School Funding Equity Smokescreens: A note to the Equity Commission

In this blog post, I summarize a number of issues I’ve addressed in the past. In my previous post, I discussed general reformy myths about school spending. In this post, I address smokescreens commonly occurring in DC beltway rhetoric about school funding equity and adequacy. School funding is largely a state and local issue, where even that “local” component is governed under state policies. So I guess that makes it a state issue, really. Occasionally, the federal government will dabble in the debate over how or whether to intervene more extensively in state and local public school finance. Now is one of those times where the federal government is again at least paying lip services to the question of equity – with some implication that they may even be talking about school funding equity. The federal government has created an equity commission!

One of my fears is that this current discussion of funding equity will be typical of recent beltway discussions of school funding, and be trapped in the constant fog of School Funding Smokescreens and insulated entirely from more legitimate representations and analyses of the critical issues that should be addressed.

So, for you – the equity commission – here’s a quick run down on School Funding Smokescreens:

1. On average, nationally, we now put more funding into higher poverty school districts than lower poverty ones (to no avail)

This argument seems to be popping up more and more of late, and often with the table below attached. This table is from the National Center for Education Statistics and shows the average current operating expenditure per pupil of school districts nationally over time. The table would appear to show that in 1994-95, low poverty school districts had between $300 and $400 less in per pupil spending than higher poverty ones. By 2006-07, the highest poverty quintile of school districts had about $100 per pupil more than the lowest poverty quintile. That’s it. We’re done. Equity problems fixed. No more savage inequalities. And after all of this fixing of school finance equity, we really got nothing for it. Achievement gaps are still unacceptably large and NAEP scores stagnant? Right? All of this after dumping a whole extra $100 per pupil into high poverty districts. I guess we should be rethinking this crazy strategy of systematically pouring so much into high poverty districts.

Table 1

NCES Oversimplification of Funding Differences by Poverty

Well, to begin with, a $100 difference really wouldn’t be that much anyway, given that the costs of actually meeting the needs of children from economically disadvantaged backgrounds are much greater than this. Setting that (really important) question aside, this table provides a less than compelling argument that we as a nation have accomplished improved funding equity for kids in high poverty districts.

Here’s the underlying scatter of school districts that lead to the neatly packed aggregations above. In the graph below, districts are plotted by current expenditures per pupil with respect to census poverty rates, using 2007-08 data. Clearly there is substantial variation in current spending. In fact, the underlying relationship isn’t even a relationship at all. It’s all over the place. And yes, if you fit a trend line – if you take out a huge magnifying glass – you can see that the trendline is ever so slightly higher in the higher poverty schools than in the lower poverty ones (perhaps about $100?). It’s not systematic. It’s not statistically significant. It’s pretty darn meaningless.

Figure 1

Pattern of school districts underlying Table 1

In our recent report Is school funding fair? we conducted a far more rigorous analysis of state and local revenue per pupil with respect to poverty, for each state. What we showed was that there exists huge variation across states both in the overall level of resources available to local public school districts and in the differences in state and local revenue in higher and lower poverty districts. In that report, we showed that 9 states have statistically significantly lower state and local revenue per pupil in higher poverty districts (after controlling for economies of scale and competitive wage variation). Overall, half of states had lower funding per pupil in higher poverty districts (with many of those approximately the same).

Among the worst states were New York, Illinois and Pennsylvania. Let’s pull Illinois forward in Figure 1 – and also look at state and local revenues (excluding federal support, to focus on state policies) in place of current expenditures.

Figure 2

State and local revenues with respect to poverty, with Illinois highlighted

Now, when we exclude federal revenues the overall line tips slightly downward. The federal effect is slight, but there. More strikingly, when we pull Illinois forward in the picture, we see that funding by poverty across Illinois districts is highly regressive, and is systematic and statistically significant. Funding inequities across Illinois districts are far from being resolved. AND ILLINOIS IS NOT ALONE. I could go on and on with this.

2. The remaining (Because of #1), most egregious disparities in funding and teacher quality occur across schools within districts (because of politically motivated and corrupt local administrators) and these disparities are what cause the persistent racial achievement gaps (the reason those gaps haven’t improved since we’ve fixed between district inequity)

To many, this argument seems absurd (and is) on its face. Who really says that? Does anyone? Am I just makin’ this stuff up? No. And in fact, because this argument has become so pervasive of late, I even had to take the time to write a fairly extensive research article on the topic. See: http://epaa.asu.edu/ojs/article/view/718

I have written about this topic on my blog on several occasions and much of my writing on this topic can be found by reading my critiques of reports from the Center for American Progress and from the Education Trust. Here are some choice quotes where CAP and Ed Trust frame this argument – or blow this smoke!

Center for American Progress

State funding formulas tend to exert an equalizing effect on per pupil revenues between districts, on average, and not by accident. These formulas were sculpted by two generations of litigation and legislation seeking equitable or adequate funding for property-poor school districts.

Scandalous inequity in the distribution of resources within school districts has plagued U.S. education for more than a hundred years.

empirical literature documenting the extent of within-district inequity is astonishingly thin. [my reply: well, not if you actually read the research on the topic]

Center for American Progress

The outcome of such practices is predictable: A further widening of the dangerous achievement gap that has become endemic in American schools today.

Education Trust

Many states have made progress in closing the funding gaps between affluent school districts and those serving the highest concentrations of low-income children. But a hidden funding gap between high-poverty and low-poverty schools persists between schools within the same district.

These gaps occur partly because teachers in wealthier schools tend to earn more than their peers in high-poverty schools and because of pressure to “equalize” other resources across schools.

All of these claims that within district inequities are the major source of persistent inequity and that our failure to close within district funding and teacher quality gaps (having already fixed between district ones) are the reason for persistent black-white and poor-non-poor achievement gaps might be reasonable if poor children and non-poor children and black children and white children actually lived in the same school districts. BUT, IN GENERAL,* THEY DO NOT! As a result this argument is patently absurd, ridiculous, irresponsible and ignorant. It’s one massive distraction. A smokescreen of monumental proportion!

Here’s a quick visual of the reality that any informed analyst (or anyone who simply lives in the real world) understands. Below are two maps of the Chicago metropolitan area. On the left is a map which shows school districts and the level of state and local revenue per pupil in each of those districts. We know from above that Illinois maintains a very regressive state school finance formula. That is, higher poverty districts have less funding than lower poverty ones. Note that the diagonal shading indicates the location of districts that have majority minority (black and Hispanic) enrollment. As it turns out, most of those districts are in the orange – lower funding levels.

Now, CAP and Ed Trust would have you believe otherwise to begin with (that poor minority districts already have enough money), but would then go further to say that the real problem is that these Illinois districts are putting money into their white, rich schools at the expense of their poor black and Hispanic ones. How is that even possible?

Okay, so let’s look at the right hand panel, in which I have indicated the locations of individual schools, with majority black schools in red and majority Hispanic schools in purple. Majority white schools are in white. NOTE THAT THE WHITE DOTS TEND TO BE DISTRICTS ENTIRELY SEPARATE FROM THE PURPLE OR RED ONES. AND ONLY CHICAGO PUBLIC SCHOOLS HAS MUCH OF A MIX OF PURPLE AND RED. Only a handful of districts have both white and majority minority schools. Also, only a handful of districts have both low(er) poverty and high poverty schools. Districts are highly segregated.

Figure 3

State and Local Revenue and the Location of Majority Minority Schools in Illinois

FEW IF ANY SCHOOL DISTRICTS IN THIS MAP HAVE THE OPPORTUNITY TO REDISTRIBUTE RESOURCES ACROSS THEIR “RICH” AND “POOR” OR “BLACK” AND “WHITE” SCHOOLS – BECAUSE THEY DON’T HAVE BOTH!!!!! Yes, Chicago Public Schools and a few other districts can re-allocate between poor black and poor Hispanic schools. But such re-allocation accomplishes little toward improving educational equity in the Chicago metro area or State of Illinois.

Now… to those at Ed Trust and CAP – if you really don’t mean this, I dare you to actually say it. Say that between district differences in demographics and funding are THE BIG ISSUE. At least as big if not much bigger than within district differences. Say it. Acknowledge it. I challenge you. Release another hastily crafted report and press release – but this time – having conclusions that are at least reasonably grounded in reality. The data are unambiguous in this regard. Yes, within district disparities exist and it is important to address them. I will certainly admit that, and I’ve never said otherwise. But solving within district resource variation alone will accomplish very little.

*Clarification – In states with county-wide districts, and large diverse populations, like Florida, one is more likely to see between school within district segregation to be a greater problem.

3. High need, poor urban districts (in addition to misallocating all of their resources to the schools serving rich white kids in their district???) are simply wasting massive sums of money on things like cheer leading and ceramics.

