dollar figures are not adjusted for differences in labor costs across settings. While $10,978 (constant dollars) is in fact higher than $10,850, this difference is hardly enough to provide for the differences in programs and services needed to close achievement gaps between our highest and lowest poverty children. But perhaps most importantly, these broad, national average figures hide substantial variation both across and within states. Many states have highly inequitable school funding systems and many districts and the children they serve continue to be significantly disadvantaged by state school finance systems, ranging from imperfect to god-awful.
In 2014 I produced a report for the Center for American Progress identifying America’s Most Financially Disadvantaged School Districts. This report came about as an extension of a series of blog posts in which I had identified what I referred to as America’s Most Screwed School Districts. It had become increasingly clear to me that the indicators we created for the School Funding Fairness report card, while useful for describing overall patterns, were hiding important disparities within states behind the averages. For example, the disparities I pointed out in the previous section in Massachusetts and New Jersey. These are two of the best, most progressive state school finance systems in the nation, but even in these states there are districts which are high in student poverty and have far fewer resources than the other districts around them. Many districts, and thus the children they serve, were being overlooked in our indicators and subject to mischaracterization by others, without readily available rebuttal.
It is important to understand that the value of any given level of education funding, in any given location, is relative. That is, it does not matter whether a district spends $10,000 per pupil or $20,000 per pupil. It matters how that funding compares to other districts operating in the same regional labor market—and, for that matter, how that money relates to other conditions in the regional labor market. The first reason relative funding matters is that schooling is labor intensive. The quality of schooling depends largely on the ability of schools or districts to recruit and retain quality employees. The largest share of school districts’ annual operating budgets is tied up in the salaries and wages of teachers and other school workers. The ability to recruit and retain teachers in a school district in any given labor market depends on the wage a district can pay to teachers relative to other surrounding schools or districts and relative to nonteaching alternatives in the same labor market. The second reason is that graduates’ access to opportunities beyond high school is largely relative and regional. The ability of graduates of one school district to gain access to higher education or the labor force depends on the regional pool in which the graduate must compete.
Table 1 lists k-12 (unified) districts identified based on 2015 fiscal and poverty data, which have <90% state and local revenue of their labor market average and >150% of the poverty rate. Many other repeat suspects like Philadelphia (w/approximately 90% revenue) continue to lie at the margins. Year after year, Philadelphia and Chicago have appeared as the two most screwed large urban districts. Along with Philadelphia, other Pennsylvania cities including Reading and Allentown face even more dire conditions, and along with Chicago, Illinois districts like Waukegan and Joliet make the list year after year. While Hartford and New Haven in Connecticut have received additional aid in support of their magnet programs, creating an appearance of progressive funding in Connecticut, other districts including Bridgeport, Waterbury and New Britain have been entirely left out. It seems a relatively easy call to suggest that disparities of this type and magnitude are simply wrong – unfair – and should be remedied.
America’s Most Financially Disadvantaged Districts 2015
To put these disparities into context, we know that high poverty districts need not only equal resources but substantially more resources per pupil to achieve common outcomes for their students. One of the more rigorous studies to ask just how much more applied cost models to districts in New York state, finding that the costs associated with each additional child in poverty (U.S. Census poverty income level) were about 1.5 more (2.5 times) the costs of achieving the same outcome measures for children not in poverty. Thus, a district serving 30% children below the poverty line would have costs approximately 75% higher or 1.75 times (.3 x 2.5) per pupil cost for a district with 0% census poverty.
As obviously problematic as these disparities are, they still have their detractors and deniers, which is especially disheartening. Take for example the twitter exchange below between Andy Smarick, Fellow of the American Enterprise Institute, later appointed President of the Maryland State Board of Education and Author of The Urban School System of the Future, and Kombiz Lavasany, a research manager at the American Federation of Teachers. The premise of Mr. Smarick’s book is that urban school systems have failed despite receiving massive resources. According to Mr. Smarick, urban traditional public school districts don’t and can’t work, and must be replaced with a portfolio of privately managed autonomous charter schools. This premise is largely borrowed from a 1997 book by Paul Hill, Lawrence Pierce and Jim Guthrie titled Reinventing Public Education.
In the exchange below, Andy Smarick opines with great confidence that Philadelphia is among those large urban districts which have received massive sums of money, repeatedly, to “prop it up.” The only hint at evidence here is the claim that Philadelphia’s state aid is among the highest in the state. Of course, that’s because Philadelphia is by far the largest district in the state (several times larger than any other district).
I might have taken less offense to Mr. Smarick’s proclamation had I not been under the false impression that most reasonably informed education policy wonks understood that Philadelphia was in fact one of (if not the) nation’s least well-funded large urban districts, operating in the context of one of the nation’s least equitable states. Apparently, it wasn’t so widely understood. Nonetheless, publicly available and easily fact-checkable data were and are pretty clear on this point.
Let’s take a look at Pennsylvania school finance and the position of Philadelphia within that mix. Figure 3 shows Pennsylvania school districts arranged by their poverty rates and by per pupil spending relative to districts in their surrounding labor market. Again, the size of each circle represents the enrollment size of each district. Philadelphia stands out as the large circle in the lower right area of the graph. That is, Philadelphia has a little more than double the poverty rate of all districts in its area, and has less than 80% of the current spending per pupil in 2015. In other words, Philadelphia is the classic case of a “Screwed District” as I originally reported on my blog in June of 2012.
