Ranking the states by different methods

Every year, through many different sources, state politicians and political activists make great waves over which state spends more on public education, and which spends less. Who’s in first place? Who’s in last? Those from differing perspectives have different motives. Politicians and anti-tax, anti-government activists search for their way to find that “our states spends more than everyone else and gets nothing for it,” while others hoping to increase education spending search frantically for low ratings – “We’re in last place and that’s a disgrace!” Of course, not everyone can be in first or last place and it’s pretty damn hard to tweak the numbers to move a state from near the top to near the bottom. Here, I’ll present a few alternative, reasonable rankings – the last two of which, I believe are most reasonable, though for some states still differ significantly.

First, let’s begin with the simplest version of the numbers- the straight up averages of school district state and local revenue per pupil (weighted by the number of pupils in each district). Now, I use the state and local revenue per pupil instead of current expenditures per pupil because state and local revenue gives a complete picture of state and local resources allocated to local public school systems and excludes expenditures of federal funds.

Politicians in New Jersey and New York love to make claims that their state is highest spending in the nation (and we get nothing for it!). Even at this most basic level, these claims are wrong. Close, but still wrong. Hooray for Vermont! But isn’t that really part of Canada anyway?

FIGURE 1

Of course, the cost of running a school varies quite significantly across states with a large share (though not all) of that variation being tied to regional differences in the competitive wage one must pay to teachers. Here, I use a competitive wage index developed by the National Center for Education Statistics (by Lori Taylor and Bill Fowler) which uses variation in non-teacher wages across labor markets to correct for variation in teacher wages. http://nces.ed.gov/edfin/adjustments.asp

FIGURE 2

Some reshuffling occurs. States like California, for example drop quite a bit because California is certainly a more expensive place to live and higher wage state. But, we may be assuming too strong a role for the wage adjustment here – assuming that state and local revenues per pupil should move on a 1 for 1 basis with wage variation. Nonetheless, not a totally unreasonable comparison.

We might also wish to consider the student populations that states must educate with their funding at present levels. That is, how much are these current dollars worth toward achieving common outcomes across students? Many cost factors influence the cost of achieving common outcomes across children, as discussed in this paper –http://surface.syr.edu/cgi/viewcontent.cgi?article=1102&context=cpr – by William Duncombe and John Yinger of the Maxwell School at Syracuse University. But, this particular paper focuses on the additional costs associated with children in poverty. Duncombe and Yinger determine that the additional cost per child falling below the federal poverty line is approximately 150% of the cost of achieving the same outcomes with a non-poor child. They also find that the additional costs associated with counts of children falling below the 185% poverty threshold are approximately 100% above average costs. Now, I go very conservative here, and I apply only a 100% weight of additional cost to children qualifying as being in poverty, using U.S. Census Bureau Small Area Income and Poverty Estimates.

FIGURE 3

But, we’ve been learning more of late about problems with using the same income thresholds for poverty across states with very different costs of living. Recently, the U.S. Census Bureau put out this exceptional paper which includes adjusted poverty measures for each state, based on three different adjustment methods – http://www.census.gov/hhes/www/povmeas/papers/Geo-Adj-Pov-Thld8.pdf. In general, the adjustments lead to much higher actual poverty rates in states with very high costs of living such as New York and California. If we use those poverty rates instead of the previous ones to adjust spending for poverty, we get this:

FIGURE 4

In this case, California moves into 49^th place, or third from bottom with Washington DC in the analysis.

We’re still missing a pretty big piece of the puzzle here – additional costs associated with economies of scale and population sparsity (for more information on economies of scale see: http://www-cpr.maxwell.syr.edu/efap/Publications/Revisiting_Economies.pdf). Notice that Wyoming and Alaska are our big spenders here. Now, there’s probably no adjustment we can find to fully account for the many ways in which Alaska is different from the lower 48. Nor do the poverty corrections seem to fully address the difficulties of Washington DC. It’s all pretty imperfect. That said, I take one more stab at things, based on a regression model which attempts to control for a) competitive wage variation, b) economies of scale and population density and c) poverty. The idea here is to account for the fact that some states have a need for very small schools and districts because of their small populations which are spread across vast rural expanses. The model attempts to avoid giving a break to other states like New Jersey or Illinois that have many tiny racially segregated enclaves in densely populated suburbs. And here are the results:

FIGURE 5

An important difference when using a regression model to determine relationships between cost factors and revenue levels – instead of just dividing by the cost factors – is that the model determines the weight of those cost factors. On thing that happens in the figure above is that the influence of wage differentials is “softened.” The model-based projections do not assume a 1 for 1 relationship between competitive wages and revenue. As a result, California does not come out as low as in previous figures. Also, the model is projecting state and local revenues for a district with X% poor children. The projection is for an “equated” condition. But, if the state has far more children in higher poverty settings, and those settings have fewer resources, the model projection does not necessarily reflect the average actual conditions. However, for California in particular, there really isn’t a systematic relationship between poverty and revenues across districts – a finding that is as bad as it might seem good. In fact, it’s just bad!

Aren’t the differences really all about state wealth?

I would be remiss if I didn’t include at least a few ugly scatterplots in this post. So here goes. The first two scatterplots here show that state and local revenues per pupil are somewhat modestly related to state average poverty rates (not adjusted regionally) and to the household income levels of families with children in the public school system.

FIGURE 6

FIGURE 7

However, this final figure shows that state and local revenues per pupil are equally related to the effort a state puts up, where effort is measured in terms of state and local revenue per pupil as a share of gross domestic product by state, or gross state product. That is, some states that don’t raise much revenue per pupil simply don’t try that hard. Very few high spending states have low effort. Tennessee, Louisiana, Oklahoma, Arizona and South Dakota are near the bottom because they don’t put up much effort. Mississippi puts up average effort, but just can’t raise much revenue. I’m far more empathetic to Mississipi’s plight! Well, our highest spender, Vermont in some cases, is off the charts on effort. Despite having less capacity than states like New York or New Jersey, Vermont still manages to outspend them.

FIGURE 8

While it makes great rhetoric to claim “first in the nation” or “last in the nation” or “most expensive,” the best one can really do here is to delineate in terms of relatively high or relatively low. Not great headline stuff, but that’s how it goes. New Jersey – NOT THE HIGHEST IN THE NATION – rather, “relatively high.” California – NOT LAST IN THE NATION – but damn close to it by some measures, and still low by others!

About a week ago, The College Board released their latest status report in their college completion series.

http://completionagenda.collegeboard.org/sites/default/files/reports_pdf/Progress_Executive_Summary.pdf

The parts of the report that seemed to grab the most media attention were those related to a) comparing the US to other countries on the percent of 25 to 34 year olds who hold an associates degree or higher and b) comparing US states to one another on the same measure.

Newspapers across the country ran with this stuff and Twitter was buzzing with punditry on what these indicators meant about the quality of K-12 public schools in each state. Our public schools must be failing us if we’re only 24th on the education level of our younger adults – one Missouri pundit tweeted (related news story here).

The first thing that caught my eye was that Washington, DC was first in the rankings of percent of 25 to 34 year olds with an associates degree or higher.  Of course it is. Washington DC is a magnet for recent college graduates. Clearly, this particular indicator says as much about the employment options for a young, college educated workforce as it does about a state’s own education system. This indicator also tells us something about the education level and expectations of the previous generation – parents of these 25 to 34 year olds, whether in the same state or elsewhere. And, this indicator may also tell us something about the extent to which a state imports or exports college students.

So, I decided to play with some data…’cuz that’s what I like to do… just to see how these rankings might change if I tweaked them a bit.

