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Take your SGP and VAMit, Damn it!

In the face of all of the public criticism over the imprecision of value-added estimates of teacher effectiveness, and debates over whether newspapers or school districts should publish VAM estimates of teacher effectiveness, policymakers in several states have come up with a clever shell game. Their argument?

We don’t use VAM… ‘cuz we know it has lots of problems, we use Student Growth Percentiles instead. They don’t have those problems.

WRONG! WRONG! WRONG! Put really simply, as a tool for inferring which teacher is “better” than another, or which school outperforms another, SGP is worse, not better than VAM. This is largely because SGP is simply not designed for this purpose. And those who are now suggesting that it is are simply wrong. Further, those who actually support using tools like VAM to infer differences in teacher quality or school quality should be most nervous about the newly found popularity of SGP as an evaluation tool.

To a large extent, the confusion over these issues was created by Mike Johnston, a Colorado State Senator who went on a road tour last year pitching the Colorado teacher evaluation bill and explaining that the bill was based on the Colorado Student Growth Percentile Model, not that problematic VAM stuff. Johnston naively pitched to legislators and policymakers throughout the country that SGP is simply not like VAM (True) and that therefore, SGP is not susceptible to all of the concerns that have been raised based on rigorous statistical research on VAM (Patently FALSE!).  Since that time, Johnston’s rhetoric that SGP gets around the perils of VAM has been widely adopted by state policymakers in states including New Jersey, and these state policymakers understanding of SGP and VAM is hardly any stronger than Johnston’s.

This brings me back to my exploding car analogy. I’ve pointed out previously that if we lived in a society where pretty much everyone still walked everywhere, and then someone came along with this new automotive invention that was really fast and convenient, but had the tendency to explode on every third start, I think I’d walk. I use this analogy to explain why I’m unwilling to jump on the VAM bandwagon, given the very high likelihood of falsely classifying a good teacher as bad and putting their job on the line – a likelihood of misfire that has been validated by research.  Well, if some other slick talking salesperson (who I refer to as slick Mikey J.) then showed up at my door with something that looked a lot like that automobile and had simply never been tested for similar failures, leading the salesperson to claim that this one doesn’t explode (for lack of evidence either way), I’d still freakin’ walk! I’d probably laugh in his face first. Then I’d walk.

Origins of the misinformation aside, let’s do a quick walk through about how  and why, when it comes to estimating teacher effectiveness, SGP is NOT immune to the various concerns that plague value-added modeling. In fact, it is potentially far more susceptible to specific concerns such as the non-random assignment of students and the influence of various student, peer and school level factors that may ultimately bias ratings of teacher effectiveness.

What is a value-added estimate?

A value added estimate uses assessment data in the context of a statistical model, where the objective is quite specifically to estimate the extent to which a student having a specific teacher or attending a specific school influences that student’s difference in score from the beginning of the year to the end of the year – or period of treatment (in school or with teacher). The best of VAMs attempt to account for several prior year test scores (to account for the extent that having a certain teacher alters a child’s trajectory), classroom level mix of students, individual student background characteristics, and possibly school characteristics. The goal is to identify most accurately the share of the student’s value-added that should be attributed to the teacher as opposed to all that other stuff (a nearly impossible task)

What is a Student Growth Percentile?

To oversimplify a bit, a student growth percentile is a measure of the relative change of a student’s performance compared to that of all students and based on a given underlying test or set of tests. That is, the individual scores obtained on these underlying tests are used to construct an index of student growth, where the median student, for example, may serve as a baseline for comparison. Some students have achievement growth on the underlying tests that is greater than the median student, while others have growth from one test to the next that is less (not how much the underlying scores changed, but how much the student moved within the mix of other students taking the same assessments, using a method called quantile regression to estimate the rarity that a child falls in her current position in the distribution, given her past position in the distribution).  For more precise explanations, see: http://dirwww.colorado.edu/education/faculty/derekbriggs/Docs/Briggs_Weeks_Is%20Growth%20in%20Student%20Achievement%20Scale%20Dependent.pdf

So, on the one hand, we’ve got Value-Added Models, or VAMs, which attempt to construct a model of student achievement, and to estimate specific factors that may affect student achievement growth, including teachers, schools, and ideally controlling for prior scores of the same students, characteristics of other students in the same classroom and school characteristics. The richness of these various additional controls plays a significant role in limiting the extent to which one incorrectly assigns either positive or negative effects to teachers. Briggs and Domingue run various alternative scenarios to this effect here: http://nepc.colorado.edu/publication/due-diligence

On the other hand, we have a seemingly creative alternative for descriptively evaluating how one student’s  performance over time compares to the larger group of students taking the same assessments. These growth measures can be aggregated to the classroom or school level to provide descriptive information on how the group of students grew in performance over time, on average, as a subset of a larger group. But, these measures include no attempt at all to attribute that growth or a portion of that growth to individual teachers or schools. That is, sort out the extent to which that growth is a function of the teacher, as opposed to being a function of the mix of peers in the classroom.

What do we know about Value-added Estimates?

  • They are susceptible to non-random student sorting, even though they attempt to control for it by including a variety of measures of student level characteristics, classroom level and peer characteristics, and school characteristics. That is, teachers who persistently serve more difficult students, students who are more difficult in unmeasured ways, may be systematically disadvantaged.
  • They produce different results with different tests or different scaling of different tests. That is, a teacher’s rating based on their students performance on one test is likely to be very different from that same teacher’s rating based on her students performance on a different test, even of the same subject.
  • The resulting ratings have high rates of error for classifying teacher effectiveness, likely in large part due to error or noise in underlying assessment data and conditions under which students take those tests.
  • They are particularly problematic if based on annual assessment data, because these data fail to account for differences in summer learning, which vary widely by student backgrounds (where those students are non-randomly assigned across teachers).

What do we know and don’t we know about SGP?

  • They rely on the same underlying assessment data as VAMs, but simply re-express performance in terms of changes in relative growth rather than the underlying scores (or rescaled scores).
    • They are therefore susceptible to at least equal error of classification concern
    • Therefore, it is reasonable to assume that using different underlying tests may result in different normative comparisons of one student to another
    • Therefore, they are equally problematic if based on annual assessment data
  • They do not even attempt (because it’s not their purpose) to address non-random sorting concerns or other student and peer level factors that may affect “growth.”
    • Therefore, we don’t even know how badly these measures are biased by these omissions? Researchers have not tested this because it is presumed that these measures don’t attempt such causal inference.

Unfortunately, while SGPs are becoming quite popular across states including Massachusetts, Colorado and New Jersey, and SGPs are quickly becoming the basis for teacher effectiveness ratings, there doesn’t appear to be a whole lot of specific research addressing these potential shortcomings of SGPs. Actually, there’s little or none! This dearth of information may occur because researchers exploring these issues assume it to be a no brainer that if VAMs suffer classification problems due to random error, then so too would SGPs based on the same data. If VAMs suffer from omitted variables bias then SGP would be even more problematic, since it includes no other variables. Complete omission is certainly more problematic than partial omission, so why even bother testing it.

In fact, Derek Briggs, in a recent analysis in which he compares the attributes of VAMs and SGPs explains:

We do not refer to school-level SGPs as value-added estimates for two reasons. First, no residual has been computed (though this could be done easily enough by subtracting the 50th percentile), and second, we wish to avoid the causal inference that high or low SGPs can be explained by high or low school quality (for details, see Betebenner, 2008).

As Briggs explains and as Betebenner originally proposed, SGP is essentially a descriptive tool for evaluating and comparing student growth, including descriptively evaluating growth in the aggregate. But, it is not by any stretch of the imagination designed to estimate the effect of the school or the teacher on that growth.

Again, Briggs in his conclusion section of his analysis of relative and absolute measures of student growth explains:

However, there is an important philosophical difference between the two modeling approaches in that Betebenner (2008) has focused upon the use of SGPs as a descriptive tool to characterize growth at the student-level, while the LM (layered model) is typically the engine behind the teacher or school effects that get produced for inferential purposes in the EVAAS. (value-added assessment system) http://dirwww.colorado.edu/education/faculty/derekbriggs/Docs/Briggs_Weeks_Is%20Growth%20in%20Student%20Achievement%20Scale%20Dependent.pdf

To clarify for the non-researcher, non-statisticians, what Briggs means in his reference to “inferential purposes,” is that SGPs, unlike VAMs are not even intended to “infer” that the growth was caused by differences in teacher or school quality.  Briggs goes further to explain that overall, SGPs tend to be higher in schools with higher average achievement, based on Colorado data. Briggs explains:

These result suggest that schools that higher achieving students tend to, on average, show higher normative rates of growth than schools serving lower achieving students. Making the inferential leap that student growth is solely caused by the school and sources of influence therein, the results translate to saying that schools serving higher achieving students tend to, on average, be more effective than schools serving lower achieving students. The correlations between median SGP and current achievement are (tautologically) higher reflecting the fact that students growing faster show higher rates of achievement that is reflected in higher average rates of achievement at the school level.

Again, the whole point here is that it would be a leap, a massive freakin’ unwarrented leap to assume a causal relationship between SGP and school quality, if not building the SGP into a model that more precisely attempts to distill that causal relationship (if any).

It’s a fun and interesting paper and one of the few that addresses SGP and VAM together, but intentionally does not explore the questions and concerns I pose herein regarding how the descriptive results of SGP would compare to a complete value added model at the teacher level, where the model was intended for estimating teacher effects. Rather, Briggs compares the SGP findings only to a simple value-added model of school effects with no background covariates,[1] and finds the two to be highly correlated. Even then Briggs finds that the school level VAM is less correlated with initial performance level than is the SGP (where that correlation is discussed above).

So then, where does all of this techno-babble bring us? It brings us to three key points.

  1. First, there appears to be no analysis of whether SGP is susceptible to the various problems faced by value-added models largely because credible researchers (those not directly involved in selling SGP to state agencies or districts) consider it to be a non-issue. SGPs weren’t ever meant to nor are they designed to actually measure the causal effect of teachers or schools on student achievement growth. They are merely descriptive measures of relative growth and include no attempt to control for the plethora of factors one would need to control for when inferring causal effects.
  2. Second, and following from the first, it is certainly likely that if one did conduct these analyses, that one would find that SGPs produce results that are much more severely biased than more comprehensive VAMS and that SGPs are at least equally susceptible to problems of random error and other issues associated with test administration (summer learning, etc.).
  3. Third, and most importantly, policymakers are far too easily duped into making really bad decisions with serious consequences when it comes to complex matters of statistics and measurement.  While SGPs are, in some ways, substantively different from VAMS, they sure as heck aren’t better or more appropriate for determining teacher effectiveness. That’s just wrong!

And this is only an abbreviated list of the problems that bridge both VAM and SGP and more severely compromise SGP. Others include spillover effects (the fact that one teacher’s scores are potentially affected by other teachers on his/her team serving the same students in the same year), and the fact that only a handful of teachers (10 to 20%) could be assigned SGP scores, requiring differential contracts for those teachers and creating a disincentive to teach core content in elementary and middle grades.  Bad policy is bad policy. And this conversation shift from VAM to SGP is little more than a smokescreen intended to substitute a potentially worse, but entirely untested method with a method for which serious flaws are now well known.

 

Note: To those venders of SGP (selling this stuff to state agencies and districts) who might claim my above critique to be unfair, I ask you to show me the technical analyses conducted by a qualified fully independent third party that shows that SGPs are not susceptible to non-random assignment problems, that they miraculously negate bias resulting from differences in summer learning even when using annual test data, that they have much lower classification error rates when assigning teacher effectiveness ratings, that teachers receive the same ratings regardless of which underlying tests are used and that one teacher’s ratings are not influenced by the other teachers of the same students. Until you can show me a vast body of literature on these issues specifically applied to SGP (or even using SGP as a measure within a VAM), comparable to that already in existence on more complete VAM models, don’t waste my time.