Figure 4 shows the plight of Philadelphia Public Schools over time, from 1993 to 2015. During this period, child poverty rates climbed from just under double the labor market average to over double the labor market average. Throughout the period of over two decades, Philadelphia has received substantively less in per pupil revenue and spent less per pupil on average than surrounding districts, despite having much greater need and facing much higher costs. Despite bombastic rhetoric to the contrary, the Commonwealth of Pennsylvania has done little, if anything, for decades to “prop up” school spending in Philadelphia. Evidence-free bluster to the contrary is reckless and irresponsible.
Among the financially disadvantaged districts of the Commonwealth, are two other eastern Pennsylvania cities – Reading and Allentown. Reading was the subject of a feature article in the Huffington Post by education writer Joy Resmovits back in 2012, in which Resmovits detailed the ground level impact of Reading’s funding plight, including substantial staffing cuts and elimination of the district’s preschool program. Kansas City native Michael Q. McShane, then with the American Enterprise Institute (now with the Missouri-based Show-Me Institute) responded to the Resmovits column in a piece he titled “It’s not about the money” in which he argued: “Ms. Resmovits was right to point to Reading as an example of a property-poor district that cannot raise enough local funds to support education. However, as the 20-year changes in funding show, the state has worked to remedy this shortfall.” McShane’s evidentiary basis for his claim was to show that the percent of Reading’s funding coming from the state had increased over time and was greater than that of other districts. Thus, the state was doing its part and responsibility for any failures should fall squarely on Reading school district officials. Clearly, however, as shown in Figure 5, the state’s efforts have been far from sufficient to remedy the shortfall. The percent of revenue that comes from the state is irrelevant if the sum of state and local revenue remains insufficient. Reading is an especially flagrant case of savage school funding inequalities. Reading is a mid-size city district with nearly 250% of the poverty rate and about 73.6% of the state and local revenue per pupil of the surrounding labor market.
While Philadelphia and Reading are particularly egregious examples of disparities, it is false to assume or make data-free proclamations regarding propping up large city school districts with vast sums of state aid. Figure 6 shows the relative poverty and relative state and local revenue for large city school districts with 50,000 or more students in 2013. Again, Philadelphia and Chicago are most disadvantaged. Boston is most advantaged here, but its margin of poverty difference is still double that of its surroundings and margin of revenue difference only about 30% higher than surroundings. Even Boston’s progressive spending differential falls well short of cost estimates for achieving common outcomes. Thus it should come as no surprise that Boston students’ outcomes continue to fall short.
The 1990s saw a flurry of studies which began to explore equity of resources across schools within districts. These studies revealed significant variation in spending across schools, raising the legitimate concern regarding the effectiveness of state school finance formulas alone for resolving inequitable resources to students. After all, in some states like New York, a single district might serve over 1/3 of all pupils, across over 1,000 schools. Getting enough money to New York City to achieve equity with other districts statewide was one thing, but ensuring that the resources flowed equitably to children across schools within this very large, socially, economically and racially diverse city was another thing entirely.
Over the next decade through the late 2000s, within district inequality became a convenient scapegoat issue for federal policymakers, informed by beltway think tanks. The message that emerged was that due to years of litigation and pressure by state courts, states had largely met their obligations to resolve disparities between local public school districts and that the bulk of remaining disparities were those that persist within school districts. Thus, the most useful exertion of federal pressure is on local district officials and their corrupt policies which drive more money to schools in rich neighborhoods within districts, and away from poor neighborhoods within the same districts.
The political convenience of focusing on within district equity was that federal policy and funding could be leveraged to place pressure on local bureaucrats – school superintendents and local boards of education – to fix their inequitable budget allocations, regardless of how much money was available. It was a simple, revenue neutral solution, one which avoided federal officials placing any pressure on state legislatures and governors to fund more equitable statewide formulas, which might require raising taxes. These federal policies exist today in the form of “comparability” regulations which require that local school districts can show that poor schools receive resources at least comparable to those of rich schools in order to qualify to receive federal Title I funding. Title I has long required that districts supplement, not supplant state and local resources with Title I funds for high poverty schools.
Indeed, it is important that we consider not only the delivery of resources from states to local districts, but also how those resources reach schools and children. But federal attention on within district disparities without regard for between district disparities has created an unfortunate distraction from the larger issue – that many high need school districts simply lack sufficient resources to provide their students equal educational opportunity – and have limited capacity to reshuffle those resources from poor to poorer schools within their highly segregated boundaries.
To begin with, assertions that the remaining dominant disparities in school finance are those across schools within district are based on analyses that range from merely insufficient to flawed and outright deceitful. Additionally, the argument falsely presumes that there exist large numbers of school districts around the country that have both rich and poor neighborhoods within their boundaries, and many schools sorted among them. Except in southern states operating county systems, most racial and economic segregation exists across school district boundaries, not across schools within districts. Further, in many states there exist a relative few districts which actually have large numbers of schools and even fewer where there exists large variation in poverty across those schools.
In a recent article on the limits of federal comparability regulation, Mark Weber and I explain that 21 states have less than one-half of students attending districts with 10 or more schools. Vermont has none. 15 Fifteen states have more than 1/3one-third of their students attending districts with fewer than five schools (meaning likely fewer than three at any grade level, three elementary, one middle, one secondary, or single high school regional districts).