I decided it might be fun to look at the differences in the rates of college educated adults – % of 25 to 34 year olds with a bachelors degree or higher – across states in three different ways:

1. percent of 25 to 34 year old current adult residents who hold a BA or higher
2. percent of 25 to 34 year old adult current residents who were born in the state who hold a BA or higher
3. percent of 25 to 34 year old adults who were born in the state, whether they continue to reside there or not, who hold a BA or higher

It would seem to me that the second of these measures is most on target – the percent of the native population that holds a certain level of education. Needless to say, when I focus on the second measure, the rankings change somewhat. Here it is:

Table 1

Education Level (% BA or Higher) of the 25 to 34 Year Old Population by State

U.S. Census – American Community Survey 2006 to 2008

Data Source: Steven Ruggles, J. Trent Alexander, Katie Genadek, Ronald Goeken, Matthew B. Schroeder, and Matthew Sobek. Integrated Public Use Microdata Series: Version 5.0 [Machine-readable database]. Minneapolis: University of Minnesota, 2010.

Washington DC which ranks 1st on resident college graduates drops to 24th on native college graduates. MA, NY and NJ which were 2, 5 & 4, are now 1, 2, 3. Virginia goes from 9th to 26th and Maryland goes from 6th to 15th when only natives are considered. This is likely a DC effect as well. NH also drops quite a bit. Wisconsin rises quite a bit. Overall, there are some pretty big changes here.

Here are a few scatterplots – ‘cuz nothin’ is more fun than a good scatterplot.

This one shows on the horizontal axis, the share of 25 to 34 year old residents who are natives (born there).  On the vertical axis is the % BA or higher for all current residents. There’s DC, way above the rest on the vertical axis and pretty far to the left on the horizontal – that is, not too many 25 to 34 year olds who live there, were born there. The native share is only lower in Nevada. But Nevada doesn’t seem to be importing college grads!

This one shows the relationship between the % BA or higher among all current residents (horizontal axis) and % BA or higher among native residents (born and live there). Clearly there’s a pretty strong relationship between the two. But, there is enough variation to really change some rankings. Mass is high either way.  The big movers are those identified above, like Maryland, Virginia and New Hampshire, which have much more educated resident young adult populations than native resident young adult populations.

This one puts the “native share” again on the horizontal axis. On the vertical axis is a measure of the difference in the education level of all current residents (25 to 34) and native current residents. It’s somewhat of a net “import” effect measure. How much more educated is the current resident population than the born and raised population? Now, this is net difference, including the fact that some individuals who were born and raised in a state might have left and become more educated. Big net importers here appear to be Maryland, Virginia and Vermont and New Hampshire (Vermont surprised me a bit here… since there isn’t a whole lot of industry to attract college grads, but Burlington does always make those “great places to live” lists). It might also be a small sample size issue with the Vermont data. At the other end of the picture are Nebraska and Nevada, which don’t appear to importing a more educated adult population. Strangely, all but Nebraska are in the positive zone on this measure (note that this measure does not have to be net-zero across states because between state migration is not the only type of migration occurring. International migration may also affect these differences. This may also reflect the fact that more educated individuals tend to be more mobile. Just pondering).

In this one, we have the “native share” again on the horizontal axis, and the difference between the education level of those born in the state – whether they stayed or not – and those who reside in the state. This is somewhat of a net “export” measure. In this case, it would appear that Wyoming is the big loser. So too are Nebraska and Wisconsin. This is the one interesting piece about Wyoming. In the rankings above, Wyoming doesn’t move much. It’s 47th in % BA for current residents and 48th for native residents. But, Wyoming does much better on the education level of those born in the state, whether they stay or not – which apparently they don’t if they have a BA or higher.

So what does all of this mean? Probably not much. These figures and additional analyses certainly tell a more nuanced story than the media buzz of last week. But, it’s hard to really link much of this back to the quality of states’ underlying elementary and secondary education systems. Far too many factors are in play here, and even tweaking this one factor – whether residents are native residents or not- has significant consequences for state rankings.

So much for attaching any simple, bold statement about [YOUR STATE HERE] to that huge, pull-out multi-color map in the College Board Report!

The spin is on. As soon as the annual school district level U.S. Census fiscal survey data are released, news outlets across the country take their shot a spinning the data to show just where their state stands. New York #1! Utah… dead last! Hawaii “above average.” Spending just really high (totally out of context)! Typically, news outlets point out spending is high when they wish to argue that it’s too high… and we should do something to curb it. No mention is made of outcomes achieved with that spending, or which districts in the state are responsible for the high average. When spending is reported as low, the spin is generally that it is too low, and that state policymakers should do something about it.