[1] Noting: “while the model above can be easily extended to allow for multivariate test outcomes (typical of applications of the EVAAS by Sanders), background covariates, and a term that links school effects to specific students in the event that students attend more than one school in a given year (c.f., Lockwood et al., 2007, p. 127-128), we have chosen this simpler specification in order to focus attention on the relationship between differences in our choice of the underlying scale and the resulting schools effect estimates.”

Should there be a Constitutional Right to Unlimited Property Taxation?

A Reply to Dunn and Derthick in Education Next

Anyone who has read my previous work knows I’m not generally a fan of tax and expenditure limits. A significant body of empirical research does show that strict tax and expenditure limits can cause significant damage to state school finance systems over the long haul. For example, Author David Figlio in a study of Oregon’s Measure 5 (National Tax Journal Vol 51 no. 1 (March 1998) pp. 55-70) finds that: Oregon student-teacher ratios have increased significantly as a result of the state’s tax limitation. David Figlio and Kim Rueben in the Journal of Public Economics (April 2001, Pages 49-71) find: Using data from the National Center for Education Statistics we find that tax limits systematically reduce the average quality of education majors, as well as new public school teachers in states that have passed these limits. In a non-peer reviewed, but high quality working paper, Thomas Downes and David Figlio “find compelling evidence that the imposition of tax or expenditure limits on local governments in a state results in a significant reduction in mean student performance on standardized tests of mathematics skills.” (http://ase.tufts.edu/econ/papers/9805.pdf)

Despite my general concerns over tax and expenditure limits, I have even greater concern over legal arguments like those posed by an affluent suburban school district in Kansas, summarized by Joshua Dunn and Martha Derthick in the Fall 2011 issue of Education Next. As Dunn and Derthick explain, beginning in the 1990s Kansas imposed limits on the amount of revenue local public school districts can raise above and beyond the revenue they are guaranteed through the state general fund aid formula. One affluent suburban district outside of Kansas City recently filed a legal challenge to those limits in Federal District Court, and that legal challenge was the subject of Dunn and Derthick’s recent column. Dunn and Derthick explain the legal arguments as follows:

Citing Supreme Court decisions in Meyer v. Nebraska (1923) and Pierce v. Society of Sisters (1925), which held that the liberty guaranteed in the Fourteenth Amendment’s Due Process Clause includes a right of parents to control the education of their children, the plaintiffs charged that the local cap infringes on that right. As well, by forbidding additional taxes it limits their right to use their property as they wish. Still more inventive, they invoked the First Amendment right of assembly, saying that the cap prevents voters from expressing their collective wishes at the ballot box. These violations together, they contended, constitute a denial of equal protection of the law.

http://educationnext.org/trouble-in-kansas/

So then, what’s wrong with considering the individual liberty to unlimited property taxation? If such liberties apply to campaign contributions or other forms of assembly, then why not to the choice to levy whatever property tax one sees fit? And what’s wrong with linking the notion of complete “local” control over property taxation to the notion of parental control over the education of one’s own children? Ah, if it was only so simple. But it’s not, and here’s a primer on why.

A Little Background Tax and Expenditure Limits (TELs)

State imposed limitations on the taxing behavior of state recognized intermediate and local jurisdictions fall into a broad category of state fiscal management policies known as Tax and Expenditure Limits, or TELs. Tax and expenditure limits have been around for decades and exist in one form or another across nearly every state.

Arguably, the modern era of Tax and Expenditure Limits began with the adoption by statewide referendum of California’s Proposition 13 in 1978, which included a series of limits to the taxable assessed values of properties and changes in those assessed values and included an overall tax rate cap.  Daniel R. Mullins and Bruce A. Wallin (2004) note that “Within two years of the passage of Proposition 13 (a California initiative), 43 states had implemented some kind of property tax limitation or relief.” [1]  By 2004, Mullins and Wallin indicate that Forty-six states have some form of constitutional or statutory statewide limitation on the fiscal behavior of their units of local government.

Statewide limitations on local property taxes exist in multiple forms across states.

Overall Property Tax Rate Limits: Mullins and Wallin note that limits on property tax rates are the most common form of Tax and Expenditure Limit. Overall property tax rate limits restrict the total (municipal, school and other) property tax rate which can be adopted by local jurisdictions. Overall property tax rate limits may but do not necessarily include an option for local override votes. That is, property tax rates are limited but may be exceeded by local voter approval, often including such restrictions as requiring a super-majority vote to achieve override. Mullins and Wallin note that 33 states have imposed property tax rate limits, with 31 limiting municipalities, 28 counties, 26 school districts and 23 all three types (p. 7).

Specific Property Tax Rate Limits: Specific property tax rate limits apply limitations to tax rates for one component of local public goods or services, for example a rate limit on municipal taxes only or a rate limit on property taxes for  operating revenues for local public schools, or for capital outlay revenues for local public school. Again, override options may or may not be included.

Property Tax Revenue Limit: Property tax revenue limits place limits on the revenue that may be derived from property taxes in a given year, regardless of the rate applied. Revenue limits may either be applied to the total revenue allowable (revenue level) or, more commonly to the rate of increase in revenue allowable.

Assessment Increase Limit: Because property tax revenues collected, and tax bills paid by property owners are a function of both the tax rate applied and the assessed value of properties, constraints placed on the allowable growth in assessed value also operate as property tax limitations.

General Revenue or Expenditure Limit: States also place caps on the total amount of revenue that can be raised from property taxes for specific purposes, or alternatively on the amount of property tax revenue that can be raised and expended in a given year. Like other limits, these may be placed on either the total level or revenue or expenditures or on the annual growth in revenue or expenditures, and may or may not be coupled with override options (where those override options are also specified in state laws).

Finally, many states include complex combinations of the above property tax and expenditure limits, such as including both a limit on the rate at which assessed property values may grow and the a limit on the property tax levy.

Property Taxation and TELs in Kansas

The above descriptions of tax and expenditure limits reveal some of the complexity of how these limits work. For example, state imposed limits on growth in property value assessments are a property tax limit to the same extent as limiting the tax rate than can be applied to those properties.  Property taxes include multiple moving parts, or multiple policy levers, the vast majority of which in most states are creations of and controlled within state constitutions and statutes. Below is a non-exhaustive list of the moving parts of the property tax revenue equation:

  1. The boundaries of taxing jurisdictions:  Taxing jurisdictions are government subdivisions within states, defined in state statutes and/or constitutions. They are creations of the state, even if granted home rule or limited home rule. Taxing jurisdictions may or may not be as simple as “cities and towns” or “municipalities.” In some states, municipal taxing jurisdictions are reasonably aligned with local school taxing jurisdictions, but in others like Kansas, they are not. The lack of contiguity between local public school district boundaries and municipal boundaries in Kansas is largely a result of school district consolidations that occurred under state statutes adopted in the 1960s, concurrent with (shortly before)  rewriting of the education article of the state constitution. In many states, the geographic spaces defined as taxing jurisdictions and enrollment areas for local public school districts continue to be redrawn, as in the case of the northeastern section of Kansas City Missouri School District which was recently annexed to Independence School District through a procedure created (specifically for that circumstance) under a recent Missouri statute.  Further, school district boundary determinations (under state laws) are often linked to a long history (including recent history) of institutionalized and state sanctioned racial discrimination in housing markets.[2] The defined geographic boundaries of a taxing jurisdiction determine the properties that are included in or excluded from that jurisdiction. Those boundaries ultimately determine the total values of property within the bounded space, and in turn the amount of revenue that can or cannot be generated by applying any given tax rate to those properties.


Figure 1

School District (green) Boundaries and Cities and Towns in the Kansas City Metro



  1. Definitions of Property Types: Different types of property exist within any taxing jurisdiction, including residential properties, residential properties owned by non-residents (second homes), commercial properties, industrial properties, utilities and farm properties. In Kansas and elsewhere property types are defined in the State Constitution (Article 11). The definition of property types influences substantially the application of “local” property taxes because each defined jurisdiction contains a different mix of property types – some with more commercial property than others – some more residential – and others more farm property. And the different values applied to different types of properties become a significant factor influencing the local revenue raising capacity of communities. Note that in Kansas, as elsewhere, the highest aggregate property values per child enrolled in school are not those in school districts with the highest valued houses, but are those in communities like Burlington, Moscow and Rolla which each include non-residential properties of significant value.
  2. Valuation Procedures: Procedures for determining the taxable value of properties are also defined in state statutes and constitutions, and in Kansas, in Article 11 of the constitution. Those valuation procedures operate as a form of tax and expenditure limitation. Residential properties are defined to have a taxable value of 11.5% of fair market value, agricultural land 30%, vacant lots 12%. States adopt such structures out of state policy interest in creating certain types of incentives or controls, including incentives to either preserve or develop farm property or vacant lots, or buffer commercial interest from escalating taxes. These differential assessment ratios are effectively limits to the revenue raising capacity from any applied tax rate.
  3. Property Tax Exemptions: States also control, typically via statute, the extent to which intermediate or local jurisdictions may grant exemptions to property taxes, including the duration over which an exemption may be granted or types of properties that may be granted exemptions. States also may impose exemptions such exempting from property taxes, a proportion of the value of residential properties owned by senior citizens, in the policy interest of protecting seniors on fixed income from escalating property taxes. As a tax equity measure, Kansas in the late 1990s adopted and exemption to the first $20,000 in taxable value of a residential property for property taxes applied to General Fund Revenues for schools (a statutory provision).
  4. Tax Rate Setting & Referendum Procedures: States also regulate the procedures by which local school district budgets are determined and/or tax rates are set. In some states with constitutional property tax limits which include override provisions, the referendum procedure for override is in the constitution, and may include a requirement of super-majority vote to achieve an override. Requirement of a super-majority is a limit. In other states, statutory provisions permit local authorities to raise taxes (or resulting revenues) to specific levels without voter approval and above those levels with voter approval. In some cases, those limits are absolute and cannot be exceeded.
  5. Debt Ratio Ceilings on Bonded Indebtedness: States also impose various limitations on the amount of debt “local” jurisdictions may accumulate toward the financing of capital projects. Kansas, like other states, imposes a limit – measured as a percentage of total taxable assessed valuation – on the amount of debt that can be accumulated through issuance of general obligation municipal bonds for the financing of new school construction or major renovations.

Each and every provision above and each and every element of the property tax system is controlled by and exists only as a function of state constitutional provisions and statutes. Further, each piece of the property tax puzzle imposes limitations – state controlled limitations – on the ability of state sanctioned local jurisdictions to raise revenues with property taxes.

Extreme Implications of a constitutional protection for complete, unregulated local citizen control over property taxation

Taken at its extreme, the assumption that local residents of any geographic space in the State of Kansas possess a Constitutional right to unlimited control over property taxation for “their” local public schools means that those local residents would have control over each and every parameter above, as each parameter above is a critical determinant of the revenue generated for local public schools by adoption of a specific property tax levy (a rate multiplier). Set any parameter – or multiplier to “0” – and the whole equation shuts down. No one piece is more important than another at determining the amount of money that can be raised for “local” public schools.

Taken at its extreme, any group of citizen residents of the state of Kansas should be able to organize themselves, and define a geographic area that they consider to be their taxing jurisdiction. They would then have the authority to define the types of properties in their jurisdiction and the method for determining the taxable value of those properties. Further, they would have the right to decide whether a mere majority or super majority vote is required in order to adopt any particular tax rate to apply to those properties.

If local citizens control only the single parameter of tax rate setting (the “mill levy”), the state could simply alter rules for adopting rate increases, such as requiring a super-majority vote. Or the state could adopt legislation which effectively reduces the taxable value of properties or exempts certain types of properties for raising additional school revenue above current local option budget limits. For example, the state could exempt all commercial and industrial properties from additional taxation (much like the 20% exemption on residential properties for General Funds Budgets). Such state controls, while not limiting the levies adopted, would limit the revenue that could be generated by those levies. Each of these rules only presently exists as a function of prior state, not local actions.