This is another absurd and empirically unfounded argument. Again, you ask, is anyone really saying that high need, low performing school districts are actually wasting money on cheerleading and ceramics that could easily be translated into sufficient resources for improving reading and math performance (can we really fire the cheer leading coach and hire 6 more math specialists)? Surely no-one is advancing an argument – SMOKESCREEN – that utterly absurd. But again, these quotes can be found all over the Beltway talk-circuit regarding the best fixes for school funding inequities and inefficiencies (and nifty was to stretch that school dollar).

Here’s the advertisement headline from a recent beltway discussion at the Urban Institute:

Urban Institute Event Headline (based on content from Marguerite Roza)

Imagine a high school that spends $328 per student for math courses and $1,348 per cheerleader for cheerleading activities. Or a school where the average per-student cost of offering ceramics was $1,608; cosmetology, $1,997; and such core subjects as science, $739.

I’ve only recently begun exploring more deeply the resource differences across school districts that fall into different performance and efficiency categories. I’ve been specifically looking at Illinois and Missouri school districts, and estimating statistical models to determine which districts are:

a) resource constrained and low performing (low-low)

b) resource constrained and high performing (low-high)

c) resource rich and high performing (high-high)

d) resource rich and low performing (high-low)

These categories are based on thoroughly cost adjusted analysis. As such, a district identified as having low or constrained “resources” may actually spend more per pupil in nominal dollars than a district identified as having high resource levels. The resource levels are adjusted for various cost pressures including differences in student needs. I should be posting the forthcoming paper on my research page some time in the next month. But here’s a preview.

In both states, most districts fall into categories a) and c), where you would expect. There’s somewhat more “scatter” in Missouri, either because Missouri has some better funded high need districts (less regressive than Illinois) or because my statistical model just isn’t working quite right. I picked these neighboring states because Missouri is less regressive than Illinois and because I had similar data on both. So, the big question here is – if I compare the dominant categories of resource constrained low performing schools to resource rich high performing ones what do we actually see in the organization of their staffing and course delivery?

In Missouri, I tabulate each individual course to which teachers are assigned. In Illinois my tabulation is by the main assignment of each teacher. To begin with, in both states, the high spending high performing schools have more course offerings per pupil and more teachers per pupil (and smaller class sizes). These differences are far greater under the more regressive Illinois policies.

Here are a few fun visuals of what I’m finding so far, expressed in “shares of staff” allocation and relating staffing allocations in low-low districts to those of high-high districts.

The first two graphs compare the main assignments of teachers in high resource high performing Illinois schools (high school assignments only) to those in low resource low performing ones. The diagonal line represents “comparable” allocation to high resource high performing schools. Assignments falling below the line represent “deficits” (relative) in low resource low performing schools.

Across all assignment areas, Figure 4 shows that kids in low resource low performing schools tend to have reduced access to physical education, biology, chemistry and foreign language. Sadly, no indicator for ceramics in these data.

Figure 4

Allocation of Main Teaching Assignments in Illinois Districts

Focusing on less frequent assignment areas – lower budget share & staff allocation areas – Figure 5 shows that in Illinois, kids in low resource low performing schools tend to have reduced access to advanced math and science courses and drivers education, but have greater access to basic courses. That is, these districts are already channeling their resources to the basic, at the detriment of potentially important advanced coursework in math and science, and even basic coursework in biology and chemistry.

Figure 5

Allocation of Main Teaching Assignments in Illinois Districts (less frequent assignments)

Missouri – despite having somewhat higher relative resource levels in higher poverty settings (than Illinois, but still regressive), shows very similar patterns. Figure 6 shows reduced access to physical education for kids in low resource low outcome schools and elevated access to “general” math and language arts courses.

Figure 6

Allocation of Assigned Courses in Missouri Districts

Kids in low resource low outcome schools have reduced access to advanced math courses including calculus and trigonometry, and reduced access to chemistry. They have higher shares of teachers in special education, basic life skills, earth and physical (basic/introductory) science and in JROTC. Again, significant reallocation to “basics” is already occurring and within significant resource constraints.

Figure 7

Allocation of Assigned Courses in Missouri Districts (less frequent courses)

Also, LET IT BE KNOWN THAT HIGH SPENDING HIGH PERFORMING SCHOOLS IN MISSOURI HAVE TWICE AS MANY CERAMICS COURSE OFFERINGS PER PUPIL AS LOW SPENDIGN LOW PERFORMING MISSOURI SCHOOLS!!!!!

4. None of this school funding equity – between district stuff – matters anyway!

Rigorous peer reviewed studies do show that state school finance reforms matter. Shifting the level of funding can improve the quality of teacher workforce and ultimately the level of student outcomes and shifting the distribution of resources can shift the distribution of outcomes.

We conclude that there is arbitrariness in how research in this area appears to have shaped the perceptions and discourse of policymakers and the public. Methodological complexities and design problems plague finance impact studies. Advocacy research that has received considerable attention in the press and elsewhere has taken shortcuts toward desired conclusions, and this is troubling. As demonstrated by our own second look at the states discussed in Hanushek and Lindseth’s book, the methods used for such relatively superficial analyses are easily manipulable and do not necessarily lead to the book’s conclusions. Higher quality research, in contrast, shows that states that implemented significant reforms to the level and/or distribution of funding tend to have significant gains in student outcomes. Moreover, we stress the importance of the specific nature of any given reform: positive outcomes are likely to arise only if the reform is both significant and sustained. Court orders alone do not ensure improved outcomes, nor do short-term responses.

Dice Rolling Activity for New Jersey Teachers

Yesterday, New Jersey’s Education Commissioner announced his plans for how teachers should be evaluated, what teachers should have to do to achieve tenure, and on what basis a teacher could be relieved of tenure. In short, Commissioner Cerf borrowed from the Colorado teacher tenure and evaluation plan which includes a few key elements (Colorado version outlined at end of post):

1. Evaluations based 50% on teacher effectiveness ratings generated with student assessment data – or value-added modeling (though not stated in those specific terms)

2. Teachers must receive 3 positive evaluations in a row in order to achieve tenure.

3. Teachers can lose tenure status or be placed at risk of losing tenure status if they receive 2 negative evaluations in a row.

This post is intended to illustrate just how ill-conceived – how poorly thought out – the above parameters are. This all seems logical on its face, to anyone who knows little or nothing about the fallibility of measuring teacher effectiveness or probability and statistics more generally. Of course we only want to tenure “good” teachers and we want a simple mechanism to get rid of bad ones. If it was only that easy to set up simple parameters of goodness and badness and put such a system into place. Well, it’s not.

Here’s an activity for teachers to try today. It may take more than a day to get it done.

MATERIALS: DICE (well, really just one Die)! That’s all you need!

STEP 1: Roll Die. Record result. Roll again. Record result. Keep rolling until you get the same number 3 times in a row. STOP. Write down the total number of rolls.

STEP 2: Roll Die. Record result. Roll again. Record result. Keep rolling until you get the same number 2 times in a row. STOP. Write down the total number of rolls.

Post your results in the comments section below.

Now, what the heck does this all mean? Well, as I’ve written on multiple occasions, the year to year instability of teacher ratings based on student assessment scores is huge. Alternatively stated, the relationship between a teacher’s rating in one year and the next is pretty weak. The likelihood of getting the same rating two straight years is pretty low, and three straight years is very low. The year to year correlation, whether we are talking about the recent Gates/Kane studies or previous work, is about .2 to .3. There’s about a 35% chance that an average teacher in any year is misidentified as poor, given one year of data, and 25% chance given two years of data. That’s very high error rate and very low year to year relationship. This is noise. Error. Teachers – this is not something over which you have control! Teachers have little control over whether they can get 3 good years in a row. AND IN THIS CASE, I’M TALKING ONLY ABOUT THE NOISE IN THE DATA, NOT THE BIAS RESULTING FROM WHICH STUDENTS YOU HAVE!

What does this mean for teachers being tenured and de-tenured under the above parameters? Given the random error, instability alone, it could take quite a long time, a damn long time for any teacher to actually string together 3 good years of value added ratings. And even if one does, we can’t be that confident that he/she is really a good teacher. The dice rolling activity above may actually provide a reasonable estimate of how long it would take a teacher to get tenure (depending on how high or low teacher ratings have to be to achieve or lose tenure). In that case, you’ve got a 1/6 chance with each roll that you get the same number you got on the previous. Of course, getting the same number as your first roll two more times is a much lower probability than getting that number only one more time. You can play it more conservatively by just seeing how long it takes to get 3 rolls in a row where you get a 4, 5 or 6 (above average rating), and then how long it takes to get only two in a row of a 1, 2, or 3.

What does that mean? That means that it could take a damn long time to string together the ratings to get tenure, and not very long to be on the chopping block for losing it. Try the activity. Report your results below.

Each roll above is one year of experience. How many rolls did it take you to get tenure? And how long to lose it?