Allow me to briefly present a slightly more nuanced picture. For the past few years, and in a number of publications, I have used a statistical model of the national school finance data to correct for such issues as a) economies of scale and population density, b) regional variation in competitive wages, and c) variations in student needs. I use this model to project what a school district, with comparable characteristics, would have in state and local revenue per pupil in each state. The methods of this madness were used in this study: http://epaa.asu.edu/ojs/article/viewFile/718/831

Here are some of the results with the 2007-08 Census Fiscal Survey data (with the model built on data from 2005-06, 2006-07  & 2007-08).

Before getting to the modeled estimates of comparable state and local revenue, lets take a quick look at the relative educational effort of each state, or the combined State and Local Revenues for K-12 education as a share of Gross State Product. Vermont and New Jersey lead the pack on this on, with other states including Maryland and New York in the mix. Note, however, that this effort can be quite unevenly distributed. In fact, it may be the case that a significant amount of effort is going into local property tax revenues being raised by the richest communities in a state. Yeah… it’s still a lot of effort, but selectively distributed among those who can put up that effort and choose to as long as it benefits (or is perceived to benefit) their own children. Total effort provides a limited window, but important one nonetheless.

Fun Fact about this first table – TAKE A LOOK AT OUR RACE TO THE TOP, ROUND 1 WINNERS! (47TH & 50TH ON EFFORT!!!!)

Now to the model based estimates of who’s really in the top and bottom ten on state and local revenue per pupil for elementary and secondary education. Let’s begin by looking at those states where the lowest poverty districts have the highest and lowest resources.

Yep, New York is #1 in per pupil state and local revenues for very low poverty districts! Indeed, very affluent Long Island and Westchester County school districts in New York State spend about as much as any districts in the nation, largely because they have the financial capacity to do so (and partly because the state has enabled them to!)

Next in line in funding for very low poverty districts are Wyoming and Vermont, which really don’t have many children attending incredibly high poverty districts. Notably, New Jersey falls well behind New York state for low poverty districts, and many of New Jersey’s affluent suburbs lie in the same labor market with the higher spending affluent New York suburbs. And then there’s Tennessee – one of our great RttT winners.  Of course, as I have shown on a previous post, this works fine for TN, which as the lowest state assessment cut scores – so most of the kids pass the tests anyway (low standards & low funding – a winning combination indeed)!  

The next table ranks per pupil funding for high poverty districts.  Notably, New York is NOT in first place on this one. New York drops to 6th, but the situation is somewhat more complicated. While this might appear okay, it can be particularly difficult for high poverty New York state school districts to recruit and retain high quality teachers when they are surrounded by so many affluent districts which already hold the recruitment and retention advantage, and have substantially more resources. For high poverty districts, New Jersey and Wyoming come in first. Wyoming is simply high across the board. And yep… there’s Tennessee again – our RttT winner in 47th place!

This next table ranks the within-state FAIRNESS of the state school funding distribution – where fairness is determined by taking the ratio of high poverty funding to low poverty funding – with the implicit assumption that state school finance systems should provide for additional support in districts serving children with greater needs. Now, this table must be taken in the context of the previous two. For example, Utah comes in first on “fairness.” But, in this case, this merely means that low poverty districts in Utah get nothing, and high poverty districts in Utah get next to nothing! In a twisted sense, that’s “fair?????”

Among states not at the bottom in overall resources, New Jersey, Ohio, Minnesota and Massachusetts seem to be driving additional resources into higher need, higher poverty districts.

States  at the other end of the spectrum include New York, Pennsylvania and Illinois. These are among the historically least equitable large, diverse states in the country. Now, to Pennsylvania’s credit, these calculations precede the phase-in of their new funding formula which the governor has continued to support even during the recession. New York and Illinois are another story. Yeah… New York also implemented – okay – kind of planned to implement a new formula. That didn’t get very far, and it is highly unlikely (okay, almost entirely unlikely based on other analysis I’ve conducted on more recent NY data) that NY has actually improved since 2007-08.  Illinois hasn’t even tried – in fact, Illinois just keeps getting worse and worse!