Assuming that there exists only a constitutional right to adopt higher tax levies, but those levies are to be adopted within an otherwise completely state controlled policy framework, is illogical. If such constitutional freedoms do exist, then they must apply to each and every relevant parameter limiting revenue.

Clearly, however, assuming that local groups of citizens have unlimited rights to determine each and every parameter in the property tax revenue generating equation is absurd, would moot numerous Kansas statues, Article 11 of the Kansas Constitution, and similar constitutional and statutory provisions across nearly every other state.

The state interest in regulating taxes imposed on non-resident property owners

As school district boundaries are presently organized, especially in the Kansas City metropolitan area, school districts each consist of many types of properties. Implicit in the assumption that there exists a constitutionally protected individual right to raise additional funds, through property taxation, for the education of one’s own children is that there exists an overly simplistic 1 to 1 to 1 ratio between children to be educated, the parents of those children and homeowner taxpayers of the jurisdiction. That is, each taxpayer homeowner is also a parent with interest in the quality of education provided to his or her child at the collective expense.  Such would be true if the group of parents organized to start a private school and used their private resources to finance the operations of that school to a level suitable to their own tastes.

This assumption crumbles when applied to local property taxation for public schools and when we consider the mix of property types, property owners and taxpayers that fall within any school taxing jurisdiction in Kansas. For example, owners of commercial and industrial properties within the jurisdiction may not be residents of the jurisdiction. Taxes paid by these individuals may be affected significantly by the decisions of a simple majority share of local residents of the district. The state has a legitimate interest in and may see fit to limit such impact. And one method for doing so is the maintenance of existing tax and expenditure limits.

It seems absurd to assume that a group of resident citizens of a jurisdiction have a constitutional right to unlimited taxation of someone else’s property without the option of state intervention.

The state interest in regulating taxes imposed on vulnerable minority voting blocks

Senior citizens who currently no-longer have children attending local public schools and are living on fixed income may be outnumbered at the polls in some jurisdictions when school budget (levy referenda) votes are held. Many states have policies exempting portions of the value of properties owned by senior citizens in order to provide some protection against escalating taxes. Those exemptions are a state imposed limit to property taxation.

As noted above, if we accept the assumption of a constitutional right for a group of local residents in a taxing jurisdiction to levy unlimited taxes on the rest of the jurisdiction, we must also accept that those same residents have control over each and every parameter in the property tax revenue generating equation that might limit their revenue raising capacity. A simple majority of residents could then negate exemptions. The state has a legitimate interest in protecting the rights of local minority voter populations, such as senior citizens, through such policy mechanisms as property tax exemptions.

The state interest in maintaining school funding fairness

Finally, the state also has an interest in the maintenance of equity in the provision of public education and in access to equal educational opportunity, and one mechanism the state has adopted in order to maintain equity is the limitation to supplemental local spending through property taxation.

Why is it problematic from an equal educational opportunity perspective for local public school districts to have unlimited ability to raise their property taxes and spend as they see fit on their local public schools? How, for example, does it harm the children of Kansas City, Kansas if the parents in Shawnee Mission or Blue Valley School Districts choose to substantially outspend Kansas City over the next several years and provide far higher quality local public schools?

Given the vast student  population differences across school districts in the Kansas City area and specifically between Kansas City and Shawnee Mission which are immediate neighbors, there exist very large differences in the actual cost of providing children with equal educational opportunity. Professor William Duncombe (Syracuse University), on behalf of the Kansas Legislative Division of Post Audit in 2006, estimated that if the cost of a specific quality of education for the state average district was set to 100 (100%), the cost of achieving equal opportunity for students in Kansas City would be about 35% higher than that average, and in Shawnee Mission would be about 12% lower than that average. Presently, the state school finance formula provides for much less difference in funding than would actually be needed to achieve more equal educational opportunity (See Table 1). In fact, when all state and local revenues are considered, Kansas is rated as having a regressive to flat state school finance system – one where higher poverty districts have systematically lower (or, at best, nearly comparable) resources per pupil than lower poverty districts – in a recent national report (as of 2007-08).[3]

The differences in cost of equal educational opportunity estimated by William Duncombe are a function of many factors, most notably vast differences in the backgrounds and needs of children attending local public school districts (See Table 2). More needy students require a wider array of services, including more specialized personnel, smaller class sizes and specific educational and support programs. The state has both an interest and a constitutional obligation to provide equal educational opportunity.

There are at least two major reasons why states have an interest in the maintenance of equity and equal educational opportunity across local public school districts.

First, education is a positional good. Access to economic opportunity, including access to higher education for children in Kansas City, Kansas depends not only on the absolute level of educational expenditure in their own public schools but on the relative quality of education they receive compared to that of other children competing for the same slots in local public and private colleges and universities.

Second is that the quality of schooling in any given location depends largely on the quality of teacher workforce that may be attracted to teach in any given location.  It is well understood that in any given labor market, working conditions – most notably student population characteristics – substantially influence teacher job choice, most often to the disadvantage of the neediest students. It would take not only equal, but significantly higher wages to recruit and retain teachers of comparable qualification to teach in Kansas City as it would to recruit and retain similar teachers to teach in Shawnee Mission, Blue Valley or Olathe. The competitive wage for teachers of specific qualifications in any given area are driven by the wages paid by each district’s nearest neighboring competitors and by the differences in working conditions across districts.

At present, teacher salaries in Kansas City, Kansas are already much lower than those in Shawnee Mission and other Johnson County districts (Table 3). They are lower partly because the state already allows Johnson County districts to levy a special “cost of living” tax (see Table 4) which falsely assumes that teachers in districts with more expensive houses are therefore more expensive to hire. Providing further opportunity for Johnson County districts to widen the salary gap, by removing state imposed tax limits, would likely lead to even greater disparities in teacher qualifications across wealthy and poor districts serving lower and higher need student populations in the Kansas City metropolitan area.

If Shawnee Mission and other Johnson County parents have the right to raise their property taxes in order to recruit and retain better teachers, don’t Kansas City parents have the same right? While they might have a similar right, they do not have similar capacity. Granting this right does not require that the state adopt any measures to equalize the capacity to compete.

For every additional mill on the local tax levy, Shawnee Mission can raise an additional $117 per pupil, whereas Kansas City can raise only $38, a greater than 3X difference (see Table 5). Even under present circumstances, with imposed limitations to the local option budget, Kansas City salaries lag behind Johnson County districts, and Johnson County districts have already been provided a local taxing opportunity to widen the gap, an option some have used.

TABLES AVAILABLE IN PDF VERSION: Fast Response Brief on Individual Liberty and Tax Limits


[1] Daniel R. Mullins and Bruce A. Wallin (2004) Tax and Expenditure Limitations: Introduction and Overview Public Budgeting and Finance (Winter) 2 – 15

[2] See Kevin Fox Gotham (2000) Urban Space, Restrictive Covenants and the Origins of Racial Residential Segregation in a U.S. City. 1900 to 1950. International Journal of Urban and Regional Research 24 (3) 616-633

The When, Whether & Who of Worthless Wonky Studies: School Finance Reform Edition

I’ve previously written about the growing number of rigorous peer reviewed and other studies which tend to show positive effects of state school finance reforms. But what about all of those accounts to the contrary? The accounts that seem so dominant in the policy conversations on the topic. What is that vast body of research that suggests that school finance reforms don’t matter? That it’s all money down the rat-hole. That in fact, judicial orders to increase funding for schools actually hurt children?

Beyond utterly absurd graphs and tables like Bill Gates’ “turn the curve upside down” graph, and Dropout Nation’s even more absurd graph, there have been a handful of recent studies and entire books dedicated to proving that court ordered school finance reforms simply have no positive effect on children. Some do appear in peer reviewed journals, despite egregious (and really obvious) methodological flaws. And yes, some really do go so far as to claim that court ordered school finance reforms “harm our children.”[1]

The premise that additional funding for schools often leveraged toward class size reduction, additional course offerings or increased teacher salaries, causes harm to children is, on its face, absurd. Further, no rigorous empirical study of which I am aware actually validates that increased funding for schools in general or targeted to specific populations has led to any substantive, measured reduction in student outcomes or other “harm.”

But questions regarding measurement and validation of positive effects versus non-effects are complex. That said, while designing good research analyses can be quite complex, the flaws of bad analyses are often absurdly simple. As simple as asking three questions: a) whether the reform in question actually happened? b) when it happened and for how long? and c) who was to be affected by the reform?

  • Whether: Many analyses argue to show that school funding reforms had no positive effects on outcomes, but fail to measure whether substantive school funding reforms were ever implemented or whether they were sustained. Studies of this type often simply look at student outcome data in the years following a school funding related ruling, creating crude classifications of who won or lost the ruling. Yet, the question at hand is not whether a ruling in-and-of-itself leads to changes in outcomes, but whether reforms implemented in response to a ruling do. One must, at the very least, measure whether reform actually happened!
  • When: Many analyses simply pick two end points, or a handful of points of student achievement to cast as a window, or envelop around a supposed occurrence of school finance reform or court order, often combining this strategy with the first (not ever measuring the reform itself). For example, one might take NAEP scores from 1992 and 2007 on a handful of states, and indicate that sometime in that window, each state implemented a reform or had a court order. Then one might compare the changes in outcomes from 1992 to 2007 for those states to other states that supposedly did not implement reforms or have court orders. This, of course provides no guarantee that states from the non-reform group (a non-controlled control group?) didn’t actually do something more substantive than the reform group. But, that aside, the casting of a large time window and the same time window across states ignores the fact that reforms may come and go within that window, or may be sufficiently scaled up only during the latter portion of the window. It makes little sense, for example to evaluate the effects of New Jersey’s school finance reforms which experienced their most significant scaling up between 1998 and 2003, by also including 6 years prior to any scaling up of reform. Similarly, some states which may have aggressively implemented reforms at the beginning of the window may have seen those reforms fade within the first few years. When matters!
  • Who: Many analyses also address imprecisely the questions of “who” is expected to benefit from the reforms. Back to the “whether” question, if there was no reform, then the answer to this question is no-one. No-one is expected to benefit from a reform that didn’t ever happen. Further, no-one is expected to benefit today from a reform that may happen tomorrow, nor is it likely that individuals will benefit twenty years from now from a reform that is implemented this year, and gone within the next three years. Beyond these concerns, it is also relevant to consider whether the school finance reform in question, if and when it did happen, benefited specific school districts or specific children. Reforms that benefit poorly funded school districts may not also uniformly benefit low income children who may be distributed, albeit unevenly, across well-funded and poorly-funded districts. Not all achievement data are organized for appropriate alignment with funding reform data. And if they are not, we cannot know if we are measuring the outcomes of who we would actually expect to benefit.

In 2011, Kevin G. Welner of the University of Colorado and I published an extensive review of the good, the bad and the ugly of research on the effectiveness of state school finance reforms.[2] In our article we identify several specific examples of empirical studies claiming to find (not just “find” but prove outright) that school funding reforms and judicial orders simply don’t matter. That is, they don’t have any positive effects on measured student outcomes. But, as noted above, many of those studies suffer from basic flaws of logic in their research design, which center on questions of whether, when and who.

As one example of a whether problem, consider an article published by Greene and Trivett (2008). Greene and Trivitt claim to have found “no evidence that court ordered school spending improves student achievement” (p. 224).  The problem is that the authors never actually measured “spending” and instead only measured whether there had been a court order. Kevin Welner and I explain:

The Greene and Trivitt article, published in a special issue of the Peabody Journal of Education, proclaimed that the authors had empirically estimated “the effect of judicial intervention on student achievement using standardized test scores and graduation rates in 48 states from 1992 to 2005” and had found “no evidence that court ordered school spending improves student achievement” (p. 224, emphasis added). The authors claim to have tested for a direct link between judicial orders regarding state school funding systems and any changes in the level or distribution of student outcomes that are statistically associated with those orders. That is, the authors asked whether a declaration of unconstitutionality (nominally on either equity or adequacy grounds) alone is sufficient to induce change in student outcomes. The study simply offers a rough indication of whether the court order itself, not “court-ordered school spending,” affects outcomes. It certainly includes no direct test of the effects of any spending reforms that might have been implemented in response to one or more of the court orders.