Now, I’ve actually given you a break here, because I’ve assumed that when you got the first of three in a row, that the number you got was equivalent to a “good” teacher rating. It might have been a bad, or just average rating. So, when you got three in a row, those three in a row might get you fired instead of tenured. So, let’s assume a 5 or a 6 represent a good rating. Try the exercise again and see how long it takes to get three 5s or three 6s in a row. (or increase your odds of either success or failure by lumping together any 5 or 6 as successful and any 1 or 2 as unsuccessful, or counting any roll of 1-3 as unsuccessful and any roll of 4 -6 as successful)

Of course, this change has to work both ways too. See how long it takes to get two 1s or two 2s in a row, assuming those represent bad ratings.

Now, defenders of this approach will likely argue that they are putting only 50% of the weight of evaluations on these measures. The rest will include a mix of other objective and subjective measures. The reality of an evaluation that includes a single large, or even significant weight, placed on a single quantified factor is that that specific factor necessarily becomes the tipping point, or trigger mechanism. It may be 50% of the evaluation weight, but it becomes 100% of the decision, because it’s a fixed, clearly defined (though poorly estimated) metric.

In short, based on the instability of measures alone, the average time to tenure will be quite long, and highly unpredictable. And, those who actually get tenure may not be much more effective, or any more, than those who don’t. It’s a crap shoot. Literally!

Then, losing tenure will be pretty easy… also on a crap shoot… but your odds of losing are much greater than your odds were of winning.

And who’s going to be lining up for these jobs?

Summary of research on “intertemporal instability” and “error rates”

The assumption in value-added modeling for estimating teacher “effectiveness” is that if one uses data on enough students passing through a given teacher each year, one can generate a stable estimate of the contribution of that teacher to those children’s achievement gains.[1] However, this assumption is problematic because of the concept of inter-temporal instability: that is, the same teacher is highly likely to get a very different value-added rating from one year to the next. Tim Sass notes that the year-to-year correlation for a teacher’s value-added rating is only about 0.2 or 0.3 – at best a very modest correlation. Sass also notes that:

About one quarter to one third of the teachers in the bottom and top quintiles stay in the same quintile from one year to the next while roughly 10 to 15 percent of teachers move all the way from the bottom quintile to the top and an equal proportion fall from the top quintile to the lowest quintile in the next year.[2]

Further, most of the change or difference in the teacher’s value-added rating from one year to the next is unexplainable – not by differences in observed student characteristics, peer characteristics or school characteristics.[3]

Similarly, preliminary analyses from the Measures of Effective Teaching Project, funded by the Bill and Melinda Gates Foundation found:

When the between-section or between-year correlation in teacher value-added is below .5, the implication is that more than half of the observed variation is due to transitory effects rather than stable differences between teachers. That is the case for all of the measures of value-added we calculated.[4]

While some statistical corrections and multi-year analysis might help, it is hard to guarantee or even be reasonably sure that a teacher would not be dismissed simply as a function of unexplainable low performance for two or three years in a row.

Classification & Model Prediction Error

Another technical problem of VAM teacher evaluation systems is classification and/or model prediction error. Researchers at Mathematica Policy Research Institute in a study funded by the U.S. Department of Education carried out a series of statistical tests and reviews of existing studies to determine the identification “error” rates for ineffective teachers when using typical value-added modeling methods.[5] The report found:

Type I and II error rates for comparing a teacher’s performance to the average are likely to be about 25 percent with three years of data and 35 percent with one year of data. Corresponding error rates for overall false positive and negative errors are 10 and 20 percent, respectively.[6]

Type I error refers to the probability that based on a certain number of years of data, the model will find that a truly average teacher performed significantly worse than average.[7] So, that means that there is about a 25% chance, if using three years of data or 35% chance if using one year of data that a teacher who is “average” would be identified as “significantly worse than average” and potentially be fired. Of particular concern is the likelihood that a “good teacher” is falsely identified as a “bad” teacher, in this case a “false positive” identification. According to the study, this occurs one in ten times (given three years of data) and two in ten (given only one year of data).

Same Teachers, Different Tests, Different Results

Determining whether a teacher is effective may vary depending on the assessment used for a specific subject area and not whether that teacher is a generally effective teacher in that subject area. For example, Houston uses two standardized test each year to measure student achievement: the state Texas Assessment of Knowledge and Skills (TAKS) and the nationally-normed Stanford Achievement Test.[8] Corcoran and colleagues used Houston Independent School District (HISD) data from each test to calculate separate value-added measures for fourth and fifth grade teachers.[9] The authors found that a teacher’s value-added can vary considerably depending on which test is used.[10] Specifically:

among those who ranked in the top category (5) on the TAKS reading test, more than 17 percent ranked among the lowest two categories on the Stanford test. Similarly, more than 15 percent of the lowest value-added teachers on the TAKS were in the highest two categories on the Stanford.[11]

Similar issues apply to tests on different scales – different possible ranges of scores, or different statistical modification or treatment of raw scores, for example, whether student test scores are first converted into standardized scores relative to an average score, or expressed on some other scale such as percentile rank (which is done is some cases but would generally be considered inappropriate). For instance, if a teacher is typically assigned higher performing students and the scaling of a test is such that it becomes very difficult for students with high starting scores to improve over time, that teacher will be at a disadvantage. But, another test of the same content or simply with different scaling of scores (so that smaller gains are adjusted to reflect the relative difficulty of achieving those gains) may produce an entirely different rating for that teacher.

Brief Description of Colorado Model

Colorado, Louisiana, and Tennessee have teacher evaluation systems proposed that will require 50% or more of the evaluations to be based on their students’ academic growth. This section summarizes the evaluation systems in these states as well as the procedural protections that are provided for teachers.

Colorado’s statute creates a state council for educator effectiveness that advises the state board of education.[12] A major goal of these councils is to aid in the creation of teacher evaluation systems that “every teacher is evaluated using multiple fair, transparent, timely, rigorous, and valid methods.”[13] Considerations of student academic growth must comprise at least 50% of each evaluation.[14] Quality measures for teachers must include “measures of student longitudinal academic growth” such as “interim assessments results or evidence of student work, provided that all are rigorous and comparable across classrooms and aligned with state model content standards and performance standards.”[15] These quality standards must take diverse factors into account including “special education, student mobility, and classrooms with a student population in which ninety-five percent meet the definition of high-risk student.”[16]

Colorado’s statute also calls for school districts to develop appeals procedures. A teacher or principal who is deemed ineffective must receive written notice, documentation used for making this determination, and identification of deficiency.[17] Further, the school district must ensure that a tenured teacher who disagrees with this designation has “an opportunity to appeal that rating, in accordance with a fair and transparent process, where applicable, through collective bargaining.”[18] If no collective bargaining agreement is in place, then the teacher may request a review “by a mutually agreed-upon third party.”[19] The school district or board for cooperative services must develop a remediation plan to correct these deficiencies, which will include professional development opportunities that are intended to help the teacher achieve an effective rating in her next evaluation.[20] The teacher or principal must receive a reasonable amount of time to correct such deficiencies.[21]

[1] Tim R. Sass, The Stability of Value-Added Measures of Teacher Quality and Implications for Teacher Compensation Policy, Urban Institute (2008), available at http://www.urban.org/UploadedPDF/1001266_stabilityofvalue.pdf. See also Daniel F. McCaffrey et al., The Intertemporal Variability of Teacher Effect Estimates, 4 Educ. Fin. & Pol’y, 572 (2009).

[2] Sass, supra note 27.

[3] Id.

[4] Bill & Melinda Gates Foundation, supra note 26.

[5] Peter Z. Schochet & Hanley S. Chiang, Error Rates in Measuring Teacher and School Performance Based on Student Test Score Gains (NCEE 2010-4004). Washington, DC: National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education (2010).

[6] Id.

[7] Id. at 12.

[8] Sean P. Corcoran, Jennifer L. Jennings & Andrew A. Beveridge, Teacher Effectiveness on High- and Low-Stakes Tests, Paper presented at the Institute for Research on Poverty summer workshop, Madison, WI (2010).

[9] Id.

[10] Id.

[11] Id.

[12] Co. Rev. Stat. § 22-9-105.5(2)(a) (2010).

[13] Id. § 22-9-105.5(3)(a).

[14] Id.

[15] Id.

[16] Id. The statute also calls for the creation of performance evaluation councils that advise school districts. Id. § 22-9-107(1). The performance evaluation councils also help school districts develop teacher evaluation systems that must be based on the same measures as that developed by the state council for educator effectiveness. Id. § 22-9-106(1)(e)(II). However, the performance evaluation councils lose their authority to set standards once the state board has promulgated rules and the initial phase of statewide implementation has been completed. Id. § 22-9-106(1)(e)(I).

[17] Id. § 22-9-106(3.5)(b)(II).

[18] Id.

[19] Id.

[20] Id.

[21] Id.

School Funding Myths & Stepping Outside the “New Normal”

I’ve been writing quite a bit lately about rather complex state school finance formula issues. That is much the point of this blog. But it may be hard for some to see how my recent posts on school finance relate back to the broader reform agenda, and to understand the implications of these posts for state policies. Let me try to summarize these posts – posts on spending bubbles, the “New Normal” and school finance PORK. My overarching goal in these posts is to explain that much of the reformy rhetoric about budget cuts and the “New Normal” is based on myth about how school funding works and what we should be doing in these catastrophic economic times.