Now for an obligatory point – Many argue that the overall funding level in states is simply a function of their wealth. Wealthier states, like wealthier school districts within states simply have the ability to spend more. That is indeed partly true. But effort also matters – remember that first slide above?  This scatterplot shows the relationship between state effort and funding levels in a hypothetical average poverty school district. There’s actually a reasonably strong relationship here, but for a few quirky outliers. In fact, based on additional analyses, a state’s effort explains about as much of the funding level as does a state’s wealth.

So, Mississippi is a very poor state that puts up relatively average effort, but simply can’t get very far with that effort. By contrast, Tennessee and Louisiana both have much higher fiscal capacity (measured by gross state product per capita) than Mississippi, but they simply don’t use it. Tennessee has little excuse for its spending level! Nor does Louisiana!

Finally, here’s a snapshot of the association between 8th grade reading and math NAEP performance and funding levels across states. As it turns out, funding levels for high poverty settings were most strongly associated with NAEP performance for all students. As one can see, there exists a reasonable correlation between funding levels and NAEP mean scale scores. That said, as I have noted in previous posts regarding such relationships, there’s a lot of circular stuff all tangled up in here. Wealthier states with more educated adult populations supporting higher education spending – and supporting and encouraging their children to do well in school, etc.  But, it is difficult to conceive how a state in the bottom left corner of this picture (very low funding in high poverty districts – and most likely, low funding across the board) can begin to lift itself out of that corner – or Race to the Top. Financial resources are a necessary underlying condition, albeit easier to achieve in some states than in others.

Note: Difficulties arise when trying to make simple comparisons of funding levels and funding gaps with achievement gaps between poor and non-poor children in each state a) because income thresholds used for subsidized lunch status characterize very different populations from one region of the country to another and from rural to urban settings within states, and b) because gaps between non-poor and poor children in states depend significantly on how wealthy are the non-poor and how poor are the poor. Sadly, these complexities make it very difficult if not impossible to use NAEP data to untangle the relationship between funding differences between lower and higher poverty districts, and outcome differences between children attending those districts in different states:

I discuss the poverty measurement problems here:

https://schoolfinance101.wordpress.com/2009/11/27/title-i-does-not-make-rich-states-richer/

Kevin Welner and I discuss evaluating the relationship between state school funding distribution and student outcomes here:

https://schoolfinance101.com/wp-content/uploads/2010/05/doreformsmatter_formatted.pdf

Here’s a quick run down on the distribution of cuts to state aid for New Jersey school districts for 2010-2011. Much has been made in the media about how the cuts are most severe for wealthy communities. The graphs below show that that depends much on what you’re looking at. Yes, these communities lose a much larger share of their state aid, but their general fund budgets are much less dependent on state aid. When viewing the cuts in terms of per pupil aid cuts, the cuts are actually higher in higher poverty districts.

As a refresher – District Factor Groups are essential wealth/income classifications, where I and J are relatively wealthy generally suburban districts and A and B represent poorer, often (though not entirely) urban core districts.

There’s just not enough time in the day to deal with all of the absurdity out there right now and to do so in any thorough way. So, here are a couple of quick replies to what I’ve seen in the past week.

1. Cato released They Spent What? http://www.cato.org/pubs/pas/pa662.pdf in an attempt to show how much more public school districts in any one location spend than “private schools.” So, Cato took total expenditure data (all capital outlay, etc.) for urban public school systems for 2009 and divided it by enrolled students to get a maximized total expenditure figure for public school districts. Then, they compared this maximized public expenditure figure to a “median” (not mean, or mean weighted by actual # of students served, but a deflated, school level median) tuition level from a national sample of private schools (about 2,300) from 2003-04 and rather arbitrarily assumed that tuition from 2003-04 represents 80% of total expenditures and that private school tuition rose by $347 per year between 2003-04 and 2009.  Note that even at the region level (not the city level used by Cato) the Schools and Staffing Survey includes relatively small samples of private schools. Further, Cato provides little justification for either the 80% or $347 figure – except to refer to their own “expert” on the topic (and some location specific reports). For a better analysis of actual private school spending with detail on samples and data sources, see: http://epicpolicy.org/publication/private-schooling-US (note that I make one similar inappropriate comparison in this report where I used operating expenditures for Wash DC in the discussion section. Other than that, this report compares totals to totals and based on actual financial documents of private schools).