Kevin Welner and I also raise questions regarding “who” would have benefited from specific reforms and “when” specific reforms were implemented and/or faded out. In our article, much of our attention regarding who and when questions focused on Chapter 6, The Effectiveness of Judicial Remedies of Eric Hanushek and Alfred Lindseth’s book Courting Failure.[3] A downloadable version of the same graphs and arguments can be found here: http://edpro.stanford.edu/Hanushek/admin/pages/files/uploads/06_EduO_Hanushek_g.pdf.  Specifically, Hanushek and Lindseth identify four states, Kentucky, Massachusetts, New Jersey and Wyoming as states which have by order of their court systems, (supposedly) infused large sums of money into school finance reforms over the past 20 years. Given this simple classification, Hanushek and Lindseth take the National Assessment (NAEP) Scores for these states, including scores for low income children, and racial subgroups, and plot those scores against national averages from 1992 to 2007.

No statistical tests are performed, but graphs are presented to illustrate that there would appear to be no difference in growth of scores in these states relative to national averages. Of course, there is also no measure of whether and how funding changed in these states compared to others. Additionally, there is no consideration of the fact that in Wyoming, for example, per pupil spending increased largely as a function of enrollment decline and less as a function of infused resources (the denominator shrunk more than the numerator grew).

Setting these other major concerns aside, which alone undermine entirely the thesis of Hanushek and Lindseth’s chapter, Kevin Welner and I explain the problem of using a wide time window to evaluate school finance reforms which may ebb and flow throughout that window:

As noted earlier, the appropriate outcome measure also depends on identifying the appropriate time frame for linking reforms to outcomes. For example, a researcher would be careless if he or she merely analyzed average gains for a group of states that implemented reforms over an arbitrary set of years. If a state included in a study looking at years 1992 and 2007 had implemented its most substantial reforms from 1998 to 2003, the overall average gains would be watered down by the six pre-reform years – even assuming that the reforms had immediate effects (showing up in 1998, in this example). And, as noted earlier, such an “open window” approach may be particularly problematic for evaluating litigation-induced reforms, given the inequitable and inadequate pre-reform conditions that likely led to the litigation and judicial decree.

There also exist logical, identifiable, time-lagged effects for specific reforms. For example, the post-1998 reforms in New Jersey included implementation of universal pre-school in plaintiff districts. Assuming the first relatively large cohorts of preschoolers passed through in the first few years of those reforms, a researcher could not expect to see resulting differences in 3rd or 4th grade assessment scores until four to five years later.

Further, as noted previously, simply disaggregating NAEP scores by race or low income status does not guarantee by any stretch that one has identified the population expected to benefit from specific reforms. That is, race and poverty subgroups in the NAEP sample are woefully imprecise proxies for students attending districts most likely to have received additional resources. Kevin Welner and I explain:

This need to disaggregate outcomes according to distributional effects of school funding reforms deserves particular emphasis since it severely limits the use of the National Assessment of Educational Progress – the approach used in the recent book by Hanushek and Lindseth. The limitation arises as a result of the matrix sampling design used for NAEP. While accurate when aggregated for all students across states or even large districts, NAEP scores can only be disaggregated by a constrained set of student characteristics, and those characteristics may not be well-aligned to the district-level distribution of the students of interest in a given study.

Consider, for example, New Jersey – one of the four states analyzed in the recent book. It might initially seem logical to use NAEP scores to evaluate the effectiveness of New Jersey’s Abbott litigation, to examine the average performance trends of economically disadvantaged children. However, only about half (54%) of New Jersey children who receive free or reduced-price lunch – a cutoff set at 185% of the poverty threshold – attend the Abbott districts. The other half do not, meaning that they were not direct beneficiaries of the Abbott remedies. While effects of the Abbott reforms might, and likely should, be seen for economically disadvantaged children given that sizeable shares are served in Abbott districts, the limited overlap between economic disadvantage and Abbott districts makes NAEP an exceptionally crude measurement instrument for the effects of the court-ordered reform.16

Hanushek and Lindseth are not alone in making bold assertions based on insufficient analyses, though Chapter 6 of their recent book goes to new lengths in this regard. Kevin Welner and I address numerous comparably problematic studies with more subtle whether, who and when problems, including the Greene and Trivitt study noted above.  Another example is a study by Florence Neymotin of Kansas State University, which purports to find that the substantial infusion of funding into Kansas school districts which supposedly occurred between 1997 and 2006 as a function of the Montoy rulings never led to substantive changes in student outcomes. I blogged about this study when it was first reported. But, the most relevant court orders in Montoy did not come until January of 2005, June of 2005 and eventually July of 2006. Remedy legislation may be argued to have begun as early as 2005-06, but primarily from 2006-07 on, before its dismantling from 2008 on. Regarding the Neymotin study, Kevin Welner and I explain:

A comparable weakness undermines a 2009 report written by a Kansas State University economics professor, which contends that judicially mandated school finance reform in Kansas failed to improve student outcomes from 1997 to 2006 (Neymotin, 2009).13 This report was particularly egregious in that it did not acknowledge that the key judicial mandate was issued in 2005 and thus had little or no effect on the level or distribution of resources across Kansas schools until 2007-08. In fact, funding for Kansas schools had fallen behind and become less equitable from 1997 through 2005.14 Consequently, an article purporting to measure the effects of a mandate for increased and more equitable spending was actually, in a very real way, measuring the opposite.[4]

Kevin Welner and I also review several studies applying more rigorous and appropriate methods for evaluating the influence of state school finance reforms. I have discussed those studies previously here. On balance, it is safe to say that a significant body of rigorous empirical literature, conscious of whether, who and when concerns, validates that state school finance reforms can have substantive positive effects on student outcomes including reduction of outcome disparities or increased overall outcome level.

Further, it is even safer to say that analyses provided in sources like the book chapter by Hanushek and Lindseth (2009), or research articles by Neymotin (2009), Greene and Trivett, provide no credible evidence to the contrary, due to significant methodological omissions. Finally, even the boldest, most negative publications regarding state school finance reforms provide no support for the contention that school finance reforms actually “harm our children,” as indicated in the title of a 2006 volume by Eric Hanushek.

Sometimes, even when a research report or article seems really complicated, relatively simple questions like when, whether and who allow the less geeky reader to quickly evaluate and possibly debunk the study entirely.  Sometimes, the errors of reasoning regarding when, whether and who, are so absurd that it’s hard to believe that anyone would actually present such an absurd analysis. But these days, I’m rarely shocked. My personal favorite “when” error remains the Reason Foundation’s claim that numerous current reforms positively affected past results! http://nepc.colorado.edu/bunkum/2010/time-machine-award. It just never ends!

Further reading:

B. Baker, K.G. Welner (2011) Do School Finance Reforms Matter and How Can We Tell. Teachers College Record. http://www.tcrecord.org/content.asp?contentid=16106

Card, D., and Payne, A. A. (2002). School Finance Reform, the Distribution of School Spending, and the Distribution of Student Test Scores. Journal of Public Economics, 83(1), 49-82.

Roy, J. (2003). Impact of School Finance Reform on Resource Equalization and Academic Performance: Evidence from Michigan. Princeton University, Education Research Section Working Paper No. 8. Retrieved October 23, 2009 from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=630121(Forthcoming in Education Finance and Policy.)

Papke, L. (2005). The effects of spending on test pass rates: evidence from Michigan. Journal of Public Economics, 89(5-6). 821-839.

Downes, T. A., Zabel, J., and Ansel, D. (2009). Incomplete Grade: Massachusetts Education Reform at 15. Boston, MA. MassINC.

Guryan, J. (2003). Does Money Matter? Estimates from Education Finance Reform in Massachusetts. Working Paper No. 8269. Cambridge, MA: National Bureau of Economic Research.

Deke, J. (2003). A study of the impact of public school spending on postsecondary educational attainment using statewide school district refinancing in Kansas, Economics of Education Review, 22(3), 275-284.

Downes, T. A. (2004). School Finance Reform and School Quality: Lessons from Vermont. In Yinger, J. (ed), Helping Children Left Behind: State Aid and the Pursuit of Educational Equity. Cambridge, MA: MIT Press.

Resch, A. M. (2008). Three Essays on Resources in Education (dissertation). Ann Arbor: University of Michigan, Department of Economics. Retrieved October 28, 2009, from http://deepblue.lib.umich.edu/bitstream/2027.42/61592/1/aresch_1.pdf

Goertz, M., and Weiss, M. (2009). Assessing Success in School Finance Litigation: The Case of New Jersey. New York City: The Campaign for Educational Equity, Teachers College, Columbia University.


[1] See, for example: E.A. Hanushek (2006) Courting Failure: How School Finance Lawsuits Exploit Judges’ Good Intentions and Harm Our Children. Hoover Institution Press.  Reviewed here: http://www.tcrecord.org/Content.asp?ContentId=13382

[2] Baker, B.D., Welner, K. (2011) School Finance and Courts: Does Reform Matter, and How Can We Tell? Teachers College Record 113 (11) p. –

[3] Hanushek, E. A., and Lindseth, A. (2009). Schoolhouses, Courthouses and Statehouses. Princeton, N.J.: Princeton University Press.

[4] B. Baker, K.G. Welner (2011) Do School Finance Reforms Matter and How Can We Tell. Teachers College Record. http://www.tcrecord.org/content.asp?contentid=16106

Who would really want to spend more than that? (Ed Next & Spending Preferences)

When Paul Peterson asks “Do we really need to spend more on schools?” we already know what he thinks the answer is – an unequivocal NO!  Knowing the answer you desire always makes it easier to frame the questions, and like previous years, this year’s Education Next survey of attitudes toward public education provides few surprises.

Before I even gained full access to Peterson’s most recent WSJ Op-ed (e-mailed to me by a family member), I was able to guess pretty much where he was going with it.  Here’s how Peterson explains the Ed Next public opinion survey findings:

At first glance, the public seems to agree with this position. In a survey released this week by Education Next, an education research journal, my colleagues and I reported that 65% of the public wants to spend more on our schools. The remaining 35% think spending should either be cut or remain at current levels. That’s the kind of polling data that the president’s political advisers undoubtedly rely upon when they decide to appeal for more education spending.

Yet the political reality is more complex than those numbers suggest. When the people we surveyed were told how much is actually spent in our schools—$12,922 per student annually, according to the most recent government report—then only 49% said they want to pony up more dollars. We discovered this by randomly splitting our sample in half, asking one half the spending question cold turkey, while giving the other half accurate information about current expenditure.

Later in the same survey, we rephrased the question to bring out the fact that more spending means higher taxes. Specifically, we asked: “Do you think that taxes to fund public schools around the nation should increase, decrease or stay about the same?” When asked about spending in this way, which addresses the tax issue frankly, we found that only 35% support an increase. Sixty-five percent oppose the idea, saying instead that spending should either decrease or stay about the same. The majority also doesn’t want to pay more taxes to support their local schools. Only 28% think that’s a good idea.

So there is the nation’s debt crisis in a nutshell. If people aren’t told that nearly $13,000 is currently being spent per pupil, or if they aren’t reminded that there is no such thing as a free lunch, they can be persuaded to think schools should be spending still more.

In other words… yeah… the ignorant general public thinks they want to spend more on schools, but only because they don’t realize how much we are already wasting on public schools! When we clue them into the egregious… no… outrageous… exorbitant spending already going on … and hold a gun to their head… and phrase our question just right… pointing out to them just how stupid we think they are… and how smart we are… then the fix their answer… and become much, much more reasonable!