In this same article, Mark Weber and I go further to illustrate that if we look across schools statewide, variations in district spending strongly dictate statewide variations in school spending. We explain that “District spending variation explains an important, policy relevant share of school staffing expenditures in 13 states. In many states, including Illinois and New York, a nearly 1:1 relationship exists between district spending variation and school site spending variation (2).” In other words, if a district has more money, so too do the schools within that district.
The right way to evaluate spending variation across schools
When evaluating within district spending we must take steps to parse “good variation” from “bad variation,” or more specifically “equity enhancing variation” from “equity eroding variation.” The same is true for between district spending differences. Recall that in the School Funding Fairness model which evaluates between district spending disparities, we estimate the relationship between census poverty rates and district revenues (and spending), while accounting for variation in competitive wages across regions, district enrollment size (economies of scale) and population sparsity. Failure to account for relevant factors influencing spending variation can lead to erroneous conclusions. Here are two examples of such erroneous conclusions from school level analyses:
Bad Example 1: A 2007 study by authors at the Buckeye Institute in Ohio counted up the districts where there existed a positive versus negative correlation between low income shares and per pupil spending across schools within those districts. They found that most of the 70 high poverty districts they studied did not have clear positive correlations between school spending and low income shares.  As I explained in a critique of this study, most of what they actually found was that school districts with one or a few elementary schools, a middle school, and a high school a) often had higher per pupil spending in the high school, and b) the high school often had lower shares of children reported as qualifying for free or reduced lunch. This was an important revelation to me at the time, since this is a common pattern, with a variety of explanations including lower compliance filing forms to qualify for subsidized lunch at the secondary level. But it’s not evidence that Ohio districts were shortchanging higher poverty schools to favor lower poverty ones.
Bad (really stupid) Example 2: A more egregious example comes from a New York based charter school advocacy organization called Families for Excellent Schools which released a report arguing that New York City’s highest funded middle schools were also its worst! The press release for their report proclaimed: “At the middle school level, the bottom 50 schools received an average $30,256 per pupil, compared with $16,277 at the top 50 middle schools.”The goal of their report was to advocate that these funds should instead be directed toward charter school expansion, since it was clear, by this finding, that the district simply didn’t know how to leverage resources to improve student achievement. But this “study” missed the simple fact that in New York City like most large districts, the primary driver of differences in spending across schools within districts is the share of children with disabilities served in the schools. Children with disabilities significantly influence staffing ratios and thus school level spending. It also turns out, not surprisingly, that schools with more children with disabilities tend to have lower average test scores. Thus, more spending leads to lower test scores?
So then, what’s the right approach for characterizing good and bad disparities across schools within districts? Through numerous peer reviewed publications and consulting work with colleagues including Jesse Levin at the American Institutes for Research, we have arrived at a common set of factors that should typically be included in any model of within district, school level spending variation. First we must consider the grade level issue, both because there exist differences in spending approaches across grade levels and differences in student need measures, such as free or reduced lunch. It’s not that we have any real basis for assuming that elementary school costs more than high school or vice versa, but that direct comparisons ignoring grade level are problematic and can lead to invalid conclusions (like the Buckeye report).
While we consider district size and population sparsity in our School Funding Fairness model evaluating district spending, one can make the argument that there should not exist inefficiently small (higher spending because they are small) schools in densely populated urban contexts. Having these schools for some drains resources from others. It’s inequitable variation, not equitable variation. Perhaps most importantly, we must consider the distribution of children with disabilities across schools, preferably with consideration of which schools are serving children with more severe disabilities requiring even more direct instructional and related services support personnel.
Let’s apply these guidelines to take a look at school site spending variations in New York City and Baltimore. Table 1 and Table 2 present results of regression models of school spending in New York City and New York State. It’s important to understand here that I’m just using these models to characterize the average patterns across all schools in each district. Often, statistical models like this are used for drawing inferences about relationships. These models are simply describing patterns – actual patterns, across all schools. For New York City, for example, I find that as we go from 0% to 100% children in middle grades, per pupil spending drops by $779 per pupil. As we move from 0% to 100% children in secondary grades, per pupil spending drops by $757. That is, elementary per pupil spending tends to be highest in New York City. The average regular elementary school spent about $21,229 per pupil in 2015. As we move from a school with 0% low income children to 100% low income children, spending increases by about $2,000 (about a 10% margin). If we went from a school with 0% children in special education to 100%, spending per pupil would double. Most schools fall between 0 and 30% special education, so the practical difference is about 1/3 of the $25,159. Importantly, these factors explain over 60% of the variations in spending across New York City Schools. That is, most of the variation in spending across New York City schools is rational, explainable variation. Still, a sizeable share is not, and should be vetted further.
Model of School Site Spending in New York City 2015
By contrast, Table 2 shows a model applied to statewide, inter-district spending variation in New York in 2015. Here, I also include factors for regional wage variation and for economies of scale and population sparsity. As such, this even richer model should be able to explain even more variation if that variation is rationally related to cost and need factors. But, the state level model only yields about 45% variation explained by rational factors. More disturbingly, however, the state model reveals an overall statewide pattern of regressive inter-district disparity, wherein a district with 100% poverty would be expected to have nearly $12,000 less in per pupil spending than a district with 0% poverty. So, at least in New York State, spending disparities within New York City are less of a problem than spending disparities statewide. New York City intra-district funding is mildly progressive whereas statewide inter-district funding is regressive.