2. Much has been made in recent days of the closure of about half of the Kansas City Missouri Public Schools. Some have compared this event to Central Falls firings – Taking a hard line on “failing urban schools in need of reform.” Wow… that’s a stretch. Others have casually thrown around rhetoric of $2 billion dollars wasted on social engineering in Kansas City, in reference to desegregation litigation that extended from the 1970s through 2003. For a more precise history of what actually went on during the desegregation litigation, see: http://law.bepress.com/expresso/eps/1213/

Also, note that in 2006 (2007?), the Missouri legislature passed a law which allowed the remaining predominantly white residential corner of Kansas City Missouri School District to vote itself out of KCMSD and into a neighboring predominantly white district (since the deseg case was over, apparently this was okay). This resulted in the transfer of a large share of children and a handful of school facilities to Independence, MO. As a result of this and other factors (including increased charter school enrollment), KCMSD now serves about half the number of children it did when I first lived there about 12 years ago.  While I’m unaware of the specific reorganization plan to be implemented, it would seem that some reorganization might be warranted. But, it should also be acknowledged that the State of Missouri has, since 1995, continuously pulled back financial support for KCMSD while advancing policies that leave KCMSD with a more needy, albeit smaller, student population to serve. More on this at a later point.

3. New Jersey’s Governor has proposed a constitutional amendment placing a cap on both local property tax growth and on state expenditure growth. It would appear that the proposal would allow for local referendum to override the cap, but it is a cap nonetheless and one based on an arbitrary constraint of 2.5%. What do we know about such “Tax and Expenditure Limitations,” or TELs? In short, they are generally bad policy which do not lead to “economic growth” (“private” sector growth) and which often lead to decreases in a) the quality of public school teachers and b) student outcomes.  That is, hard caps on state expenditures and/or local property taxes tend to harm public service quality – specifically public education – and really don’t provide other economic growth benefit. Colorado’s TABOR is a particularly striking example. The local override option would result in significant inequities across municipalities and school districts which have widely varied capacity to achieve an override. Link to analysis of Massachusetts’ Prop 2 1/2.

4. Finally, the NCLB Blueprint (http://www2.ed.gov/policy/elsec/leg/blueprint/blueprint.pdf) was released by the Dept. of Ed. The Blueprint contains only this short reference to “equity,” and seems to emphasize the district responsibility over the state responsibility – in part by the sequence of their reference, but also by reference to “comparability” which is terminology specifically related to within district allocations and Title I aid.

Greater equity. To give every student a fair chance to succeed, and give principals and teachers the resources to support student success, we will call on school districts and states to take steps to ensure equity, by such means as moving toward comparability in resources between high- and low-poverty schools.

As I’ve noted many times before, the greatest disparities between higher and lower poverty schools are those that exist between, not within districts, largely because few districts have both high and low poverty schools. Rather, there are higher and lower poverty districts. I will post more on this topic when my forthcoming article on within versus between district disparities comes out in the near future.

Apparently, we now have a national initiative underway to replicate the Central Falls, Rhode Island drama across poor urban and inner urban fringe schools and districts. This national initiative places blame for school failure directly on school principals (who, by necessity must be replaced if reform is to happen) and on at least 50% of each failing school’s teachers. Once again, there is no attention paid to whether or not the STATE has, in fact, fulfilled its obligation to provide (or ensure) equitable and adequate financial resources for the district or schools under fire. None – no mention of it whatsoever. Clearly, it must be the principal’s and teachers’ fault if a school fails, regardless of the resources available to that school or host district???

Few locations nationwide provide more stark examples than Duncan’s home state of Illinois for why this “blame the school,” “blame the teachers,” “blame the central district office” perspective is so deeply problematic.