This explanation is problematic at a number of levels.  First, let’s explore the basic model of local voter preferences for spending on local public schools – specifically the information on price and quality that informs those preferences. First, local public school revenue comes from two primary sources – local property taxes paid on various types of properties within school districts and state general funds derived largely from state sales and income taxes. The mix varies widely from state to state. Residential property owners frequently pay their property taxes embedded in monthly mortgage payments and renters pay their landlords’ property taxes embedded in rent prices. Homeowners and renters have at least some feel for the reasonableness of their aggregate monthly housing payments, and some feel for the quality of public services they receive (schools, fire, police, parks, etc.) for the aggregate price they pay. They also have some feel for a) whether they would like those services improved and b) whether they are willing to pay a bit more to support those improvements. In short, a typical taxpayer/survey respondent has a reasonable gut feel regarding their “tax price” paid for the quality of public service provided.

The local taxpayer/voter/survey respondent sufficiently involved with local public schools (having children in the schools, working in the schools, having children who are recent graduates of the schools, or having recently graduated themselves) probably has some indicators of schooling quality in his/her head that guide his/her preference to pay more (or less). Has class size risen, or does it just seem too large? Has the district cut visible programs like music, arts or athletics of late, or has the district increased fees to cover the costs of these programs? As a result, the respondent is at least somewhat able to piece together whether they wish to spend a little more to decrease class sizes, expand programs or reinstate programs previously cut.

But, the typical taxpayer/voter/survey respondent likely a) doesn’t give a damn about and b) is generally unable to contextualize the meaning of the Total per Pupil Expenditures for a local public school district. It’s an abstract concept. A number that relates in a meaningful sense only to those who really spend their days steeped in such numbers. A number most likely to do little more than bias a response in this case, and it seems to, though it is hard to know precisely why.

Even worse is when those numbers are used totally out of context, as in Peterson’s argument above. Peterson’s description above is actually even worse than the methods description provided at Ed Next (Interestingly, Peterson also adds over $600 per pupil to the average spending figure, and then rounds it up to $13,000 by the end of his op-ed, compared to the information in the paragraph below from Ed Next):

A segment of those surveyed were asked the same ques­tion except that they were first told the level of per-pupil expenditure in their community, which averaged $12,300 for the respondents in our sample. For every subgroup con­sidered, this single piece of information dampened public enthusiasm for increased spending. Support for more spend­ing fell from 59 percent to 46 percent of those surveyed. Among the well-to-do, the level of support dropped dramati­cally, from 52 percent to 36 percent. Among teachers, sup­port for expenditure increases fell even more sharply—from 71 percent to 53 percent (see Figure 7).

Surely, it would be completely absurd to ask (as implied by Peterson’s op ed) the average person in Tennessee if their schools should spend more, after telling that person what the average district spends nationally – implying to the respondent that the figure represents Tennessee spending (as seemingly implied by Peterson’s Op-Ed, and as in the online survey at Ed Next).  It is only marginally more useful, however to ask the average respondent in Tennessee whether they should spend more or less, given a completely out of context representation of their local spending per pupil.

Here’s how the 2008-09 actual national mean per pupil spending compares to the distribution of per pupil spending across Tennessee districts:

(national mean current spending per pupil in 2008-09 was $10,209.83 [w/outliers excluded])

Now, it might be interesting to show the average voter respondent in Tennessee this graph and then ask him/her whether they think more should be spent in Tennessee? This graph provides some context. Context that is completely absent when informing a Tennessee respondent either of their own local district spending WITH NO OTHER CONTEXT AVAILABLE or of the national spending WITH NO OTHER CONTEXT AVAILABLE.

Put very simply, a per pupil spending figure out of context is meaningless.  $17,000 I say! $17,000… an abomination I say. It’s  a huge number! Why would we ever consider spending more than that per pupil in New York City? Well, what if it just happened to turn out that in the same year, that $17,000 per pupil was lower, on average, than most of the surrounding districts with much less needy student populations? What if that $17,000 was only approximately 50% of what was being spent in private independent schools operating within the city?  It doesn’t sound so big any more does it?  How would survey respondents in New York City change their answer if this information was provided?

The Ed Next survey, while fun to ponder each year, isn’t particularly helpful for really understanding voter’s preferences or awareness regarding spending on public schools or perceived quality.

Actual data on local budget votes, including those involving tax increases (increasing the more voter-distasteful local property tax) tend to be a much more useful barometer and even in the worst of economic times, local voter support – especially where voters have the financial capacity to provide that support – remains overwhelmingly positive  (Example NY State Data & previous NJ Blog Post [over 70% pass rate in wealthy districts in worst year]).  Matt Di    Carlo provides further discussion of this topic here, explaining the general voter preferences. It is also worth noting that even the most poorly constructed and phrased polls do not find significant shares (if any) responding that less should be spent.  Yet that is precisely the argument advanced by many pundits in response to these surveys.

Inexcusable Inequalities! This is NOT the post funding equity era!

I’ve heard it over and over again from reformy pundits. Funding equity? Been there done that. It doesn’t make a damn bit of difference. It’s all about teacher quality! (which of course has little or nothing to do with funding equity?).  The bottom line is that equitable and adequate financing of schools is a NECESSARY UNDERLYING CONDITION FOR EVERYTHING ELSE!

I’m sick of hearing, from pundits who’ve never run a number themselves and have merely passed along copies of the meaningless NCES Table showing national average spending in high poverty districts slightly greater than that for lower poverty ones. 

I’m sick of the various iterations of the “we’ve tripled spending and gotten nothing for it” argument and accompanying bogus graphs.  And further, the implication put forward by pundits that these graphs and table taken together mean that we’ve put our effort into the finance side for kids in low-income schools, but it’s their damn lazy overpaid teachers who just aren’t cutting it.

I’m intrigued by those pundits who would point out that perhaps outcomes of low-income children have improved over the past few decades and that the improvement is entirely attributable to increased accountability measures (when the same pundits have argued previously that the massive increases in funding led to no improvement. Perhaps there has been improvement, and perhaps there has been some increase in funding on average… and perhaps that’s the connection? More insights on achievement gap closure and shifting resources here!).

I’m also sick of those who would so absurdly argue that districts serving low-income and minority children really have more than enough money to deliver good programs, but they’ve squandered it all on useless stuff like cheerleading and ceramics.

Anyway, the goal of this post is  to point out some of the inexcusable inequalities that persist in K-12 education, inequalities that have real consequences for kids. Let’s take a look, for example, at two states that have persistently large achievement gaps between low-income and non-low income students – Illinois and Connecticut. These two states have somewhat different patterns of overall funding disparity, but suffice it to say, both states have their winners and losers, and the differences between them are ugly and unacceptable.

Let’s start with Connecticut. Below is a graph of Connecticut school district “need and cost adjusted current spending per pupil” and standardized test outcomes on the Connecticut Mastery Test (CMT). Expenditures are adjusted for differences on labor market competitive wages and for shares of children qualifying for free or reduced price lunch and for children with limited English language proficiency (based on estimates reported here). I’ve used essentially the same methods I discussed in this previous post.

What we see here is that resources – after adjustment for needs and costs – vary widely. Heck, they vary quite substantially even without these adjustments! What we also see is that we’ve got some really high flyers, like Weston, New Canaan and Westport, and we’ve got some that, well, are a bit behind in both equitable resources and outcomes (Bridgeport and New Britain in particular). To be blunt, THIS MATTERS!  Yeah.. okay, reformy pundits are saying, but they really have enough anyway. Why put anything else into those rat-holes.

Let’s break it down a bit further. Here are the characteristics of a few of the most advantaged and most disadvantaged districts in the above figure.

But of course, all we need to do is reshuffle the deck chairs  in Bridgeport and New Britain – fire their bottom 5% – heck let’s go for 20% teachers – pay the new ones based on test scores… and all will be fixed! Those deficits in average salaries might be a bit problematic. And even the nominal (no adjustments) spending figures fall well short of their advantaged neighbors. But bring on those reformy fixes, and throw in some funding cuts while you’re at it!

I’m sure… absolutely sure that the only reason those salaries are low is because they’ve wasted too much money on administrators and reducing class size… which we all know doesn’t accomplish anything???? But wait, here are the elementary class sizes?

Well, there goes that ridiculous reformy assumption. Class sizes are actually larger in these higher need districts! and Salaries lower. Damn cheerleading costs! Killing us! Perhaps it’s even going into  junk like band and art which are obviously a waste of time and money on these kids!

Well, here are the staffing structures of the schools, with staffing positions reported per 100 pupils.

Hmmm… disadvantaged districts have far fewer total positions per child, and if we click and blow up the graph, we can see some striking discrepancies! Those high need districts have far more special education and bilingual education teachers (squeezing out other options, from their smaller pot!). Those high need districts have only about half the access to teachers in physical education assignments or art, much less access to Band (little or none to Orchestra), and significantly less access to math teachers!

But, okay… this Connecticut thing is a freakin’ anomaly, right?  These kind of disparities – savage inequalities – are surely a thing of the past. This is, after all, THE POST-FUNDING EQUITY ERA? Been there and done that!

Let’s do the same walk through for a few Illinois districts. First, here are the graphs of need and cost adjusted (based on a cost model used in my previous post and related working paper) operating expenditures and outcomes –

For unified K-12 districts


For High School districts

Here are the basic stats on these districts

In this case, imagine trying to recruit and retain teachers of comparable quality in JS Morton to those in New Trier at $20k less on average, or in Aurora East compared to Barrington, at nearly $20k less. Ahh…you say… Baker… you’re making way too much of the funding issue. First, we know their wasting it all on small class size and cheerleading. Second, Baker… you’re missing the point that if we fire the bad teachers and pay the good teachers based on student test scores, those New Trier teachers will be banging down the door to get into J S Morton! That’s real reform dammit! And we know it works (even though we don’t have an ounce of freakin’ evidence to that effect!).

Clearly, if schools in Aurora East and JS Morton are slated for closure under NCLB (I’ve not checked this actually), it’s not because of poverty. It’s not for lack of resources… Clearly it’s their lazy, overpaid teachers who refuse to pull all-nighters with their kids to beat those odds????? To get those kids into calculus and trig classes presently filled with empty seats (and their own overpaid under-worked teachers!)

So, here’s what the staffing ratios look like.

First, those advantaged districts just have a lot more teacher assignments (position assignments) than the disadvantaged ones. And they especially have far more assignments in advanced math, advanced science, Library/Media, Art and music. There’s not a whole lot of squandering on extras going on in JS Morton and Aurora East. Like CT though, the disadvantaged districts do have bilingual education and special education teachers!  The staffing disparities are baffling – Savage in fact!

In fact, I must be making this stuff up right. After all, THIS IS THE POST-FUNDING DISPARITY ERA? This kind of stuff is just pulled from the chapters of an old Kozol book!  Teachers matter. Not funding. We all know that (except perhaps the various researchers who’ve actually explored the relationship between school funding reforms and student outcomes, only to find that it does matter).

Clearly, this matters. These funding disparities are substantial. And while these examples are selected from the extremes of the distributions, these districts have plenty of company at the extremes, and these districts fall along a clearly patterned continuum. And, with enough data and enough space, I could keep going and going here. CT and IL are not unique – though IL is clearly among the worst in the nation. New York anyone?

Utica is quite possibly one of the most financially screwed local public school districts in the nation (Poughkeepsie isn’t far behind)!

Arguably, there are entire states – like Tennessee and Arizona that are approaching (if they’ve not already surpassed) the conditions of districts like Utica, JS Morton, Bridgeport or New Britain.

Until we take these disparities seriously and stop counting on miracles and superman to give us a free ride, we’re not likely to make real progress on the “Scarsdale-Harlem” achievement gap.

