Friday, May 15, 2015

Kansas Education Funding Analysis Part 1

Public education funding in Kansas is a huge mess.  The last ten years have seen multiple lawsuits, annual battles in the State Legislature on education spending, and massive changes in the State education funding formula.  

Why is this such a big deal in Kansas?  A couple of factors.  First, the Kansas Constitution has a section that says the State must adequately fund education.  Second, the Kansas legislature is largely made up of small-government fiscal conservatives, so it is relatively difficult to increase public spending for anything.  Twice in the last decade, citizens have filed lawsuits against the State to force an increase in public spending.  Twice they have (essentially) won.

At the heart of this question seems to be two main questions:
  • How much do we actually need to spend on schools to meet constitutional requirements?
  • If we fund schools more, will we get better results?


If you're just interested in whether or not spending matters to educational outcomes, skip to the conclusion section.

I have mentioned before that some of my first professional work was on a Kansas education "cost study" for the State auditor's office, about a decade ago.  During that study I did quite a bit of work on relative teacher salaries, but also some work on the relationship between spending and education outcomes.  For that study we contracted with a couple of professors out of Syracuse, their study can be found here.  (That study goes into more detail, so if you're very interested in method, start there).

Now, 10 years on in my career, I know I can do the spending to education outcomes research on my own, specifically replicating their original research and answering some questions:
  • How have coefficient values and relationships evolved over the past 10 years?
  • Does increased spending continue to relate to better outcomes in Kansas education?


My methodology here is  to replicate other education research on spending to outcome, this is just part 1 of a potentially many part series.  Please keep in mind:
  • I'm just one dude running this analysis while my other queries run.
  • The original study cost the State over a million dollars, and took up 6 full months of the State audit offices time (read: 25ish staff).
  • I'm going to build models slowly, as data comes available, so what I have for today is just a truncated model, but it's a start for a conversation.
I went to the State of Kansas Department of Education website looking for data, and found a nice data warehouse where I can run custom queries.  I pulled down data for the past three complete school years, did a bit of data cleaning, and pulled out what appeared to be the top variables.  I also bucketized district size, as had been done in the original study.

The model type here is a cost function, which estimates how much something will cost by various input factors.  Certain things increase costs for a district, which we can measure (poor kids, having fewer kids (being less efficient), and performing better on standardized tests, theoretically, should cost more money).

Here's my variable list and an explanation:

  • PERPUPSEPDN: Our dependent variable, per pupil dollars spent.
  • AVGASS: Our most important independent variable, average assessment values for each district (how well do kids do on standardized assessments).
  • FREELUNCH: % of kids on free lunch.  Kids in poverty are more difficult to educate, so this increases cost.
  • TEACHSAL: Average salary of teachers in the district.
  • VALPUP: Per pupil property values.  This is part of the efficiency variables used in the original study.  Effectively, these efficiency variables measure factors that make it easier for school district to spend money in inefficient ways.
  • ENROLLCAT: Categories for different school district sizes.
  • YEAR: Fixed effect for what year we are measuring.


So, what did I find?  

The important AVGASS variable is positive, and approaching statistical significance, meaning that some kind of relationship likely exists. 

Percent of kids receiving free lunch (a proxy for poverty) shows that districts with a lot of kids in poverty still spend more to get the same results.  Also, "property rich" districts still outspend "property poor" districts.

Keeping in mind that this data is still a bit noisy and I'm not yet controlling for all of the factors of the original study, nor using as many years of data, this is quite promising.  I can generally conclude, spending is still significantly related to education outcomes.*

Next Steps:  For the next steps here, I will try to acquire more years of data and more attributes, clean the current data set (I think it's likely form what I've seen that I have some data entry errors), and work on a better model.  Eventually, I may try to calculate actual spending levels required to hit specific outcomes levels for different school districts.

*Quick footnote from above.  I'm purposely avoiding terms that insinuate causal linkages, largely because this analysis has not yet flushed that issue out.  Do I think it's likely that spending more can create better results?  Yes.  But I also know how confounding some of these issues can be.  Specifically, intervening and co-linear variables mean that the relationship observed isn't as simple as spend more, get better.  It's likely that more affluent districts both tend to spend more money AND get better results for other reasons (less "unmeasurable" poverty, other social problems, parents with de-facto higher education levels, etc).  My point:  this doesn't prove cause, though through iterated analysis, we should be able to move in that direction.


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