To date (assuming Pennsylvania continues to follow through on its finance reforms), Illinois maintains the most regressive state school finance system in the nation. What I mean by this is that Illinois maintains a system whereby higher poverty school districts – and higher minority concentration school districts – receive systematically less state and local revenue per pupil than lower poverty ones. And the disparities in Illinois are far greater than almost anywhere else in the country.

Here, for example, is the relationship between the relative level of state and local revenue per pupil (2007-08) and district shares of low-income children for school districts in the immediate Chicago region. 1.0 on the vertical axis represents the average state and local revenue. A value of less than 1.0 indicates less than average state and local revenue in the region. As poverty increases, relative state and local revenue decreases. There are no fancy cost adjustments applied here. This is just straight up, state and local revenue – the bulk of school funding.

So – why does this matter? The reality is that these much higher need districts require more, not fewer resources if we expect them to approach comparable outcomes. On the one hand, they need more resources in order to provide greater staffing intensity (staffing quantities) to provide the level of services needed to overcome background differences of the children they serve. In addition, it is increasingly well understood that to recruit and retain comparably qualified teachers into higher poverty, higher minority concentration settings requires a higher wage than in surrounding districts which happen to have more desirable working conditions. In short, higher need districts require a greater number of comparable teachers, who come at a higher price per teacher. That gets expensive.  So, even if these higher poverty districts had comparable state and local revenue, they’d be in a bind.

Here’s a map of district composite test scores expressed as standard deviations from the mean of districts serving comparable grade levels (because proficient rates tend to shift by grade level). Deeper brown colors are the “low performers” and darker blue colors are the “high performers.” We can quickly identify a few good candidates for whipping out the Central Falls playbook! There they are – in Deep Brown… the failures… who must be reformed… replaced…!!! And of course, it’s their own darn fault! Mismanagement. An abomination. Our kids deserve better!!!!! How dare these adults treat them so poorly!!!! (in this map, purple dots indicate majority Hispanic schools and red dots, majority black schools).

Okay, enough of that. Note above that most of these low performing districts are also districts with majority black or Hispanic schools. Here’s the same map but with school poverty represented as circle size. Notably, these low performing districts also have very high poverty schools.

Now let’s go back to the scatterplot of relative state and local revenues, but focus on the highest poverty school districts:

Our very low performing, high poverty districts are also very low in state and local revenue per pupil relative to their surrounding districts! Again, this graph includes no fancy attempt to adjust the value of state and local revenues for the different costs and needs faced by these districts.

But, this map does!!! Here, I have estimated a statistical model to determine the relative costs in each district of achieving state average composite scores, and from that model I have calculated just how much more (or less) each district would need to spend per pupil in order to be able to achieve state average outcomes (if it did so at average efficiency).Districts in the deepest red color would need over $5,000 more in per pupil current operating spending in order to have a shot at achieving average outcomes. Districts in blue have well more than what they need in order to achieve averageness.

Each of our Central Falls candidates is in deep red here. Each has well less than it would need in order to achieve average outcomes. And that deficit is a function of inequitable and selectively inadequate state funding for local public school districts.

Should we really be blaming, exclusively, the teachers and principals in these schools and districts for their failures? Does adequate and equitable state and local funding of these schools and districts have absolutely no place in the current policy discourse? How can two guys from Illinois take such a stand and do so with a straight face?

Isn’t the state at least partly to blame for which schools and districts are failing under the STATE’s accountability system and under the STATE’s approach to financing schools? Do we really think that we can fix these schools and districts by dumping half the teachers, firing the principal and giving them a one time infusion of federal funding without ever addressing the systemic failures of the state’s education policies?

I’m not trying to argue that these schools and districts actually do have great teachers who should be considered untouchable. They may not. They may have among the least qualified teachers available on the local labor market. It might even actually be a good idea to replace some or many of them. But, replacing these teachers with “better” teachers will require appropriate sustained resource levels – not a one shot infusion of federal bailout, or a constant churning of cadres of eager, well-educated and well-meaning volunteers.