Here are the myths and some of the realities.

Reformy myth #1: That every state has done its part and more, to pour money into high need, especially poor urban districts. It hasn’t worked, mainly because teachers are lazy and overpaid and not judged on effectiveness, measured by value-added scores. So, now is the time to slash the budgets of those high need districts, where all of the state aid is flowing, and fire the worst teachers. And, it will only help, not hurt.

Reality: Only a handful of states have actually targeted substantial additional resources to high need districts. See www.schoolfundingfairness.org. And the effort of states to finance their elementary and secondary education systems varies widely. Some states have in fact systematically reduced their effort to finance local public schools for decades. That is, the tax burden to finance public schools in some states is much lower now than it was decades ago. Very few states apply much higher effort than in the past. See: https://schoolfinance101.wordpress.com/2010/12/23/is-it-the-new-normal-or-the-new-stupid/

Reformy myth #2: The only aid to be cut, the aid that should be cut, and the aid that must be cut in the name of the public good, is aid to high need, large urban districts in particular. The argument appears to be that handing down state aid cuts as a flat percent of state aid is the definition of “shared sacrifice.” And the garbage analysis of district Return on Investment by the Center for American Progress, of course, validates that high need urban districts tend to be least efficient anyway. Therefore, levying the largest cuts on those districts is entirely appropriate.

Reality: As I have discussed in my series of recent posts, if there are going to be cuts – if states really believe that cuts to state aid are absolutely necessary, many state aid formulas include aid that is more appropriate to cut. That is, aid to districts who really don’t need that aid. Aid to districts that can already spend well above all others with less local effort. Aid to districts that will readily replace their losses in state aid with additional local revenues (or even private contributions). That’s the pork, and that’s where cuts, if necessary, should occur.

Reformy myth #3: The general public is fed up and don’t want to waste any more of their hard earned tax dollars on public schools. They are fed up with greedy teachers with gold plated benefits and fed up with high paid administrators. They don’t care about small class sizes and…well… are just fed up with all of this taxing and spending on public schools that stink. As a result, the only answer is to cut that spending and simultaneously make schools better.

Reality: The reality is that local voters in a multitude of surveys rate their own local public schools quite highly and that local voters when given the opportunity, even during the recent economic downturn, show very high rates of support for school budgets – including budgets with significant increased property taxes (the most hated tax). As I noted in a previous post, when New Jersey handed down state aid cuts to 2010-2011 school budgets and when- for the first time in a long time- the majority of local district budgets statewide failed to achieve approval from local voters, it was still the case that the vast majority (72%) of local budgets passed in affluent communities – in most cases raising sufficient local property tax resources to cover the state aid cuts. In another case, local residents in an affluent suburban community raised privately $420,000 to save full day kindergarten programs. Meghan Murphy’s analysis of Hudson Valley school districts shows that New York State districts also have attempted to counterbalance state aid cuts with property tax increases, but that the districts have widely varied capacity to pull this off. Parents in a Kansas district are suing in federal court requesting injunctive relief to allow them to raise their taxes for their schools (they use faulty logic and legal arguments, but their desire for better schools should be acknowledged!).

Reformy myth #4: None of this school funding stuff matters anyway. It doesn’t matter what the overall level of funding is and it doesn’t matter how that funding is distributed. As evidence of this truthiness, reformers point to 30+ years of huge spending growth coupled with massive class size reduction and they argue… flat NAEP scores, low international performance and flat SAT scores. Therefore, if we simply cut funding back to 1980 levels (adjusted only for the CPI) and fire bad teachers, we can achieve the same level of outcomes for one heck of a lot less money.

Reality: First of all, these comparisons of spending now to spending then are bogus. I address the various factors that influence the changing costs of achieving desired educational outcomes in this post: https://schoolfinance101.wordpress.com/2011/01/12/understanding-education-costs-versus-inflation/. Second, rigorous peer reviewed studies do show that state school finance reforms matter. Shifting the level of funding can improve the quality of teacher workforce and ultimately the level of student outcomes and shifting the distribution of resources can shift the distribution of outcomes. http://www.tcrecord.org/content.asp?contentid=16106 Similarly, constraining education spending growth over the long term can significantly harm the quality of public schools. See: https://schoolfinance101.wordpress.com/2010/04/22/a-few-quick-notes-on-tax-and-expenditure-limits-tels/

An opportunity for states?

I would argue that now… right now… represents a real opportunity for those states who actually want to step up, and really invest in the quality of their education systems and use the quality of their education systems to drive their economic futures.

I stumbled across this article http://www.foxnews.com/us/2011/02/13/states-offer-tax-breaks-guarantee-jobs/ on the Fox News website the other day, and it presents some useful insights for state policy makers regarding tax policy decisions and economic growth. I’ve written about the same point very early in my blogging (that the Small Business Survival Index in particular, misses some big points about location selection). Here’s a short section of the Fox News piece:

But there’s a catch to the anti-tax, pro-business rhetoric: Businesses consider a range of factors when deciding where to locate, including the quality of schools, roads and programs that rely on a certain level of public spending and regulation. And evidence suggests there is little correlation between a state’s tax rate and its overall economic health.

“Concerns about taxes are overstated,” said Matt Murray, a professor of economics at the University of Tennessee who studies state finance. “Labor costs, K-12 education and infrastructure availability are all part of a good business climate. And you can’t have those without some degree of taxation.”

States’ tax rates also do not predict their resilience during an economic downturn.

Arguably, no time is better than now. Other states are jumping on board with the “New Normal” reformy logic that slashing education budgets, increasing class sizes and narrowing curriculum around tested content areas is the only way to go. Yet, educated parents invariably want small class sizes (often topping the list of preferences for private or public schools) and want their children to have intellectually stimulating and broad, enriched curriculum. The current environment presents a great opportunity for some states to step outside the “New Normal” and truly race to the top with real investment in their public schooling systems.

Reformy Disconnect: “Quality Based” RIF?

I addressed this point previously in my post on cost-effectiveness of quality based layoffs, but it was buried deep in the post.

Reformers are increasingly calling for quality based layoffs versus seniority based layoffs, as if a simple dichotomy. Sounds like a no brainer when framed in these distorted terms.

I pointed out in the previous post that if the proposal on the table is really about using value-added teacher effect estimates versus years of service, we’re really talking about the choice between significantly biased and error prone – largely random – layoffs versus using years of service. It doesn’t sound as much like a no brainer when put in those terms, does it? While reformers might argue that seniority based layoffs are still more “error prone” than effectiveness rating layoffs, it is actually quite difficult to determine which, in this case, is more error prone. Existing simulation studies identifying value-added estimates as the less bad option, use value-added estimates to determine which option is better. Circular logic (as I previously wrote)?

We’re having this policy conversation about layoffs now because states are choosing (yes choosing, not forced, not by necessity) to slash aid to high need school districts that are highly dependent on state aid, and will likely be implementing reduction in force (RIF) policies. That is, laying off teachers. So, reformy pundits argue that they should be laying off those dead wood teachers – those with bad effectiveness ratings, instead of those young, energetic highly qualified ones.

So, here are the basic parameters for quality-based RIF:

1. We must mandate test-score based teacher effectiveness ratings as a basis for teacher layoffs.

2. But, we acknowledge that those effectiveness ratings can at best be applied to less than 20% of teachers in our districts, specifically teachers of record – classroom teachers – responsible for teaching math and reading in grades 3 to 8 (4 to 8 if only annual assessment data)

3. Districts are going to be faced with significant budget cuts which may require laying off around 5% or somewhat more of their total staff, including teaching staff.

4. But, districts should make efforts to layoff staff (teachers) not responsible for the teaching of core subject areas.

Is anyone else seeing the disconnect here? Yeah, there are many levels of it, some more obvious than others. Let’s take this from the district administrator’s/local board of education perspective:

“Okay, so I’m supposed to use effectiveness measures to decide which teachers to lay off. But, I only have effectiveness measures for those teachers who are supposed to be last on my list for lay offs? Those in core areas. The tested areas. How is that supposed to work?”

Indeed the point of the various “quality based layoff” simulations that have been presented (the logic of which is problematic) is to layoff teachers in core content areas and rely on improved average quality of core content teachers over time to drive system wide improvements. These simulations rely on heroic assumptions of a long waiting list of higher quality teacher applicants just frothing at the mouth to take those jobs from which they too might be fired within a few years due to random statistical error (or biased estimates) alone.

That aside, reduction in force isn’t about choosing which teachers to be dismissed so that you can replace them with better ones. It’s about budgetary crisis mode and reduction of total staffing costs. And reduction in force is not implemented in a synthetic scenario where the choice only exists to lay off either core classroom teachers based on seniority, or core classroom teachers based on effectiveness ratings (the constructed reality of the layoff simulations). Reduction in force is implemented with consideration for the full array of teaching positions that exist in any school or district. “Last in, first out” or LIFO as reformy types call it, does not mean ranking all teachers systemwide by experience and RIF-ing the newest teachers regardless of what they teach, or the program they are in. Specific programs and positions can be cut, and typically are.

