Monday, March 14, 2016

Kansas Education Policy: Building a Funding Formula, Pt3: Valuations

I've spent my last few posts focusing on whether Bernie Sanders still has a potential to win the Democratic nomination, but I haven't forgotten about my Kansas education project.  Time to stop neglecting that project with a new variable: assessed property valuations.

WHERE WE ARE

Last time we improved our regression model for a new State funding formula by adding a variable to control for poverty impacts on education.  Once again we're building slowly and hoping to help solve this issue, though it appears the legislature is moving forward with a couple of pieces of legislation on this topic.

There is quite a bit of argument around how to fix the Kansas education funding formula to comply with court orders, the main two methods by either adding money or simply redistributing funds.  It seems possible though, that all prior arguments will be somewhat moot after revenues are re-estimated in April by legislative researchers.  At that point, future revenue estimates may be revised down causing a significant refactoring of the entire budget.

This whole situation has given rise to some interesting punditry, including a  Lawrence Journal World editorial which may be rated as one of the worst risk analyses in history, saying schools PROBABLY won't open in August, and may be closed all next school year.  Seriously..  The author thinks that legislators might risk school closings during an election year, based on a single timing-based data point, in a case where schools were not shut down.  Risk analysis was not this person's forte.

ADDITIONALLY THIS WEEK

This week we're looking at how higher local property values impact local education spending.  There are at least three a priori theories on how this relationship functions:
  • High assessed property values = High costs: In this scenario, districts in high assessed value regions have to pay more for services (e.g. teacher salaries) due to higher cost of living.  In essence: teachers rationally choose to simultaneously maximize salary and minimize costs, thus ceterus parabis, districts with higher property value have higher wage costs.  Other costs such as construction and goods that vary regionally could also contribute to higher costs.
  • High assessed property values = Higher spending: An additional hypothesis here is that "rich" districts can more-easily raise capital by raising a few mills, and thus have an easier time spending more money. Effectively they can spend more money more freely, and may spend in an attempt to get better results out of students.  Economists may also contend, that being less capital constrained, they may be more likely to spend inefficiently.
  • High assessed property values = Endogeneity: This is a fairly complex statistical concept. In this case, causation runs from the independent variables to the dependent variables, but also, causation can run in the opposite direction.  A good example of this is the jobs market (also good for some arguments occurring in the Kansas Legislature right now).  If we were to model job growth in a region, we would likely want to include population growth as a predictor as population growth can lead to more jobs through more regional spending and cheaper labor. We also know that strong job growth can improve cause inbound population migration, meaning causation runs both ways, and an individual coefficient can be inaccurate.  There are a few statistical techniques to deal with this (most commonly instrument variables).  The point is though, it can muddle our statistical statement that x leads to y.  In the instance of property values, endogeneity would work this way: valuations can lead to higher spending generally, as well as more spending on teacher salaries.  But we also know, that if more is spent on schools (in the right ways) we could create better schools, which in turn can increase property values. 

The way that we introduce this variable into our equation assessed valuation per FTE, because our dependent variable is already on a per FTE basis.  Here's what it looks like in a regression.



The regression shows a positive statistically significant relationship, as valuations increase so does spending.  The coefficient demonstrates an elasticity of 0.08, or that a 1% increase in valuations leads to a 0.08% increase in spending.  That might not seem like a lot, but there's a lot of variation in valuation per FTE, which can lead to wide variations (here's a view of variation, by FTE per district):



OPERATIONALIZING

We just throw this into our new funding formula and roll with it... right?  Wrong.  Remember our three functional theories as to why higher assessed valuation leads to higher education spending from above.  These theories matter because in some cases they indicate districts that require higher funding and in other cases they do not:
  • If a district has ceterus parabis higher costs due to cost of living issues we absolutely want to give higher funding, so that they can provide comparable and equitable education.
  • If a district is simply choosing to spend more, due to ease of raising capital, we probably don't want to give higher funding, else risking equitable funding.
  • If we're just measuring an endogenous relationship, we absolutely don't want to use this as a basis of funding, because statistical issues should never become a basis for varied funding.
What we need to do now is figure out a way to fund district on costs and not efficiency.  Over the development of our function, we will do this in two ways:

  • To parse out what are truly higher costs, we will bring in cost of living/cost of comparable teacher variables to measure the local cost and teacher-labor market variation.
  • To deal with the endogeneity issue, we'll use an instrumental variable regression methodology and test whether the endogeneity is having a significant impact.

CONCLUSION AND WHAT'S LEFT

In our work on the Kansas education funding we have covered these basic attributes, and believe that any functioning funding formula will need a way to control for these attributes:
  • Economies of scale: The formula will need to account for the lack of economies of scale in running rural, low enrollment school districts.  We have established a fairly good picture of what that cost curve looks like.
  • Poverty: Education literature demonstrates that poverty negatively impacts education outcomes, and we demonstrated that schools with kids in poverty in Kansas perform poorer.  Currently districts with higher poverty rates are also spending more, so any future funding formula likely needs to account for varying levels of poverty in schools.
  • Local Property Values: This variable correlates positively with prior spending, but we need to sort out what is higher cost, and what is potentially spending due to easier access to capital.

In addition to this, I started keeping a list of elements I need to address in the future (contact me if you would like anything added to the list):
  • Transportation Funding (geographical size modifier)
  • Performance Measures
  • Special Education
  • Teacher Salaries
  • Consolidation Equilibrium
  • Avoiding the Ipso Facto: making sure our regression equation doesn't mirror old funding formula



1 comment:

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