Treating teachers like crap, cutting state funding, basing teacher salaries on student test scores will do nothing to correct these disparities, and will likely only make them worse. Nor can we expect to close the gap by simply replacing the current underfunded schools with comparably underfunded schools under new management (or simply paying parents of kids in these districts a discount rate to just go somewhere else, and never follow up on the kids). This reformy goo is a dangerous distraction from the real issues!

THIS IS NOT THE POST FUNDING EQUITY ERA.

FUNDING MATTERS.

GOOD EDUCATION IS EXPENSIVE & WORTH IT!

EQUITABLE AND ADEQUATE FUNDING IS A NECESSARY UNDERLYING CONDITION FOR THE FUTURE SUCCESS OF AMERICAN PUBLIC EDUCATION.


Teacher Selection: Smart Selection vs. Dumb Selection

I had a twitter argument the other day about a blog posting that compared the current debate around “de-selection” of bad teachers to eugenics. It is perhaps a bit harsh to compare Hanushek  (cited author of papers on de-selecting bad teachers) to Hitler, if that was indeed the intent. However, I did not take that as the intent of the posting by Cedar Riener.  Offensive or not, I felt that the blog posting made 3 key points about errors of reasoning that apply to both eugenecists and to those promoting empirical de-selection of fixed shares of the teacher workforce.  Here’s a quick summary of those three points:

  • The first error is a deterministic view of a complex and uncertain process.
  • The second common error becomes apparent once the need arises to concretely measure quality.
  • The third error is a belief that important traits are fixed rather than changeable.

These are critically important, and help us to delineate between smart selection and, well, dumb selection.  These three errors of reasoning are the basis for dumb selection.  A selection that is, as the author explains, destined to fail.  But, I do not see this particular condemnation of dumb selection to be a condemnation of selection more generally. By contrast, the reformy pundit with whom I was arguing continued to claim that Riener’s blog was condemning any and all forms of selection as doomed to fail, a seemingly absurd proposition (and not how I read it at all).

Clearly, selection can and should play a positive role in the formation of the teacher workforce or in the formation of that team of school personnel that can make a school great.

Smart Selection: In nearly every human endeavor, in every and any workforce or labor market activity exists some form of selection. Selection of individuals into specific careers, jobs and roles and de-selection of individuals out of careers, jobs and roles. Selection in and of itself is clearly not a bad thing. In fact, the best of organizations necessarily select the best available individuals over time to work within those organizations. And, individuals attempt to select the best organizations, careers, jobs and roles to suit their interests, motivation and needs. That is, self-selection. Teacher selection or any education system employee selection is no different. And good teacher selection is obviously important for having good schools. Like any selection process on the labor market, teacher selection involves a two-sided match. On the one hand, there are the school leaders and existing employees (to the extent they play a role in recruitment and selection) who may play a role in determining among a pool of applicants which ones are the best fit for their school and the specific job in question. On the other hand, there are the signals sent out by the school (some within and some outside the control of existing staff and leaders) which influence the composition of the applicant pool and for that matter, whether an individual who is selected decides to stay. These include signals about compensation, job characteristics and work environment. Managing this complex system well is key to having a great school. Sending the right signals. Creating the right environment. Making the right choices among applicants. Knowing when a choice was wrong. And handling difficult decisions with integrity.

There has also been much discussion of late about a recent publication by Brian Jacob of the University of Michigan, who found that when given the opportunity to play a strong role in selecting which probationary teachers should continue in their schools, principals generally selected teachers who later proved to generate good statistical outcomes (test scores). Note that this approach to declaring successful decision making suffers the circular logic I’ve frequently bemoaned on this blog. But, at the very least, Jacob’s findings suggest that decisions made by individuals – human beings considering multiple factors – are not counterproductive when measured against our current batch of narrow and noisy metrics. Specifically, Jacob found:

Principals are more likely to dismiss teachers who are frequently absent and who have previously received poor evaluations. They dismiss elementary school teachers who are less effective in raising student achievement. Principals are also less likely to dismiss teachers who attended competitive undergraduate colleges. It is interesting to note that dismissed teachers who were subsequently hired by a different school are much more likely than other first-year teachers in their new school to be dismissed again.

That to me seems like good selection. And it seems that principals are doing it reasonably well when given the chance. And this is why I also support using principals as the key leverage point in the process (with the caveat that principal quality itself is very unequally distributed, and must be improved).

Dumb “Selection:” Dumb selection on the other hand – the kind of selection that is destined to fail if applied en masse in public schooling or any endeavor suffers the three major flaws of reasoning addressed by Cedar Riener in his blog post.  Now, you say to yourself, but who is really promoting dumb selection and what more specifically are the elements of dumb selection when it comes to the teacher workforce? Here are the elements:

  1. Heavy (especially a defined fixed, large share) weight in making teacher evaluation, compensation or dismissal decisions placed on Value-Added metrics, which can be corrupted, may suffer severe statistical bias, and are highly noisy and error prone.
  2. Explicit, prior specification of the exact share of teachers who should be de-selected in any given year, or year after year over time OR prior specification of exact scores or ratings (categories) derived from those scores requiring action to be taken – including de-selection.

Sadly, several states have already adopted into policy the first of these dumb selection concepts – the mandate of a fixed weight to be place on problematic measures.  See this post by Matt Di Carlo at ShankerBlog for more on this topic.

Thus far, I do not know of states or districts that have, for example, required that 5% of the bottom scoring teachers in any given year be de-selected. But, states and districts have established categorical rating systems for teachers from high to low rating groups, based arbitrary cut points applied to these noisy measures, and have required that dismissal, intervention and compensation decisions be based on where teachers fall in the fixed, arbitrary classification scheme in a given year, or sequence of three years.

To some extent, the notion of de-selecting fixed shares of the teacher workforce based on noisy metrics comes from economists simulations based on convenience of measures than on active policy conversations. But in the past year, the lines between these simulations and reality have become blurred as policy conversations have indeed drifted toward actually using fixed values based on noisy achievement measures in place of seniority as a blunt tool to deselect teachers during times of budget cuts.  If and when these simplified social science thought exercises are applied as public policy involving teachers, they do reek of the disturbingly technocratic, “value-neutral” mindset pervasive in eugenics as well.

One other recent paper that’s gotten attention, applies this technocratic (my preference over eugenic) approach to determine whether using performance measures instead of seniority would result a) in different patterns of layoffs and b) in different average “effectiveness” scores (again, that circular logic rears its ugly head) Now, of course, if you lay off based on effectiveness scores rather than seniority, the average effectiveness scores of those left should be higher. The deck is stacked in this reformy analysis. But, even then, the authors find very small differences, largely because a) seniority based layoffs seem to be affecting mainly first and second year teachers, and b) effectiveness scores tend to be lower for first and second year teachers. Overall, the authors find:

We next examine our value-added measures of teacher effectiveness and find that teachers who received layoff notices were about 5 percent of a standard deviation less effective on average than the average teacher who did not receive a notice. This result is not surprising given that teachers who received layoff notices included many first and second-year teachers, and numerous studies show that, on average, effectiveness improves substantially over a teacher’s first few years of teaching.

Perhaps most importantly, these thought experiments, not ready for policy implementation prime time (nor will they ever be?) necessarily ignore the full complexity of the system to which they are applied, and as Riener noted, assume that individual’s traits are fixed – how you are rated by the statistical model today is assumed to correct (despite a huge chance it’s not) and assumed to be sufficient for classifying your usefulness as an employee, now and forever (be it a 1 or 3 year snapshot). In that sense, Riener’s comparison, while offensive to some, was right on target.

To summarize: Smart selection good. Dumb selection bad. Most importantly, selection itself is neither good nor bad. It all depends on how it’s done.

More Flunkin’ out from Flunkout Nation (and junk graph of the week!)

Earlier today I stumbled across this brilliant post by RiShawn Biddle over at Dropout Nation.

Biddle boldly claims:

Despite the arguments (and the pretty charts) of such defenders as Rutgers’ Bruce Baker, there is no evidence that spending more on American public education will lead to better results for children.

Now, regarding the “no evidence” claim, I would recommend reading this article from Teachers College Record, this year, which summarizes a multitude of rigorous empirical studies of state school finance reforms finding generally that increased funding levels have been associated with improved outcomes and that more equitable distributions of resources have been associated with more equitable distributions of outcomes.

In fact, even the Spring 2011 issue of the journal Education Finance and Policy includes an article by Joydeep Roy supporting the positive results of state school finance reforms (using Michigan data).

Proposal A was quite successful in reducing interdistrict spending disparities. There was also a significant positive effect on student performance in the lowest-spending districts as measured in state tests.(from abstract)

As Kevin Welner and I point out in our article, this study is not unique in its findings. Here are a few others:

Card & Payne (2002)

Using micro samples of SAT scores from this same period, we then test whether changes in spending inequality affect the gap in achievement between different family background groups. We find evidence that equalization of spending leads to a narrowing of test score outcomes across family background groups. (p. 49)

Deke (2003)

Using panel models that, if biased, are likely biased downward, I have a conservative estimate of the impact of a 20% increase in spending on the probability of going on to postsecondary education. The regression results show that such a spending increase raises that probability by approximately 5% (p. 275).

Papke (2001)

Focusing on pass rates for fourth-grade and seventh grade math tests (the most complete and consistent data available for Michigan), I find that increases in spending have nontrivial, statistically significant effects on math test pass rates, and the effects are largest for schools with initially poor performance. (Papke, 2001, p. 821.)

Downes (2004) on VT

All of the evidence cited in this paper supports the conclusion that Act 60 has dramatically reduced dispersion in education spending and has done this by weakening the link between spending and property wealth. Further, the regressions presented in this paper offer some evidence that student performance has become more equal in the post–Act 60 period. And no results support the conclusion that Act 60 has contributed to increased dispersion in performance. (p. 312)

Downes, Zabel & Ansel (2009) on Mass

The achievement gap notwithstanding, this research provides new evidence that the state’s investment has had a clear and significant impact. Specifically, some of the research findings show how education reform has been successful in raising the achievement of students in the previously low-spending districts. Quite simply, this comprehensive analysis documents that without Ed Reform the achievement gap would be larger than it is today. (p. 5)

Guryan (2003) on Mass

Using state aid formulas as instruments, I find that increases in per-pupil spending led to significant increases in math, reading, science, and social studies test scores for 4th- and 8th-grade students. The magnitudes imply a $1,000 increase in per pupil spending leads to about a third to a half of a standard-deviation increase in average test scores. It is noted that the state aid driving the estimates is targeted to under-funded school districts, which may have atypical returns to additional expenditures. (p. 1)

Goertz & Weiss (2009) on NJ

State Assessments: In 1999 the gap between the Abbott districts and all other districts in the state was over 30 points. By 2007 the gap was down to 19 points, a reduction of 11 points or 0.39 standard deviation units. The gap between the Abbott districts and the high-wealth districts fell from 35 to 22 points. Meanwhile performance in the low-, middle-, and high-wealth districts essentially remained parallel during this eight-year period (Figure 3, p. 23).

I could go on. But that’s a fair share of evidence right there.

And what does Biddle provide as counter evidence to this – apparent lack of evidence I summarize above (I’ve sent the article link to Biddle on more than one occasion, but he apparently doesn’t read this kind of academic stuff)?

Biddle counters with a link to this graph – a true gem (I’ve added some annotation, not in his original)!

Yes, Biddle’s entire counter to the body of research he has not and will not read, is to use this graph of “promoting power” by student race group for Jersey City, NJ in 2004 and 2009. Note that the infusion of additional funds in NJ occurred mainly from 1998 to 2003, leveling off thereafter. But that’s a tangential point (not really).  So, Biddle’s absolute verification that more money doesn’t matter is to simply assert without verification that Jersey City got a whole lot more money and then to use this graph to argue that nothing improved!