Model of Statewide Current Spending per Pupil for New York State Districts in 2015
Charter Expansion and Within-District Equity
Now let’s take a look at Baltimore, where I include two different models. The New York City analysis above does not include charter schools. The Baltimore analysis does. In an equitable district/charter system, after accounting for the relevant factors, there should not be any difference in spending between charter schools and district schools. Otherwise, charter schooling in-and-of-itself is introducing inequity. Baltimore, unlike New York City does spend more in schools serving more secondary level students. Again, the margins of difference related to special education are the greatest, but are somewhat buffered where the shares of students who have mild disabilities is greater.
In the first model, it would appear that on average, schools serving more low income children have lower per pupil spending. That is, Baltimore school funding is flat to regressive. But, when one takes account of charter schools, what we see is that charter schools, on average, spend slightly more ($249 per pupil) than otherwise similar district schools and that spending with respect to low income children is slightly progressive ($183 per pupil increase moving from 0% to 100% low income). The reason this pattern flips when accounting for charter schools is that a) Baltimore charter schools serve, on average, fewer low income students than do district schools and b) Baltimore charter schools spend slightly more per pupil than district schools. That is, they introduce an inequity to the system. This finding is common.
Model of School Site Spending In Baltimore 2013-2015
In a 2015 article in the journal Education Finance and Policy, Ken Libby, Katy Wiley and I discuss similar findings regarding charter schools in New York City and Houston. Specifically, we found that New York City charter schools both served less needy student populations than nearby district schools and on average, after accounting for student population differences, those charter schools spent significantly more per pupil than district schools. Even more striking were the differences in spending within the charter sector, between schools having substantial private contributions versus those receiving far less outside of their public subsidies.  In follow up work, in an article published in 2017 in the journal Educational Policy, Mark Weber and I found that for-profit charter operators, on average divert more money from direct classroom services, leading to even greater variation across schools in jurisdictions with a mix of district schools, for-profit and non-profit charter schools. 
In ongoing work, Mark Weber, Ajay Srikanth and I are finding that across large school districts which have sizeable and growing charter sectors, student sorting by demographics is exacerbated and school spending variations increased. That is, expanded chartering seems to be leading to increased inequality across schools within common geographic spaces. Using data from two waves of the Civil Rights Data Collection, we again find that controlling for the factors listed previously, New York City charter schools continue to spend far more than district schools serving similar populations (Figure 1). Results are mixed for other settings, but inequities are inequities, in whichever direction they fall.
Focusing for the moment specifically on New York City – as I show above, New York City, across public district schools has achieved greater equity than New York State has achieved across districts. In fact, one of the most significant factors compromising equity across schools within New York City is the expansion of charter schools!
Worse, the extent to which charter expansion adversely affects equity for children within New York City is difficult to measure accurately in the absence of a common financial reporting system inclusive of all revenues and expenditures for school sites, including the value of allocated services.
Tightening comparability regulations governing within district equity (defined in terms of progressiveness) while pushing for expanded choice and diversification of operators and governing bodies are entirely incompatible policies. In some states, charter schools are governed by and financed through local district budgets, providing the opportunity for districts to use common formulas for funding district and charter schools. In other states, fully independent charter schools may be authorized to operate within district spaces but outside of their control or financing. Some states like Texas have both.
Expanding the mix of providers and provider types in a common space is more likely to result in increased variations in quality and spending than in convergence toward equity. Private providers have widely varied access to outside resources, resulting in highly unequal “revenue enhancement.” The incentive for school operators is to pursue whatever means is necessary to be the preferred school of choice (for the preferred students), not to spend only what is needed to provide equal..
In a 2015 article in the journal Education Finance and Policy, Ken Libby, Katy Wiley and I discuss similar findings regarding charter schools in New York City and Houston. Specifically, we found that New York City charter schools both served less needy student populations than nearby district schools and on average, after accounting for student population differences, those charter schools spent significantly more per pupil than district schools. Even more striking were the differences in spending within the charter sector, between schools having substantial private contributions versus those receiving far less outside of their public subsidies. [i] In follow up work, in an article published in 2017 in the journal Educational Policy, Mark Weber and I found that for-profit charter operators, on average divert more money from direct classroom services, leading to even greater variation across schools in jurisdictions with a mix of district schools, for-profit and non-profit charter schools. [ii]
In ongoing work, Mark Weber, Ajay Srikanth and I are finding that across large school districts which have sizeable and growing charter sectors, student sorting by demographics is exacerbated and school spending variations increased. That is, expanded chartering seems to be leading to increased inequality across schools within common geographic spaces. Using data from two waves of the Civil Rights Data Collection, we again find that controlling for student characteristics and grade ranges served, New York City charter schools continue to spend far more than district schools serving similar populations (Figure 1). Results are mixed for other settings, but inequities are inequities, in whichever direction they fall.
Tightening comparability regulations governing within district equity (defined in terms of progressiveness with respect to low income concentrations) while pushing for expanded choice and diversification of operators and governing bodies are entirely incompatible policies. In some states, charter schools are governed by and financed through local district budgets, providing the opportunity for districts to use common formulas for funding district and charter schools. In other states, fully independent charter schools may be authorized to operate within district spaces but outside of their control or financing. Some states like Texas have both.