And it is unlikely that local district administrators in high need districts would, or even should, look first to cut deeply into core content area teachers. So, a 5% staffing cut might be accomplished before ever cutting a single teacher for whom an effectiveness rating occurs – or very few. So, in the context of RIF, layoffs actually based on effectiveness ratings are a drop in the bucket.

So now I’m confused. Why is this such a pressing policy issue here and now? Does chipping away at seniority based provisions really have much to do with improving the implementation of RIF policies? Perhaps some are using the current economic environment and reformy momentum to achieve other long-run objectives?

Pork Hunting 101: Shredding the Pork

I love a good pulled pork sandwich. In fact, it’s one of my favorite foods. And the best damn pork sandwich I can think of is the Hog Heaven at Oklahoma Joe’s in the side of a freakin’ gas station in Kansas City, Kansas. I expect that this statement will perhaps be the most controversial statement I’ve made on this blog thus far. Really… it’s awesome… and one of the few things I really miss about being in Kansas City.

That aside, I’ve been blogging lately about a different kind of PORK – school finance pork. I started this pork campaign with a posting on state aid in New York after the announcement of Governor Cuomo’s proposed budget and education cuts. Despite large amounts of aid still being allocated to some of the wealthiest school districts in the nation, the good democratic Governor of New York decided it was somehow most appropriate to largely protect that aid, and instead slam the highest need, large urban, mid-sized city and poor rural districts in the state.

This post provides a primer on finding pork in state school finance formulas. It’s a two part process that begins with screening for pork, and then involves more intensive investigative research.

What is School Finance Pork? School finance pork is state aid that is currently being allocated to districts that otherwise don’t really need that aid. In this case, need is defined in terms of the needs of the students to be served AND in terms of the ability of the local public school districts and its residents and property owners to pay the cost of those services. In overly simple terms, some local public school districts can easily pay for the full cost of their needed educational programs and services on their own and with much less effort (tax effort) than others. Allocating state aid to these districts while depriving others with greater student needs and the inability to meet those needs is inexcusable. Cutting aid to needier communities who are unable to replace those lost revenues, while retaining aid to the wealthy is inexcusable. That’s PORK. And like other political pork-barrel spending, it exists because state legislators negotiate for state aid formulas that bring something home to their own districts.

How do legislators generate PORK? Pork can be generated by at least two different approaches. In the first approach, legislators actually try to manipulate the general state aid formula to find ways to argue that it’s actually more expensive to educate kids in wealthier communities, or alternatively that its cheaper to educate kids in poor communities. If legislators can raise the foundation funding targets to wealthy communities they can increase the likelihood that they can drive state support to those districts. Seems a bit of a stretch, but it’s been done by many. A second “within the formula” approach to generating pork is to adopt “minimum state aid” and/or hold harmless aid provisions that guarantee that no matter how wealthy a district is, that the state will still pick up X% of its formula funding. That is, the state will cover X% even if the district can raise double what it needs with very little tax effort. Another type of pork is Outside the Formula pork. It can be tricky (though not impossible) for legislators to sufficiently manipulate a state aid formula to provide more aid to wealthier districts. It’s relatively easy for state legislators to adopt outside the formula aid programs that allocate aid in very different ways – like flat allocations per pupil across all districts, regardless of local wealth, or even like New York’s property tax relief targeted to wealthier communities.

How do we screen for Pork? Here, I provide a few examples of screening for pork, using a national data set – the U.S. Census Bureau’s Fiscal Survey of Local Governments. As we stated in our Is School Funding Fair? A bare minimum goal of a state school finance formula would be to achieve a FLAT relationship between poverty and state and local revenue per pupil. Ideally, state school finance formulas would result in additional resources targeted to higher poverty districts – that is – systematically higher state and local revenue per pupil in higher poverty districts. One can take and state’s school finance data – here from 2007-08 – and make a graph of the a) local revenue per pupil, b) state aid per pupil and c) total revenue per pupil across districts by poverty. Here’s Texas:

I’ve dropped very small districts because they tend to scatter the pattern for a variety of legitimate cost related reasons. Blue circles represent the state and local revenue per pupil, which on average in Texas has a slight downward slope. Red triangles are the local revenue. Clearly, the lower poverty districts are raising quite a bit in local revenue but the higher poverty districts aren’t raising much. On average the state aid in green squares is being allocated in inverse proportion to the local revenue… but not that aggresively. For example, if we look way to the upper left…. we’ve got some red triangles – local revenue per pupil – above $10,000 per pupil. That is, some of these very low poverty districts are able to raise on their own, over $10k per pupil. But look at the trajectory of the state aid allotments – those districts are still getting a significant amount of state aid – enough to still raise their totals even higher as pointed out by yellow arrows. Would it not make more sense for this aid to be allocated to the districts at the far right hand side of the picture? Amazingly, the state aid distribution slope in Texas is quite gradual – to be kind. It would appear to include significant “flat” distributions of either or both minimum aid and/or outside the formula aid which goes to lower poverty and/or higher wealth districts the expense of higher need, lower wealth districts. THAT’s PORK!

Here’s New York, when using the same data set:

And here’s Pennsylvania:

In Pennsylvania, minimum aid provisions and flat distribution of Special Education aid are partly to blame.

And here’s Illinois:

Illinois also has minimum aid provisions in the form of alternative formulas for districts otherwise too wealthy to receive aid through the primary foundation formula. And Illinois allocates numerous categorical aids outside the formula in ways that reinforce the aggregate disparities.

So, here are four states and in each one, total state and local revenue per pupil is slightly to significantly lower per pupil in higher poverty than in lower poverty districts. And in each state, the state aid is distributed in ways that guarantee the provision of at least some – sometimes significant – aid per pupil to districts that on their own are raising and spending significantly more money per pupil than their much higher need counterparts.

How do we identify the source of the Pork? This part is a bit harder, and requires much more detailed investigation of the state school finance formulas and of the “runs” of state aid programs. I hope to get a chance to provide more information on pork finding at a later point. But for those interested in exploring these issues on their own, there are two sources of information that are critical to the search – a) the documentation that explains the calculations behind the allocation of state aid and b) much more importantly, what are called the “runs” of state aid allocations to local public school districts that are used by legislators to negotiate changes to the aid formula and/or the distribution of cuts. It’s that time of year – legislative session season. And legislators are interested to see which pieces of the aid formula will bring home pork, or how cuts will affect their districts. If you’re as warped as I am about this stuff… and actually really like digging through these numbers… contact your state legislators and find out who to get in touch with in order to get an electronic spreadsheet run of the various district by district state aid allocations. And be sure to get other basic district data, including wealth measures (property wealth and income) and enrollment characteristics (Total enrollments and student needs). Play around with graphs of aid (divided by pupils – per pupil aid) with respect to wealth and need measures – and look for those aid programs in particular that appear to allocate systematically more aid to districts that are otherwise less needy. Feel free to e-mail me spreadsheets of those runs. If I get a chance, I’ll see what pork I can find!

Cheers.

And here’s to Pork!

Disparate thinking: The administration’s blind eye & racially disparate impact

Apparently the U.S. Department of Education has decided to take on public education policies that not only are intentionally racially discriminatory, but also state and local policies that happen to have a racially disparate impact on certain populations. Now, these are departmental regulations, not statutes and not a constitutional protection, so this doesn’t mean that advocates can start filing lawsuits on behalf of groups disparately affected by education policies (except where other state laws prohibiting disparate impact exist, like Illinois). But, it does mean that the U.S. Department of Education can use its biggest available threat – denial of funding – to pressure state and local education agencies to change policies that result in racially disparate impact.

Statistically, what disparate impact means is that the policy in question results in a disproportionate effect on one group of individuals versus another, where “groups” are defined by race, ethnicity or national origin. There are many occurrences in public education where racially disparate impact rears its ugly head. For example, racially disparate classification rates of children with disabilities, or racially disparate rates of disciplinary action. Identifying appropriate policy changes to reduce racially disparate impact in any of these areas, while not compromising other interests is indeed important – such as making sure that kids with legitimate special education needs still get identified and served, or making sure appropriate discipline is handed out where necessary.

The administration’s renewed interest in racially disparate impact was announced almost a year ago, and has apparently crept back into the conversation in the past few weeks.

Here’s the Education Week synopsis:

At the Feb. 11 briefing, Ricardo Soto, the deputy assistant secretary for the Education Department’s office for civil rights, elaborated on the office’s new policy, saying that the Obama administration is “using all the tools at our disposal,” including “disparate-impact theory,” to ensure that schools are fairly meting out discipline to students. Some research shows, for instance, that suspension rates for African-American males in middle school can be nearly three times as high as those for their white, male peers.