First of all, that analysis wouldn’t pass muster in as a master’s degree level assignment (I teach a class on this stuff at that level), no less major research conclusions. From a graphing standpoint, I often criticize my students’ work for what I refer to as gratuitous use of 3d – especially where the use of 3d bars actually obscures the comparisons by making it hard to see where they align on the axis.

But, the really funny if not warped part of this graph is that there appear to be significant gains for black males between 2004 and 2009, but those gains are obscured by hiding the 2009 black male score behind the 2004 black female score.

Note that the graph also contains no information regarding the actual shares of the student population that fall into each group? Not very useful. Pretty damn amateur. Certainly fails to make any particular point, and certainly doesn’t refute the various citations above – all of which employ more rigorous analytic methods, apply to more than a single district, and most of which appear in rigorous peer reviewed journals.

References:

Card, D., and Payne, A. A. (2002). School Finance Reform, the Distribution of School Spending, and the Distribution of Student Test Scores. Journal of Public Economics, 83(1), 49-82.

Deke, J. (2003). A study of the impact of public school spending on postsecondary educational attainment using statewide school district refinancing in Kansas, Economics of Education Review, 22(3), 275-284.

Downes, T. A. (2004). School Finance Reform and School Quality: Lessons from Vermont. In Yinger, J. (ed), Helping Children Left Behind: State Aid and the Pursuit of Educational Equity. Cambridge, MA: MIT Press.

Downes, T. A., Zabel, J., and Ansel, D. (2009). Incomplete Grade: Massachusetts Education Reform at 15. Boston, MA. MassINC.

Goertz, M., and Weiss, M. (2009). Assessing Success in School Finance Litigation: The Case of New Jersey. New York City: The Campaign for Educational Equity, Teachers College, Columbia University.

Guryan, J. (2003). Does Money Matter? Estimates from Education Finance Reform in Massachusetts. Working Paper No. 8269. Cambridge, MA: National Bureau of Economic Research.

Papke, L. (2005). The effects of spending on test pass rates: evidence from Michigan. Journal of Public Economics, 89(5-6). 821-839.

Roy, J. (2003). Impact of School Finance Reform on Resource Equalization and Academic Performance: Evidence from Michigan. Princeton University, Education Research Section Working Paper No. 8. Retrieved October 23, 2009 from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=630121(Forthcoming in Education Finance and Policy.)

Private Choices, Public Policy & Other People’s Children

I don’t spend much if any time talking about my personal decisions and preferences on this blog. It’s mostly about data and policy.  There’s been much talk lately about whether a Governor’s or President’s choice to send their children to elite private schools, or where Bill Gates, Mark Zuckerberg or prominent “ed reformers” attended school are at all relevant to the current policy conversation around  “reforming” public schools.  When those choices have been questioned publicly, they’ve often been met with the backlash that those are personal choices of no relevance to the current policy debate – just dirty personal attacks about personal, rational choices.

I have no problem with these personal choices. But, these personal choices may, in fact be relevant to the current policy debate.  I do keep in mind my own personal choices and preferences as I evaluate what I believe to be good policy for the children of others. And, I try to keep in mind what I know from my background in research and policy when I make my personal choices.   Like these prominent politicos and pundits, I too choose private independent schools – relatively expensive ones – for my children, and I have my reasons for doing so. As I’ve noted on my blog on a number of occasions, I taught at an exceptional private independent school in New York City, and have relatives and friends who continue to be involved in (and with) high quality private independent schools as teachers, administrators and parents. I did not, however, attend private school. I attended public school in Vermont, followed by private college (Lafayette College).

Why do I personally prefer private independent schools, which often come with a high price tag?  Here are a few reasons:

  1. The responsiveness that comes from a close-knit small community with not only small class sizes but also lower total student load for teachers (at middle and secondary level in particular)
  2. The depth and breadth of curricular offerings ranging from Latin in the middle school, to a diverse array of social science, advanced science and math courses at the high school level and a plethora of opportunities in the arts and athletics.
  3. The lack of emphasis on standardized testing – bubble tests and overemphasis on tested curricular areas and state standards.

Yes, I do consider it important that these schools are not test-whipped, specifically that they are not obsessed with basic reading and math bubble tests alone, or even more disturbing, tests of science and social studies content where the balance (or absence) of content is a function of partisan preferences of ill-informed politically motivated elected officials (e.g. Kansas science standards, or Texas social studies/history standards – thankfully, I’m not in KS anymore).

These days, I consider it especially important that my children not be in a school where teachers have to hang their hopes of achieving a living wage (or getting a bonus to afford cosmetic surgery as in “Bad Teacher”[hope to see that one soon!]) on whether or not my child gains X+Y points on those reading or math tests. In fact, these may now be my main reasons for opting out.

So yes, you might try to call me a hypocrite for preferring private schools for my own children while apparently being such a staunch defender and supporter of the public system (including voting yes on local district budgets, even when encouraged to vote no by public officials). But that would be a dreadful oversimplification and misrepresentation of my position.

I have worked in both public and private schools – one good and one bad of each – over a 10+ year period prior to my life in higher education.  I’ve studied and compared public and private schools in various locations and of various types for over 15 years and published numerous articles, papers and reports. What I’ve learned most from these studies is that private and/or less regulated markets are simply more varied than public and/or more regulated markets. Neither better nor worse on average – simply more varied.

Top notch private schools spend much more, and many financially strapped, relatively average to very low academic quality private schools do spend much less. Much more and much less than one another, and much more and much less than nearby public schools.  It is a massive bait and switch to suggest – look how great Sidwell Friends (DC),  Dalton or Fieldston (NYC) are compared to public schools, and look how much the average Catholic parish elementary school spends compared to the urban public district?  Of course, it’s never as obviously phrased as a bait and switch – suggesting that you can get a Sidwell or Dalton education at an urban Catholic elementary school price.  You can’t! Yes, the average Catholic parish elementary school likely spends less per pupil than the public district. But that school is no Sidwell, Dalton or Fieldston, which spend closer to and in excess of double the public schools in their area.

Private schools do not, as many assume, spend only about half what public schools do. This is urban legend, drawn from dated analyses that were misrepresented to begin with (over 10 years ago).  My extensive report on private school supply and spending covers these issues quite extensively.

To reiterate a major finding from my study of private school costs, private independent schools of the type I am talking about here (members of NAIS or NIPSA), spend ON AVERAGE, 1.96 times the average per pupil amount of public schools in the same labor market! (and have half the pupil to teacher ratio)

I am quite convinced that many of the policy makers who choose elite private schools for their own and advocate for scaling back the public system, really don’t understand the difference. They really don’t know that their private schools outspend nearby traditional public schools – by a lot – despite serving more advantaged student populations. Heck, I’ve talked to administrators in private independent schools who feel that their own budgets are tight (legitimately so), and assume that the public schools around them spend much more per child. But they are simply naïve in this regard (while wise in many other ways). No intent to harm. They’ve simply bought into the misguided rhetoric that private schools spend less and get more and they’ve never double-checked the facts. But even a few minutes of pondering their own budgets and looking up local public school spending brings them around. (Part of this perception is likely driven by differences in access to funding for capital projects, where heads of private schools recognize the heavy lifting of major fundraising campaigns, and envy the taxing authority of public school districts for these purposes).

In my view, the hypocrisy lies in what those who choose elite private schools for their own argue are the best solutions for public education for the children of others.  If the preferences are the same, there is no hypocrisy. The problem is when those preferences are vastly different – completely at odds – as they tend to be in the present “ed reform” and “new normal” debate.

It is hypocritical for pundits who favor for their own children, expensive schooling with diverse curriculum, small class size and little standardized testing (freeing teachers to be professionals), to argue for less money, class size increases and increased standardized testing (and teacher evaluation based on those tests) when it comes to other peoples’ children.

Yes, I too personally favor expensive private schooling for the reasons I’ve indicated above. And yes, my private school significantly outspends both the elite suburban public school district where I live and New Jersey’s reasonably well funded urban districts (compared to other states, see: http://www.schoolfundingfairness.org).   The way I see it, I would not just be a hypocrite, but a complete a-hole if I used my pulpit (what little pulpit I have) as a school finance expert to argue that we should be spending less on others, advocating different policies for others than I desire for myself.  But it’s precisely because I spend my day buried in data on school finance and education policy that I see this glaring hypocrisy.

The difference is that I believe that other children – those whose parents are not able to make this expensive choice – should have access to well-funded schools that also provide small class sizes, diverse curriculum, and for that matter, place less emphasis on standardized tests, and treat teachers as responsible, knowledgeable professionals (not script reading stand-ins and test proctors).

To clarify, this is not a criticism of individuals with personal preferences for high quality education for their own children who are otherwise unconcerned with (or oblivious to) the broader public policy questions pertaining to the children of others. Rather, this is a direct criticism of those public officials and vocal “ed reformers” who prefer high quality, well funded education for their own and then loudly and publicly advocate for a very different quality (and type) of education for the children of others.

If we could actually close the gap between public school resources and resource levels of elite private schools, there might be less demand for those elite private schools (though some would indeed respond with an arms race to outpace public schools).  Presently, however, elite private schools stand to benefit significantly from the “ed reform” and “new normal” movement which will likely make more public schools – including those in more affluent ‘burbs – even less desirable for parents currently on the fence.

So, here’s my challenge to all those policymakers who also prefer elite private independent schools for their children.  I urge you to make a list of all of the reasons why you chose a private independent school. Notably, many if not most parents list class size as a major factor (and most schools advertise class size as a major benefit).  Make a list of the specific attributes of your private school including:

  1. Average class size
  2. Teacher education levels
  3. Numbers and types of elective and advanced course offerings
  4. Numbers and types of extracurricular activities
  5. Whether they pay more experienced teachers more than less experienced ones (or more for teachers holding advanced degrees?)
  6. Whether they emphasize student test scores when evaluating or compensating teachers?

and whatever else you might think of. (here are a few sample NJ private schools)

Get a copy of the school’s IRS 990 tax filing from the school (or from:  http://foundationcenter.org/, or http://www.guidestar.org) to find out roughly how much your school spends each year, and divide that by the number of total enrolled pupils.

Then, gather similar information on surrounding public schools. Make your own comparisons. And after you’ve done so, let me know if you’re still comfortable making bold public proclamations that we need to reign in the absurd spending of public schools, increase class sizes and slash all of those frivolous extracurricular programs for other people’s children, but certainly not our own!

Video Extra:

And a Song:

Zip it! Charters and Economic Status by Zip Code in NY and NJ

There’s no mystery or proprietary secret among academics or statisticians and data geeks as to how to construct simple comparisons of school demographics using available data.  It’s really not that hard. It doesn’t require bold assumptions, nor does it require complex statistical models. Sometimes, all that’s needed to shed light on a situation is a simple descriptive summary of the relevant data.  Below is a “how to” (albeit sketchy) with links to data for doing your own exploring of charter and traditional public school demographics, by grade level and location.

Despite the value of a simple, direct and relevant comparison using accessible data providing for easy replication, many continue to obscure charter-non-charter comparisons with convoluted presentations of less pertinent information.  Matt DiCarlo recently published a very useful post (at Shanker Blog) explaining the various convoluted descriptions from Caroline Hoxby’s research on charter schools that make it difficult to discern whether the charter schools in her comparisons really had comparable student populations to nearby, same grade level traditional public schools.

 As I’ve discussed in the past, charter advocate researchers tend to avoid these basic comparisons, instead showing that students selected through the lottery were comparable to those not selected but who still entered the lottery (excluding all those who didn’t enter the lottery). While this information is relevant to the research question at hand (comparing effectiveness among lottery winners and losers), it skips over entirely another potentially relevant tidbit – whether, on average, the charter students are comparable to students in surrounding schools.