Expanding the mix of providers and provider types in a common space is more likely to result in increased variations in quality and spending than in convergence toward equity. Private providers have widely varied access to outside resources, resulting in highly unequal “revenue enhancement.” The incentive for school operators is to pursue whatever means is necessary to be the preferred school of choice (for the preferred students), not to spend only what is needed to provide equal opportunity to achieve common outcomes.
Expanding choice also means accepting the presence of inefficiently small startups, at least for a period of time. Continued shifting of students from one sector to another within the same geographic space means accepting simultaneously inequities and inefficiencies associated with growth related costs in one sector, and stranded expenses in another. For a system to be equitable, policymakers must figure out how to manage these inequities. Thus far, they’ve largely ignored them.
[i] Baker, Bruce D., Ken Libby, and Kathryn Wiley. “Charter School Expansion and Within-District Equity: Confluence or Conflict?.” Education Finance and Policy (2015).
[ii] Weber, Mark, and Bruce Baker. “Do For-Profit Managers Spend Less on Schools and Instruction? A national analysis of charter school staffing expenditures.” Educational Policy (2017): 0895904816681525.
Much of the expansion of charter schooling occurred during the recession. That is, states were adding schools while reducing overall funding, adding inequitable choices on top of increasingly inequitable and inadequate systems. Expanded charter schooling was a centerpiece of the Duncan/Obama education reform platform which coincided with the recession and “new normal” era.
Cursory descriptive analyses (as well as more complex longitudinal models) suggest that states which most expanded their charter sectors are also among those states which most reduced their overall effort toward financing public education. This is a disturbing finding in part because charter schools rely similarly on public financing. Reducing public financing affects negatively both district and charter schools. Further, increasing the number of schools, holding enrollments constant, shifting students from one sector to another creates additional costs, at least in the short run.[i]
It is conceivable that state policymakers with an ideological preference for choice and the assumption that a competitive market based system can “do more with less,” apply that ideology to state tax and spending policies. Or it just may be that states where legislators prefer choice and charter schools are also states where legislators prefer not to raise taxes or spend money on schools in general, whatever type. Whatever the cause, Figure 1 shows that states like Colorado and Arizona with very high charter market share have, in 2015, the lowest effort rates for financing public education (inclusive of charter spending). Michigan, another high charter share state reduced its effort more than any other state from 2007 to 2014 (Figure 2, applying alternative measure of effort). Overall, higher charter share states have lower effort.
Focusing on four high charter market share states – Arizona, Colorado, Michigan and Ohio – we can see in Figure 3 that beginning in 2009 as charter market shares accelerated beyond 5%, state and local effort toward financing public schools dropped precipitously. All four states have charter market shares over 5%, with Colorado and Arizona over 10%. Two of the four states started as low effort states and two as higher effort states. Michigan and Arizona saw the greatest drop in effort, but effort also declined in the other two.
Whether there is causal direction between charter market share and state effort or these patterns merely exist as a function of shared ideologies of state policymakers, these patterns are problematic for both charter and district schools in these states. Equitable and adequate financing is prerequisite regardless of operator type.
[i] Bifulco, Robert, and Randall Reback. “Fiscal impacts of charter schools: lessons from New York.” Education Finance & Policy 9.1 (2014): 86-107.
As I continue work on my forthcoming book on school finance, I find myself reflecting on the state of the field. Where we stand, and how we got here, and perhaps most importantly, how we should move forward.
I personally began studying school finance around 1994 and completed my doctoral work in 1997 at Teachers College at Columbia University in New York. In 1995, while a doctoral student, and still largely unaware of the breadth of literature in my field, I received (actually, I went in someone’s place) an invitation to attend a symposium sponsored by the New York State Regents. The symposium was on the topic of Cost-Effectiveness in Education and included research papers presented by academic researchers from universities across the state. The papers were released as a report to the New York State Board of Regents in March of 1996.[i] These were state supported research studies, including research advancing the application of conceptual models and statistical methods for studying cost-effectiveness and efficiency of local public school districts. The studies were at the highest levels of empirical and conceptual rigor, and conducted by leading researchers in the field (something I totally didn’t get at the time as a cocky and naïve doctoral student). These studies were part of an ongoing research consortium among scholars from Cornell, Syracuse, SUNY Albany and NYU, supported by the Regents of the State of New York. Over time, research emanating from this group would serve to break ground in analyses of equity,[ii] efficiency,[iii] resource allocation and the use of state longitudinal data systems to study teacher labor markets.