So, the interest here is specifically the racially disparate distribution of disciplinary actions. That’s all well and good. We should explore these issues and resolve them appropriately if we can. Once again, the usual target is local public school districts because when it comes to any of today’s education policy issues – in the eyes of the current administration and their closest advisers – only local public districts and local administrators can be to blame (even when it comes to funding disparities?).

But, as my readers know, this a school finance blog, and that means that eventually this topic is coming back around to school finance (okay, not always). Could it possibly be that some states actually continue to operate state school finance formulas that produce “racially disparate” effects? That is, that districts serving larger shares of minority children have systematically (statistically) less state and local revenue (the revenue under control of state policy) than districts with fewer minority children? And if so, which states might those be?

Clearly, substantial disparities in the quality of education received (funding, class sizes, teacher credentials, etc.) by minority children as a function of state policies to provide, or not, sufficient financial support to their schools is at least equally relevant to rates of discipline referrals of minority versus non minority children attending the same school or district.

In our report Is School Funding Fair?, We evaluated states in terms of whether they provided for systematically more or less state and local revenue per pupil in higher versus lower poverty districts. Now, which states did the worst on this measure? Among the big ones, the most poverty disparate states in funding were Illinois, Pennsylvania and New York! New York makes this list largely because of its Pork barrel finance policies. And believe, me, Pennsylvania and Illinois have similar pork to shred. But poverty related disparities while important, don’t fall under this racially disparate impact umbrella. The real question here is which states have the largest racial disparities in school funding?

A few years back, Robert Bifulco, now at the Maxwell School at Syracuse, did a nice quick number crunch on black-white funding disparities nationally, in the Journal of Education Finance. On average, Bob found that without any corrections for costs related to student needs, or other costs, districts with higher concentrations of black enrollments had marginally higher per pupil spending. But after correcting for costs and needs, districts with higher black enrollments had lower per pupil spending. But, these findings, while interesting and useful, look at the nation as a whole, and, if I recall, do some breakouts by region.

Just like the poverty related variation we show in the fairness report, racial disparities in funding also vary widely across states. And those disparities occur for a variety of reasons. Yes, to some extent those disparities exist because of differences in local property wealth in blacker versus whiter communities and the state’s failure to allocate sufficient aid to offset those disparities. And in many places, those disparities in property values are largely a function of carefully planned racial segregation of housing stock and distribution of other property types (Brilliant article on real estate development in the Kansas City metro: http://www.tulane.edu/~kgotham/RestrCovenants.pdf)

BUT CAN A STATE LIKE NEW YORK REALLY CLAIM THAT THERE’S JUST NOT ENOUGH AID AVAILABLE TO FIX THE RACIAL DISPARITIES WHEN IT IS DUMPING TAX RELIEF AID AND MINIMUM FOUNDATION AID INTO THE WEALTHIEST DISTRICTS IN THE COUNTRY? Redistributing the pork might not erase the disparities entirely, but it’s a start.

Other disparities actually exist by the design of the aid formulas, and various Tricks of the Trade that create racial disparities in funding – deceptively and arguably quite intentionally. Here’s an outstanding sarcastic, critical analysis of how Kansas legislators gamed their finance system to embed racially disparate effects over time: http://www.pitch.com/2005-04-14/news/funny-math/ (after reading this, consider the role of real estate development addressed in the Kansas City article above!)

Quick side-bar – This brilliant piece of school finance journalism is written by Tony Ortega, when he was in Kansas City (written from the perspective of a KC Strip Steak… it’s a Kansas City thing). Now, Tony is editor and chief of the Village Voice in NY. He Tony, how ‘bout that New York finance stuff? Subsidies for Scarsdale, while cutting Utica and Middletown, or Mt. Vernon? (even if from the perspective of a NY Strip Steak?)

There are lots of Tricks of the Trade used to reduce aid to high minority districts. A favorite choice of state legislators is to allocate aid based on average daily attendance rather than enrollment. Higher poverty, higher minority concentration districts tend to have lower attendance rates… thus reducing their aid, compared to what they would receive if the aid was allocated on the number of enrolled pupils.

There was a brief period in the late 1990s when three separate federal court challenges were filed against racially disparate state school finance systems – in Pennsylvania (Powell v. Ridge), in New York (AALDF v. State) and in Kansas (Robinson v. Kansas). These cases were cut off by a series of related decisions in the early 2000s (which basically said that an individual does not have a right to sue over racially disparate effects, because the language of “racially disparate” effects appears only in regulations and not in the Civil Rights statutes themselves).

Interestingly, around that time, the Illinois legislature countered with state legislation that prohibits policies having racially disparate effect (Illinois Civil Rights Act of 2003). And as it turns out, Illinois school districts are applying this law to challenge the Illinois school funding formula – a formula that is among the most racially disparate in the nation.

http://www.jenner.com/news/news_item.asp?id=15009924

So, what’s my point here? I’m doing a bit of rambling. Well, here’s my best shot at a synopsis.

1. Yes, it is important that the current administration explore the reasons for, and possible resolutions of racially disparate effects in such areas as discipline referrals or special education placement rates (although they appear focused on the former, not latter).

2. Yes, there exists the likelihood that local public school districts are engaged in practices that harm minority populations, ranging from the previously mentioned issues, to others such as highly tracked curricular offerings which result in significant within school and within district segregation, as well as attempts by some local boards of education to undo long running diversity plans.

3. But, I would argue, that while these issues are important, there are other at least equally important issues to be addressed – substantial racial disparities in funding and resulting programs and services – in some states far more than others. AND ILLINOIS IS ONE OF THOSE STATES!

4. And those racial disparities in funding are not entirely a result of not enough money to offset differences in local wealth, or neglect of the state aid formula (underfunding the formula) – WHICH IS BAD ENOUGH – but are largely a function of maintaining PORK in affluent communities at the expense of poor minority districts, and of Tricks of the Trade – or specific provisions in state school funding formulas that drive money to whiter districts and create or reinforce racial disparities.

READINGS

Green, P.C., Oluwole, J., Baker, B.D. (2010) Getting their hands dirty: How Alabama’s public officials may have maintained separate and unequal education. West’s Education Law Reporter 253 (2) 503‐
520

Green, P.C., Baker, B.D., Oluwole, J. (2008) Obtaining racial equal educational opportunity through school finance litigation. Stanford Journal of Civil Rights and Civil Liberties IV (2) 283‐338

Baker, B.D., Green, P.C. (2005) Tricks of the Trade: Legislative Actions in School Finance that Disadvantage Minorities in the Post‐Brown Era American Journal of Education 111 (May) 372‐413

Baker, B.D., Green, P.C. (2003) Commentary: The Application of Section 1983 to School Finance Litigation. West’s Education Law Reporter. 173 (3) 679‐696

Green, P.C., Baker, B.D. (2002) Circumventing Rodriguez: Can plaintiffs use the Equal Protection Clause to challenge school finance disparities caused by inequitable state distribution policies? Texas
Forum on Civil Liberties and Civil Rights 7 (2) 141 – 165

Where’s the Pork? Mitigating the Damage of State Aid Cuts

This is a very long and complicated post, so I’ll give you a few take home points up front…

Equity Center Radio on School Finance Pork: http://216.246.105.5/Audio_Recordings/2011-0053_ECRadio_Bruce_Baker_02-18-2011.mp3

Take home points

Crude assumptions promoted by the “new normal” pundit crowd that across the board state aid cuts are a form of shared sacrifice are misguided and dreadfully oversimplified.
State aid cuts hurt some districts and the children they serve more than others, even when those aid cuts are “flat” across districts. Districts with greater capacity will readily recover their losses (and then some) with other revenue sources. Those who can’t are out of luck.
State aid cuts as a proportion of state aid are particularly bad because they take the most from those who need the most.
Some might find it surprising that many state school finance formulas contain special provisions that allocate relatively large sums of aid to very affluent school districts – districts that could easily pay for the difference on their own and serve relatively low need student populations. One can readily identify hundreds of millions of dollars in New York State being allocated as aid to some of the nation’s wealthiest school districts.
State legislators and Governors often protect this aid – which I refer to as school finance PORK – even while slashing away disproportionately at aid for the neediest districts.
That’s just wrong!

Now for the lengthy post…

It’s budget proposal and state-of-the-state time of year right now. And Governors from both parties are laying out their state budget cuts, many refusing to consider any type of “revenue enhancements” (uh… tax increases). These include New York’s Governor Cuomo suggesting a 7% cut to state aid.

There exists a baffling degree of ignorance being spouted by pundits about school budget cuts. Stuff like – NY’s Governor cut school funding by 7%… Now, school districts need to figure out how to cut their spending by 7%! Everyone, 7% less, across the board! Shared sacrifice! It will make everyone better and more efficient in the long run (especially those high poverty districts that we know are least efficient of all)! Pundits argue – Cuts have to be made. Cuomo’s cuts are a perfect example of this reality (from a Dem. Governor). Everyone will be cut. Just suck it up and learn to deal with the New Normal.