Alternatively, charter advocate researchers will compare charter characteristics to district wide averages, or whatever comparison sheds the most favorable light.  For example, Matt DiCarlo explains of Caroline Hoxby’s NYC charter research that:

“The authors compare the racial composition of charter students to that of students throughout the whole city – not to that of students in the neighborhoods where the charters are located, which is the appropriate comparison (one that is made in neither the summary nor the body of the report). For example, NYC charter schools are largely concentrated in Harlem, central Brooklyn and the South Bronx, where regular public schools are predominantly non-white and non-Asian (just like the charters).”

The better approach is, of course, to compare against the, well, most comparable schools – or those serving similar grade levels in the same general proximity – or even to be able to identify each individual school (such that one can determine comparable grade levels) among districts in similar locations.

Here’s my general guide to making your own comparisons using a readily available data source.

Go to: www.nces.ed.gov/ccd

Use the Build a Table function: http://nces.ed.gov/ccd/bat/

  1. Select as many years of data you want/need (first screen toggle)
  2. Select the “school” as your unit of analysis for your data (first screen, drop down)
  3. Select “contact information” from the drop down menu on next screen
    1. Select location zip code
    2. Select location city
  4. Select “classification information” from the drop down menu
    1. Select the “charter” indicator
    2. Select the “magnet” indicator (in case you want to include/exclude these)
  5. Select “total enrollment” from the drop down menu
    1. Select total enrollment
  6. Select “students in special programs” from the drop down menu
    1. Select students qualifying for free lunch
    2. Select students qualifying for reduced price lunch
  7. Select “Grade Span Information” from the drop down menu
    1. Select “school level” identifier
    2. Select “High Grade” and “Low Grade” indicators if you want more flexibility in comparing “like” schools
  8. Pick the state or states you want (you can’t use this tool to pull all schools nationally because the data set will be too large for this tool. Complete data are downloadable at: http://nces.ed.gov/ccd/pubschuniv.asp )

Calculate Percent Free Lunch and Percent Free & Reduced Lunch (divide groups by total enrollment)!

Play…

Here are some examples…

First, here are a handful of New Jersey Charter Schools compared to other schools (comparable and not) in their same zip code.

In this first figure, from a Newark, NJ zip code, we can see quite plainly and obviously that the shares of children qualifying for free lunch in Robert Treat Academy are much lower than all other surrounding schools, including the high school in the zip code (Barringer), where high schools typically have lower rates of students qualifying (or filing relevant forms) for free lunch.

Here are a few more.

Other “high flying” charters in Newark including North Star Academy, Gray Charter School and Greater Newark Academy, in a zip code with fewer traditional public schools, tend to have poverty concentrations more similar to specialized/magnet schools than to neighborhood schools in Newark. Other charter schools like Maria Varisco Rogers and Adelaide Sanford have populations more comparable to traditional neighborhood schools.  But, we don’t tend to hear as much about these schools – or their great academic successes.

Things aren’t too different over in Jersey City.  In the area (zip code) around Learning Community Charter School, other charters and neighborhood schools have much higher rates of children qualifying for free lunch than LCCS. Only the special Explore 2000 school has a lower rate.

Ethical Community Charter also stands out like a sore thumb when compared to all other schools in the same zip code, including those serving upper grades which typically have lower rates.

But what about those NYC KIPP schools? How about some KIPP BY ZIP?

So much has been made of the successes of KIPP middle schools, coupled with much contentious debate over whether KIPP schools really serve representative populations and/or whether they are advantaged by selective attrition. I included some links to relevant studies on those points here. But even those studies, which make many relevant and interesting comparisons, don’t give the simple demographic comparison to other middle schools in the same neighborhood. So here it is:

Paul Mulshine, Amoral Self-Indulgence & New Jersey School Finance

On most days, I can simply laugh off a ridiculous Paul Mulshine column in the Star Ledger. Most of his claims regarding education, taxation and the intersection of the two range from flat-out incorrect to wacky and misguided. But Mulshine’s claims in his column on Wednesday June 22nd necessitate a response.

For several years, I have been a professor where one of my primary responsibilities has been to train future school administrators. I believe strongly that well-informed well prepared and knowledgeable school administrators can and should play a critical role in guiding public education policy.  As one might figure from the name of this blog, my emphasis is on teaching school finance – an inherently political and divisive topic that often pits one district against another or even one school against another. As a result, I believe it is particularly important that leading voices in education policy in a state understand not only how policies affect their own district and children but how those policies affect children statewide – that local school administrators can think beyond the boundaries of their own school district and local constituents, and be mindful of the good of the public as a whole.

Any local school administrator would likely want to find ways to manipulate the state formula for allocating aid in a way that drives more aid to their district. And over the years, I’ve seen many twisted and unethical arguments advocated and legislated to accomplish these goals – including Jackson Wyoming – the wealthiest district in Wyoming – arguing (successfully) that it needs 30% more funding than any other district in the state simply because it is so wealthy. Kansas similarly adopted provisions which provide for more funding in districts a) with higher priced houses and b) with more children attending school in new facilities. I’ve seen more money driven to wealthier districts in South Carolina on the argument that they have more gifted children. And I’ve seen more money targeted to white schools than black schools in Alabama (still in effect) on the basis that white schools have more teachers with advanced degrees and that teachers with advanced degrees cost more (built into the state aid calculation). I’ve written on this topic in peer-reviewed research.

I’ve often been frustrated to see local public school administrators in districts advantaged by these illogical policies either sit idly by, knowing the policies to be wrong, or advocate loudly on behalf of these policies, still knowing full well that the policies are built on flimsy if not absurd arguments.  In the politics of state school finance, self-interest is often hard to overcome.  It is a rare administrator who is able to balance these conflicts well – to not take the easy way out and accept an absurd or even unethical policy position simply because it drives more dollars to their constituents. Earl Kim of Montgomery Township is one of those rare administrators.

Mr. Mulshine’s view that the only role of the local public administrator is to get more for his or her constituents, and that local bureaucrats should never take any action to the contrary – regardless of ethical considerations – is not only absurd but is indicative of much of what is wrong in politics today and society in general.  Mulshine prefers his bureaucrats to be amoral sock puppets.

Here is a clip of what Mulshine had to say about Earl Kim:

“Let me offer a hint to this overpaid bureaucrat: An employee of the school board  has no say whatsoever in such public policy matters as the proper amount of property-tax relief.”

“If he did, however, he should not be advising his superiors to take a course of action that deprives the taxpayers of tens of millions of dollars that could lower their property taxes and help keep them in their houses.”

What Earl Kim understands and what Mulshine clearly doesn’t, is that while Doherty’s “Fair School Funding” plan might drive a lot more money into Earl Kim’s district, it would only do so at the expense of the system as a whole. And that is an ethical compromise that Earl Kim seems unwilling to support. To Mulshine, however, ethics seem inconsequential, when traded for millions of dollars.

Let’s actually take a simulated look at why Earl Kim might be concerned about the Doherty plan. Let’s start with a quick look at how school finance formulas work.

Local public school districts receive varied amounts of state aid based on two major types of factors:

  1. Differences in local school districts’ ability to raise local tax revenue to pay for schools;
  2. Differences in the needs and costs of providing adequate educational services to widely varied student populations.

In simple terms, the current formula – SFRA – accounts for both, and the Doherty plan accounts for neither.  Aw… what the heck, all that math is too complicated anyway!

In a typical state school finance formula, there is a target amount of revenue to be raised by each school district – based on the estimated differences in needs and costs of children attending each district and other factors such as variations in competitive wages for teachers. But, even if the target funding per pupil was the same for each district, the state aid share would be very different. Why? Because some districts have far greater capacity to raise local property tax revenues than others.

Here’s a New Jersey SFRA simulated (oversimplified) example using data from 2009 and 2010. Under the 2010 SFRA, the average target budget per pupil for an Abbott district was $16,387, based on the greater needs of children in these districts and the fact that the largest Abbott districts were also in higher cost north Jersey labor markets.

Applying equitable tax effort, Abbott districts are only able to raise about $4,300 per pupil compared to wealthy districts (to 2 deciles) which can raise, on average, over $13,000 (which is actually more than they would need).  State aid, as it currently stands in NJ (and in other states with similarly structured formulas) is used to fill the gap between what can be fairly raised locally, and what is estimated to be needed to provide an adequate education.

Expressed as effective tax rates, the local share for wealthy I&J districts appears to be slightly higher when expressed relative to property values, but these districts have the lowest effective rate with respect to income – even under SFRA. Overall, the distributions are relatively fair. I’ve written previously about this.

So then, how would it work to simply give every district the same amount of state aid per pupil? The Doherty plan argues for giving every district $7,481 per pupil a) regardless of need and costs and b) regardless of ability to raise local revenue. That would be unprecedented, even in Kansas, Alabama or Wyoming.*

This table shows one perspective on the Doherty plan – if the state simply gave every district the same amount of state aid per pupil, but if we then assumed that districts would need to raise the rest on their own if they really wanted to provide an adequate education (as estimated under SFRA). That is, if high need districts like Abbott districts still wished to try to raise what SFRA projected that they needed. Abbott districts would be expected to raise $8,906 per pupil toward their $16,387 and the wealthiest districts would be expected to raise on their own, about $5,000 per pupil. This creates a nearly nine-fold difference in the effective income tax equivalent across districts! And that’s “Fair School Funding?” One can understand Earl Kim’s concern, even if the proposal would bring home millions to Montgomery Township!

Here’s what it looks like in pictures. In the first picture, we see how SFRA operates pretty much like any state school finance formula built on a “foundation formula” approach. Each district has a target revenue per pupil. And the poorest districts – those with the least local fiscal capacity – are expected to raise the least toward this total. Wealthy districts even when applying equitable tax effort can raise far more than they need!

Here’s what the Doherty plan would look like. Here, every district gets the same regardless of need or capacity. This is rather like arguing that we should distribute food stamps and other financial assistance to residents of the estates of Far Hills in equal amounts to the distributions in Camden, or that we should pave well-conditioned and little used roadways with comparable frequency to heavily worn, highly traveled ones. When we place Doherty aid on top of 2009 local revenues per pupil, we see that the lowest income districts end up having combined state and local revenue per pupil well under $10,000 and that the wealthy districts now have combined state and local revenue per pupil approaching $25,000.

Here’s how it looks with respect to children qualifying for free or reduced price lunch. New Jersey’s school finance system has been praised in several national reports, including this one, for most effectively targeting additional resources toward greater needs. And there exists a significant body of research to validate that such school finance reforms actually do matter (regardless of political rhetoric to the contrary). Indeed the Doherty plan would turn New Jersey school finance on its head – making the system among the most regressive in the nation. That is, a system where higher need districts have systematically fewer resources per pupil.

Now, I don’t expect that this proposal really has much broad-based support, and I would not have typically bothered to critique or debunk it. I’ve stated my reasons above for why I needed to take this particular issue on at this time and under these circumstances.  It would simply make no sense for a well-informed local public school administrator like Earl Kim to advocate on behalf of a policy that is so clearly wrongheaded, so obviously unfair, simply because that policy would drive money into the pockets of his constituents.

(Finally, as an interesting aside, we also know from a series of studies of property tax relief aid for wealthy districts in New York State that increasing state aid to wealthy districts is among the surest ways to increase inefficiency in school district spending.  I often use the analogy that it’s like giving out $100 gift cards to Scarsdale residents to shop at Neiman Marcus. They take the $100 and spend $500 for something they didn’t really need. That is, these policies seem to encourage inefficient spending as much if not more than they provide tax relief. Meanwhile, we might have reallocated those $100 gift cards for basic needs in nearby Yonkers or Mount Vernon.)

*Note: It is conceivable that a state would attempt to create a fully state financed education system (that is, eliminate local share) in which case there is no need to correct for differences in capacity to raise local share. But, a completely flat allocation under these circumstances would fail to address differences in needs and costs.  Relying entirely on state source revenues (sales and income taxes) can, however, reduce the stability of revenue flow to schools (property tax revenues tend to be more stable in economic downturns).