Similar efforts on similar topics were occurring in the State of Texas, where state agencies and academic researchers were collaborating to better understand variations in labor costs, in order to inform re-calibration of their state school finance formula. In early 2000s, the Texas legislature established the Texas School Finance Project, in which I was involved with researchers from Texas A&M University.[iv] State supported efforts in Texas, like New York served to significantly advance our knowledge of education costs, cost analysis, cost variation and efficiency through the production of numerous prominent and frequently cited reports.[v]
Research emanating from these states also found its way into national symposia sponsored by the U.S. Department of Education and released in two different recurring report series – Developments in School Finance and Selected Papers in School Finance. These reports have long since been discontinued, occurring mainly between 1995 and 2005. These reports like the state efforts in New York and Texas, tackled with empirical rigor, issues including the development of national indices to capture variation in teacher wages from region to region, labor market to labor market and district to district, [vi] the application of statistical modeling techniques to estimate costs of achieving common outcome goals,[vii] and statistical tests of the reliability and validity of estimates of school performance and efficiency.[viii]
These were the very types of analyses needed to inform state school finance polices and to advance the art and science of evaluating educational reforms for their potential to improve equity, productivity and efficiency. But these efforts largely disappeared over the next decade. More disconcerting, these efforts were replaced by far less rigorous, often purely speculative policy papers, free of any substantive empirical analysis and devoid of any conceptual frameworks.
This shift was largely brought about under the leadership of Arne Duncan. Kevin Welner of the University of Colorado and I explained first in a report for the National Education Policy Center and subsequently in shorter form in the journal Educational Researcher, that Secretary Duncan had begun to give lip service to improving educational productivity and efficiency, but accompanied that lip service with wholly insufficient resources. Kevin Welner and I explained that:
“the materials provided on the Department’s website as guiding resources present poorly supported policy advisement. The materials listed and recommendations expressed within those materials repeatedly fail to provide substantive analyses of the cost effectiveness or efficiency of public schools, of practices within public schools, of broader policies pertaining to public schools, or of resource allocation strategies.” [ix]
Among other issues, the materials provided on the web site failed to acknowledge even the existence of the relevant conceptual frameworks and rigorous empirical methods which had risen to prominence in state supported and federally documented research in the years prior.
Not surprisingly, a similar shift occurred in the states. In 2011, John King, New York Education Commissioner, close ally of and eventual replacement for Arne Duncan took a “different” approach to the annual Regents Symposium. Prominent researchers were invited to sit in the audience and be subjected to presentations by the authors of many of the materials from Duncan’s productivity web site, including but not limited to, the baseless graph I have presented in several previous posts. Here it is again (as much as it pains me):
Researchers in attendance that day forwarded to me their critique of that graph:
Maguerite Roza made claims that the service delivery programs she discussed could increase the productivity of educational spending several fold and illustrated this point with a graph. It is hard to know where to start in responding to this claim. First, she did not explain how the productivity of current spending was measured, so it is difficult to assess the claims she made about that. However, we are familiar with the literature on educational productivity and do not believe the productivity of current service delivery models can be estimated as precisely as she claimed, and suspect that the basis for the figures presented is questionable. Concerning the productivity of the innovations she was advocating there are several problems. It is not at all clear what the innovations are that she is claiming would create such large productivity improvements. Also, it is not clear what research those productivity estimates are based on. During the Q&A session, she indicated that these innovations were less than a year old. How can the productivity gain produced by service models used in a very small number sites for a very short time be determined? They can’t. It is not an overstatement to say that the claims about productivity improvement were simply made up. [emphasis added]
Below is a second, equally problematic graph that was presented in that same symposium, also later used by John King in presentations to school administrators across New York. Here, the presenter (using a graph from an organization called Educational Resource Strategies) argues that only a very small share of teacher salaries actually goes into “responsibility for results.” Thus, all of the salary differences above base salaries – and by extension much of school funding in total – is squandered inefficiently and can and should be reallocated, presumably toward “responsibility for results,” whatever that may mean or however that should be measured.
Researchers in attendance that day forwarded to me this critique:
Dr. Fisher [the presenter of this graph] made the claim that 30 to 40 percent of school district dollars were spent on resources that had no relation to student achievement. Again, the basis for this claim was not presented, so it is difficult to assess. However, the little explanation that was presented suggests that the analysis suffered from serious conceptual errors. Arguments can be made for a flatter teaching salary schedule, however, those arguments are more complicated than the speaker acknowledged. Particularly, the suggestion that any spending on teacher salaries above the starting salary is unproductive is, well, wrong.
It is likely that most of the people in the audience did not take these claims very seriously. Nonetheless, it was disappointing to see such claims being made by researchers in a forum like this one.
I raise these issues because:
It is vital that we return to the application of relevant frameworks and rigorous methods for studying productivity and efficiency; and
It is vital that the U.S. Department of Education and State Education Agencies play a role in supporting this research and enabling the highest quality research and data to inform policy – specifically, state school finance policy and the federal role.
To reiterate a take home point of many previous posts, equitable and adequate financing are prerequisite conditions for our education systems, regardless of how we choose to deliver those systems. System delivery may alter what’s equitable or adequate. But without rigorous and relevant analyses, we can never know how or to what extent.
[i] Berne, Robert. Study on Cost-effectiveness in Education. University of the State of New York, State Education Department, 1996.
[ii] University of the State of New York. Board of Regents. Supporting Cost-effective School Reform: New York State Board of Regents 1996-97 Detailed Proposal On School Aid. Albany, N.Y.: University of the State of New York, State Education Dept., 1996.
[iii] Duncombe, William, and Jerry Miner. “Productive Efficiency and Cost-Effectiveness: Different Approaches to Measuring School Performance.” Study on Cost-Effectiveness in Education: Final Report, ed. R. Berne (Albany: State Education Department, New York State Board of Regents, 1996) (1996): 141-156.