Wrong – at so many levels it’s hard to even begin explaining reality to those pundits who’ve clearly never even taken the most basic course on public finance or public school finance and have absolutely no understanding of the interplay between “local” property tax revenues and state aid, or the process of school budget planning and adoption. More on this later.

Thankfully, most readers of my blog and many in the general public actually seem to understand this stuff better than the blowhards (bloghards and tweethards) leading the “new normal” campaign from their DC think tanks. Particularly astute are those families with children who have interest in the quality of their local public schools and live in states where local school district budget setting remains at least partially an open public budgeting process.

And there a few good education writers out there who have developed a solid grip on the interplay between state and local revenues and the resulting effects of state aid cuts. Meghan Murphy, in the Hudson Valley in New York State has done some particularly nice writing on the topic: http://www.recordonline.com/apps/pbcs.dll/article?AID=/20100426/NEWS/100429738

My reason for this post is to expand on a point made by Meghan Murphy in her writing on Hudson Valley Districts and by David Sciarra in this Education Week Article:

But David G. Sciarra, the executive director of the Education Law Center, argued that if states cut funding to school districts during this difficult financial period, the pain will be felt most by disadvantaged students. Impoverished districts have little local property-tax wealth to draw from, and so state aid is a lifeline, said Mr. Sciarra, whose Newark, N.J.-based group advocates for poor students and schools. He urged state officials to work cooperatively with districts in the years ahead to set budget priorities so that current inequities aren’t made worse.

State officials “have an obligation to look for better ways to spend money,” Mr. Sciarra said, but “they’ve got to be very careful in how they do this. Across-the-board cuts and freezes have a negative impact on schools in need.” Ideas about how to cut spending are often proposed at “30,000 feet,” he said, but officeholders need to “take a serious look at how [cuts] would play out in their state.”

Yes, that’s the point. Cuts don’t mean “cuts” or “across the board” uniform distribution, “shared sacrifice,” at least not as they are typically implemented. In general, cuts to state aid lead to increased inequity. They hurt some more than others. Some districts, in fact, have little problem overcoming cuts – rebalancing their budgets, while others, well, to put it simply – are screwed. In many states, those most screwed by aid cuts are those who’ve been most screwed all along.

The model underlying local school budgets is that local voter/citizen/parent/homeowners (not entirely overlapping) desire a certain level or quality of schooling, usually in tangible terms like class sizes or specific programs they wish to see in their schools. That is, the local voter may not be able to “guess” the per pupil expenditure of their district (nor is it particularly relevant if they can) and evaluate whether it’s enough, too much or too little, but the local voter can evaluate whether that per pupil dollar buys the programs and services – and class sizes that voter wants to see in his/her local school district and whether they are willing to add another dollar to that mix.

When the state cuts aid to local school districts the usual first local response is to figure out how to raise at least an equal sum of funding – plus additional funding to accommodate increased costs – so as to maintain the desired schooling (class sizes, programs, etc.). Few local voters seem to really want to cut back service quality. Depending on the state (or type of district within the state), district officials put together a budget requiring a specific amount of revenue – which in turn dictates the property tax rate required to raise that revenue – given the state aid allotted – and then the budget is approved by referendum – or other adoption (or back-up mediation) process.

Clearly some communities have much greater capacity than others to offset their state aid losses with additional local revenues!

For example, when New Jersey handed down state aid cuts to 2010-2011 school budgets and when- for the first time in a long time- the majority of local district budgets statewide failed to achieve approval from local voters, it was still the case that the vast majority (72%) of local budgets passed in affluent communities – in most cases raising sufficient local property tax resources to cover the state aid cuts. In another case, local residents in an affluent suburban community raised privately $420,000 to save full day kindergarten programs. Meghan Murphy’s analysis of Hudson Valley school districts shows that New York State districts also have attempted to counterbalance state aid cuts with property tax increases, but that the districts have widely varied capacity to pull this off. Parents in a Kansas district are suing in federal court requesting injunctive relief to allow them to raise their taxes for their schools (they use faulty logic and legal arguments, but their desire for better schools should be acknowledged!)

Distribution of State Sharing

There’s a bit of important background to cover here. State aid formulas drive state funding – usually from income and sales tax revenues collected to the state general fund – out to local public school districts based on a number of different factors. Typically these days, state funding formulas start with a calculation of the amount of money – state and local – that should be available at a minimum in order to provide adequate public schools. That is, each district is assigned a target amount of state and local funding. That target amount usually varies by the types of students served in a district and by other factors such as regional labor costs and remote, rural locations. In any case, each district ends up with a different estimate of total funding needs.

Next, the aid formula includes a calculation of the amount of that funding target that should be paid for with local property taxes – a local fair share, per se, or local contribution. One approach is to determine how much each district would raise if each district adopted a uniform property tax rate. For those districts that raise more than their target funding with that tax rate alone, the state would kick in nothing. For those districts that implement the local fair share tax rate and still come up short of their target funding, the state would apply aid to cover the difference.

So, for example, you might have three districts in a state, where:

The first district has a very low need student population (almost all from affluent, educated families), and has significant taxable property wealth. That district might have a target funding per pupil of $10,000, and might be able to raise all of it with an even lower tax rate than would be required. In fact, if they put up the local fair share tax rate, they might raise $20,000 per pupil. And the school finance system might allow them to do that.
A second, “middle class” district that has a modest share of children in poverty in the district, leading to an estimated target funding of $12,000 per pupil. The district adopts the local fair share tax rate and raises $8,000. The state allocates the additional $4000.
And the third district, a high need district with weak property tax base, might end up with an estimated target funding of $15,000 per pupil and after adopting the local fair share tax rate only raises $2,000 per pupil, so the state allocates $13,000.

THAT’S THE BASIC STRUCTURE OF A ‘FOUNDATION AID’ FORMULA!!!!

Now, I should be very clear here that the relationship between student needs and tax base is not really that simple. Both parts of this puzzle are critical to the formula and don’t always move in concert. There exist districts with high value tax base and high need student population and vice versa, and for many reasons.

Here’s an example of the distribution of the basic “sharing ratio” for New York State school districts, with respect to district Income/Wealth (IWI) ratio. For the lowest income/wealth districts, the state share is about 90%. For wealthier districts, that ratio – in theory – drops to 0.

Figure 1: NY State Sharing Ratio by District Income/Wealth

Types of Cuts

When it comes to state aid cuts, there’s really nothing for the state to cut from the first district above – that is – unless for some reason the state is giving other money to that district that can raise double it’s need target with the same local tax rate and no state support. But that would be silly, right? More later on that. Here are the two most common approaches to handing out cuts:

Option 1: Cut state aid proportionately across the board

This is actually usually the worst option and most regressive. Let’s take our districts above. A 5% cut to state aid for our first district is, of course, nothing. A 5% cut for our middle class district is $200 per pupil. A 5% cut to state aid for our high need district is $650 per pupil. Yes – the biggest cut comes down on the highest need district.

This distribution of cuts is problematic for two reasons. First, the biggest cut falls on the neediest kids. Second, the biggest cut falls on the district with the least capacity to offset that cut. I’m assuming here that these districts have all adopted the local fair share property tax rate. That rate only raises $2,000 per pupil in revenue for the high need district whereas the same rate raises 4X as much in the middle class district. Clearly for this reason alone, even if the cuts were in equal amounts per pupil, the middle class district would have a much easier time replacing the aid cut with local resources. Further, there a plethora of additional factors that increase the likelihood that the middle class district can offset the cut more easily than the high need, low-income district.

Option 2: Cut funding targets proportionately across the board

A better, though still problematic approach is for the state to recalculate the funding targets – – to reduce that level by just enough to result in the same state aid savings. Taking this approach leads to a constant per pupil cut in state aid across districts, for those districts receiving at least as much state aid per pupil as is being cut.

This too is of no consequence for our district that needs no foundation aid. Let’s say this approach leads to a $400 per pupil across the board reduction in target funding. If we still assume that districts are to raise the same amount of local revenue (local fair share of the fully funded target amount, as opposed to raising the local share of the lowered amount), this would result in a 10% aid cut to the middle class district ($400/$4000) and a 3.1% cut to the high need district. The per pupil target funding change would be the same. But the middle class district would certainly complain that their cut as percent of aid is much larger. But again, that district has 4X the local revenue raising capacity on a dollar per pupil basis, and in this case, they still got less than 4X the per pupil cut.

Even this approach is likely to lead to larger average budget reductions in higher need districts.

Again, there are tons of additional factors involved, and various ways that states might cut aid differently to yield different distributional effects across districts.

State Aid Formulas have Lots of Parts! Some better than others!