[v] Alexander, Celeste D., et al. “A study of uncontrollable variations in the costs of Texas public education.” A summary report prepared for the 77th Texas Legislature, Austin: Charles A. Dana Center, University of Texas-Austin. Available at http://www. utdanacenter. org/research/reports/ceireport. pdf.(Last accessed on 3/4/04.) (2000).
Taylor, Lori L., et al. “Updating the Texas cost of education index.” Journal of Education Finance 28.2 (2002): 261-284.
Taylor, Lori L., and Harrison Keller. “Competing perspectives on the cost of education.” Developments in School Finance 2002–2002 (2003): 111-26.
Baker, Bruce D., Lori Taylor, and Arnold Vedlitz. “Measuring educational adequacy in public schools (Report prepared for the Texas Legislature Joint Committee on Public School Finance, The Texas School Finance Project).” Retrieved August 17 (2004): 2006.
[vi] Chambers, Jay G. “Public school teacher cost differences across the United States: Introduction to a teacher cost index (TCI).” Developments in school finance (1995): 19-32.
[vii] Reschovsky, Andrew, and Jennifer Imazeki. “The development of school finance formulas to guarantee the provision of adequate education to low-income students.” Developments in school finance 124 (1997): 121-48.
[viii] Bifulco, Robert, and William Duncombe. “Evaluating School Performance: Are we ready for prime time?.” Developments in School Finance, 1999–2000 (2002): 9.
Rubenstein, Ross, et al. “Distinguishing good schools from bad in principle and practice: A comparison of four methods.” Developments in School Finance: 2003 (2004): 53.
Stiefel, Leanna, Hella Bel Hadj Amor, and Amy Ellen Schwartz. “Best schools, worst schools, and school efficiency: A reconciliation and assessment of alternative classification systems.” Developments in School Finance: 2004 81 (2005).
[ix] Baker, Bruce, and Kevin G. Welner. “Evidence and rigor: Scrutinizing the rhetorical embrace of evidence-based decision making.” Educational Researcher 41.3 (2012): 98-101.
A common refrain among school choice advocates is that expansion of choice through vouchers and charter schooling is the “civil rights issue of our time.”[i] That introduction of competition through choice is a tide which raises all boats! These claims infer a connection between expanding the “liberty” associated with choice and improving educational equality, educational adequacy and equal educational opportunity across all children and groups. But, these assertions inappropriately conflate the “liberty” associated with choice, with equality.
A lengthy literature in political theory explains that liberty and equality are preferences which most often operate in tension with one another.[ii] While not mutually exclusive, they are certainly not one-and-the-same. Preferences for and expansion of liberties most often leads to greater inequality and division among members of society, whereas preferences for equality moderate those divisions.[iii] The only way expanded liberty can lead to greater equality is if available choices are substantively equal – conforming to a common set of societal standards. But if available choices are substantively equal, then why choose one over another? Systems of choice and competition rely on differentiation, inequality, winners and losers.
Applied to the real world context of America’s highly racially and socio-economically segregated system of public schooling, choice advocates assert that the liberty of school choice necessarily disrupts the inequitable relationship between ones’ zip code of residence and the quality of schooling available. Providing choices across jurisdictional boundaries can also disrupt the capitalization relationship which drives growing inequality. That is, if housing isn’t linked to local schools, then housing prices are less likely to respond to differences in local school quality.[iv]
Choice advocates are divided on whether expanded school choice should include vouchers for private schools or merely subsidies for private operators of government sanctioned charter schools, the line between the two being more blurred in legal terms than typically acknowledged.[v] Regardless, however, in most cases, choice-based systems of schooling remain highly limited by geography and often restricted to the same geographic boundaries which define local public school districts and municipalities. For example, in many states, charter school choice is functionally limited to choice between district schools and charter schools within the district boundaries. That is, charter school choice, and voucher systems in cities like Milwaukee merely permit the reshuffling of urban minority children among a limited set of alternatives. Broader geographic expansion faces significant political and operational hurdles (e.g. “perfect mobility”).
Further, as displayed in my own work on charter school expansion in New York City, Texas, [vi] and nationally,[vii] expansion of charter schooling has largely led to expansion of vastly unequal choices. Some charter schools, operated by politically connected and financially well-endowed management companies are able to provide longer school years, longer days, smaller classes and richer curricula than others.[viii] Those same charter schools are the ones most chosen, with the longest waiting lists. That is, the choices are unequal and unequally accessible. A system of unequal choices is still an unequal system. As for “adequacy,” a system where the most available choices are the least adequate is not adequate. We must be willing as a society to deal openly with our preferences for liberty versus equality and design systems which best balance these often competing preferences.
[ii] De Tocqueville, A. (1835). Democracy in america (Vol. 2). Saunders and Otley.
[iii] Levin, H. (2001). Privatizing Education: Can The School Marketplace Deliver Freedom Of Choice, Efficiency, Equity, And Social Cohesion?. Westview Press. See also: Matear, A. (2007). Equity in education in Chile: The tensions between policy and practice. International Journal of Educational Development, 27(1), 101-113.
[iv] Brunner, E., Sonstelie, J., & Thayer, M. (2001). Capitalization and the voucher: an analysis of precinct returns from California’s Proposition 174. Journal of Urban Economics, 50(3), 517-536.
[v] Green, P. C., Baker, B. D., & Oluwole, J. (2014). Having it both ways: How charter schools try to obtain funding of public schools and the autonomy of private schools.