The state aid cut scenarios presented above assume a logical and oversimplified world of state aid and local district budgets in many ways. The cut scenarios presented above adopt one really big assumption about state aid to local schools – an assumption that may not be and usually isn’t entirely true:

That the only state aid available to cut is aid that is allocated in proportion to wealth and need across districts. That because state aid is allocated in greater proportion – if not almost in its entirety to poor and need school districts, then those districts must necessarily suffer most from the cuts! As a result, the best you can do is to spread evenly those cuts across wealthy and poor, higher and lower need districts and live with the fact that some will bounce back easier than others.

The fact is that state aid formulas may at their core be built on a seemingly logical foundation funding structure with state and local sharing as described above. But rarely these days does a state aid formula make it into law without a multitude of adjustments and other PORK added on. Yes, Pork! School finance pork and lots of it. Clearly state reps from those towns that would otherwise get nothing from the general aid formula are going to search for a way to bring home some pork.

Here are a few examples of school finance pork from the New York State school finance formula:

1) Minimum Foundation Aid: Like many state funding formulas, even though the first (and most logical) iteration of calculations for estimating the district state share of funding would end up providing 0% state aid to many districts, those formulas include a floor of funding – a minimum guarantee of state aid. That’s right, even our district above that could raise double their target funding by applying the local fair share tax rate would get something. Perhaps this is a reasonable tradeoff when the money is there. But do we really want to keep allocating that money to a district that’s a) fine on its own and b) could easily replace the money with modest increases to local taxes – and would do so.

Here, for example, is the effect of New York State’s minimum threshold factor on foundation aid. The red diamonds indicate the foundation state aid that would be received if districts got what is initially calculated to be their state share. State share hits 0 at an income/wealth index around 1.0 in the basic calculation. But, the actual calculation of state share includes a few adjustments shown in blue squares. First, between income/wealth ratios of about 1.0 to 2.0, the actual state share cuts the corner providing more gradually declining aid rather than going straight to 0. Then, above IWI of 2.0 it never hits 0, but rather levels off providing a minimum allotment of several hundred to about $1,000 per pupil to even the wealthiest districts in the state (which, by the way, are among the wealthiest in the nation!).

Figure 2: Application of Original Calculation and Adjusted Calculation for State Aid Share

Altogether, the adjustments – which also yield additional aid for New York City – add up to nearly $3.8 billion dollars. That’s right… $3.8 billion (where’s that NY Mega Millions guy when you need him?). Now, assuming that it’s hard to get the sharing ratio correct for NYC to begin with and that it is a very high need district that in fact needs this aid, it’s really just over $2.0 billion in potential excess allocation that could be redistributed. That ain’t chump change.

But let’s go really conservative here, and just look at the minimum aid being shuffled out to the richest communities. That alone is still $134 million dollars.

At the very least, if you’re going to cut state aid, cut this first! If you’re not going to cut, consider redistributing this.

2) School Tax Relief Aid: Many state aid formulas include a variety of other types of aid, some which are distributed in flat amounts across all districts regardless of need, and some which may be even allocated in inverse proportion to what most would consider needs – either local capacity related needs or educational programming and student needs. Such is the politics of school finance. For those really interested in this stuff, see the following two articles:

Baker, B.D., Green, P.C. (2005) Tricks of the Trade: Legislative Actions in School Finance that Disadvantage Minorities in the Post-Brown Era American Journal of Education 111 (May) 372-413

Baker, B.D., Duncombe, W.D. (2004) Balancing District Needs and Student Needs: The Role of Economies of Scale Adjustments and Pupil Need Weights in School Finance Formulas. Journal of Education Finance 29 (2) 97-124

New York State’s piece de resistance is a program called STAR, or School Tax Relief program. In simple terms, STAR provides state aid in disproportionate amounts to wealthy communities to support property tax relief. Here’s the distribution of STAR aid with respect to district income/wealth ratios:

Figure 3: STAR Aid per pupil and District Income/Wealth Ratio

Excluding STAR aid to NYC, the aid program in 2008-09 provided $642 million in aid to districts with an income/wealth ratio over 1.0! Even in New York State, that’s not chump change. It’s over $150 million to the wealthiest districts. Add these hundreds of millions to those above and we’re getting somewhere.

Once again, at the very least, if you’re going to cut state aid, cut this first! If you’re not going to cut, consider redistributing this.

For more information on the equity consequences of STAR (as well as simulated solutions), see: http://eus.sagepub.com/content/40/1/36.abstract

A closer look at aid to the wealthy in New York State

Here’s a closer look at minimum foundation aid and STAR aid received by several of the state’s wealthier communities (at least statistically wealthier). Note that our recent report on school funding fairness (www.schoolfundingfairness.org) identified New York State (along with Illinois and Pennsylvania) as having one of the most regressively financed systems in the nation. On average, low poverty districts have greater state and local revenue than high poverty ones, yet the state is still allocating significant sums of aid to low poverty districts!

Table 1: Aid to the Wealthy in New York

This table shows that many of these wealthier communities are picking up millions in STAR aid and upwards to a thousand dollars per pupil in basic foundation aid. Yes, the state is subsidizing the spending – quite significantly – of some of the wealthiest districts in the nation, while maintaining a regressive system as a whole. And now, while cutting aid disproportionately from poor districts.

The distribution of proposed aid cuts

Here is the distribution of the Governor’s proposed cuts in foundation aid to NY State school districts, on a per pupil basis, with respect to Income/Wealth Ratios of school districts:

Figure 4: Governor’s proposed cuts to foundation aid and income/wealth ratios

The income/wealth ratio is along the horizontal axis. The per pupil cuts in aid are on the vertical axis. Here, I’ve represented district size by the size of the “bubble” in the graph. New York City is the bowling ball here! And NYC gets a larger per pupil cut than many much wealthier districts – wealthy districts that actually receive aid! Excuse me, PORK!

Yes, districts with very high income/wealth ratios will experience little cut at all. $100 per pupil or so will look like a big cut of their $1000 per pupil in foundation aid, but some low wealth districts will actually see their foundation aid cut by $1000 per pupil.

And with respect to the Pupil/Need Index:

Figure 5: Governor’s proposed cuts to foundation aid and pupil need index

In this figure, as student needs increase, per pupil cuts in aid increase. Yep, that’s right, districts that NY State itself identifies as having higher needs receive larger per pupil cuts in aid, even while the PORK… yes PORK is retained in the system! All the while, the state continues to allocate minimum foundation aid and STAR funding to wealthy districts.

Summing it up

Yes, it may suck for legislators or the Governor to tell Scarsdale, Pocantico Hills or Locust Valley that there’s just not enough aid to continue to subsidize their “tax relief” (PORK) or to subsidize their very small class sizes and rich array of elective courses and special programs with add-ons such as minimum foundation aid. But aid programs for these districts are merely the bells and whistles – or PORK – of state school finance policies, the political tradeoffs which are often needed to get a formula passed. It’s not a politically easy task, but it’s time to collect that pork and redistribute it to where it’s actually needed.

Further, it is highly unlikely that these districts will actually go without those bells and whistles – that they will forgo their furs and Ferraris. Yes, we all know that school finance formulas are a complicated mix of political tradeoffs. The goal of this post is to make that painfully clear. Let’s call it what it is – school finance Pork. And let’s take that pork and make better use of it. And let’s make it absolutely clear that protecting the pork while slashing basic needs is entirely unacceptable.

There may not be enough pork in the system to either cover all of the proposed cuts, or to be redistributed to fully resolve the funding deficits of higher need districts. But in New York State and many others, there’s quite a bit – quite a bit of aid that could be either used to make the formula fairer to begin with or to buffer the neediest districts and children they serve from suffering the most harmful and real funding cuts.

Note: On Cuts and Caps

Now, some of what I discuss herein is complicated by the current bipartisan political preference to show affluent communities that we’re not going to push these costs off onto them in the form of property tax increases – or backhanded property tax increases through state aid cuts. Instead, we’ll tell them they simply can’t raise their property taxes to cover the difference. That’ll learn ’em!

Capping property taxes while cutting state aid, is simply lying about the true cost of the programs and services desired by local citizens, who invariably have supported tax increases for their local schools and have reverted to major private giving when tax increases were not feasible. There are legitimate reasons to control local spending variation – a topic for another day – and states need to have such policy tools available. But slash-and-cap policies are generally shortsighted and ill-conceived.

Cutting and limiting property taxes merely shifts the burden in yet a different direction – increased fees, private fundraising, volunteering etc. All of which increase inequities in access to quality schooling.

Yeah, I know… all of the pundity types are saying that voters are fed up… they totally underestimate what’s being spent on their schools and they’re totally fed up with paying higher taxes. They don’t want any more of this school spending – bloated bureaucracy, etc. I might buy that if the local voter behavior in affluent communities with preferences for high quality schooling actually supported that argument. But it doesn’t!

1. On average, nationally, we now put more funding into higher poverty school districts than lower poverty ones (to no avail)

3. High need, poor urban districts (in addition to misallocating all of their resources to the schools serving rich white kids in their district???) are simply wasting massive sums of money on things like cheer leading and ceramics.

4. None of this school funding equity – between district stuff – matters anyway!

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