Green, P. C., Baker, B. D., & Oluwole, J. (2015). The legal status of charter schools in state statutory law.
[vi] Baker, B. D., Libby, K., & Wiley, K. (2015). Charter School Expansion and Within-District Equity: Confluence or Conflict?. Education Finance and Policy.
[vii] Baker, B. (2016). Exploring the consequences of charter school expansion in US cities. Economic Policy Institute, November, 30.
[viii] Baker, B. D., Libby, K., & Wiley, K. (2015). Charter School Expansion and Within-District Equity: Confluence or Conflict?. Education Finance and Policy.
The next several posts will include what I consider to be prerequisite material for understanding state school finance systems and public education systems more broadly.
Education as a Taxpayer Financed (Quasi) Public Good
Let’s step back for a moment and consider broadly the provision of public goods and services through a system of taxpayer support. In the United States, for elementary and secondary education in particular, that system combines federal (about 10%), state and local (varied by state) tax dollars to pay for the provision of public education systems.
Public education systems are largely state governed. Local tax dollars are generally raised from property taxes on residential, commercial and industrial properties within geographic spaces defined in state law as local public school districts. In some states, these “districts” are contiguous with other municipal (city/town) boundaries, while in others they are not. Generally, the rules for and parameters determining local taxation are governed by states. State dollars typically come from state general funds fueled by income and sales taxes and are allocated to local districts through various aid formulas, governed by state legislatures. Similarly, federal aid is allocated to states by various formulas governed by Congress, from revenue derived primarily from federal income taxes.
Governments (at all levels), established by the people for the people, collect and redistribute tax dollars to provide for the mix of public goods and services desired. Investment in public schooling is investment in “human capital,” and the collective returns to that investment are greater than the sum of the returns reaped by each individual who furthers her education.[i] We invest public resources into the education of the public, for the benefit of the public.
The dollars provided via taxation support both a) infrastructure and b) annual operations for public goods & services. This includes schools, roads, public safety (police/fire), national security, public utilities, parks, etc. Investment in public infrastructure meant to serve not only immediate users, but users for generations to come. Support of annual operations is also not exclusively to the benefit of those using the service or good today or this year. Contributors of tax dollars include those directly and indirectly benefiting (both parents of school-aged children, property owners without school aged children). Indirect benefits accrued from investment in system of public schooling include capitalization in housing values (e.g. “better” schools increase property values).
The necessity to revisit the basic connections between taxation and the provision of public goods[ii] comes about partly in response to a frequent argument of school choice (voucher and charter) advocates that the public tax dollars belong (or at least should belong) to the child, not the institutions (schools). Institutions – especially government institutions – are faceless bureaucracies, thus “bad” whereas children are obviously “good.” That is, even if those institutions are established to serve the children. While this claim makes a compelling soundbite, it falsely assumes an oversimplified linear tax collection to spending distribution path and individualistic benefit basis for public goods and services. That taxpayer-parents (presumed one and the same?) pool their money such that the money can be distributed not based on any collective preferences but instead according to each individual’s preferences for their own child, and with no other contributor to the pool having influence over individual choices. That is, individual parent preferences for the use of public dollars always supersede societal preferences. This ideology runs contrary to the basic concept of public goods and services.
The “money belongs to the child” claim also falsely assumes that the only expenses associated with each individual’s education choice are the current annual expenses of “educating” that individual. Further, that the expenses associated with educating (equitably) the first, second and third child are the same as the ninety-ninth child choosing any one institution. That is, it ignores entirely marginal costs and economies of scale, foundational elements of origins of public institutions. We collect tax dollars and provide public goods and services because it allows us to do so at an efficient scale of operations.
Rather, the tax dollars collected belong to (are governed or controlled by) the democratically governed community (local, state, national/Federal) that established the policies for collecting those tax dollars, which are to be distributed according to the demands – preferred goods and services – of that community within the constraints of the law. Public spending matters not only to those using it here and now. Those dollars don’t just belong to parents of children presently attending the schools. The assets acquired with public funding, often with long-term debt (15 to 20 years), surely do not belong exclusively to parents of currently enrolled children. This is not to suggest that this is the perfect or even best possible system, but rather that it is the system we have in place – one that provides for democratic control and taxpayer financing of public schooling – governed and regulated by the appropriate local, state and federal authorities and laws.[iii]
[i] Sweetland, S. R. (1996). Human capital theory: Foundations of a field of inquiry. Review of educational research, 66(3), 341-359.
[ii] Education, or public schooling (public school systems) in particular is not typically considered a “public good” as the provision of public schooling does not comply with the definition of a “pure public good” which can be equally accessed by all, without reduction in benefits to any. The intent here (in the above tweet-storm) was to shed some light on the importance of understanding the role/position of these publicly financed education systems in society and that there’s more to these systems than the year to year provision of “schooling” to those who happen to be school aged in a specific community at a specific point in time.
[iii] Another recent policy proposal which ignores these realities is the “parent trigger,” a policy which allows the parents of children presently attending a particular public school to petition (by simple majority) to have that school taken over by a private management company, including potential transfer of capital assets to the private manager.[iii] Clearly, 51% of parents of currently enrolled students should not be granted such authority over an institution financed by a much larger taxpaying public.
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