WHERE WE ARE
Last time we started putting together a regression model for our new State funding formula. We're building this thing slowly, and putting some thought into it; hopefully we'll be done by the time the issue is legislatively solved.
The issue we analyzed last week dealt with economies of scale for different school district sizes. Essentially: smaller school districts are more costly due to the lack of economies of scales in procurement and general operations. We estimated what that curve may look like, and came up with this general chart for 2015 data:
A quick note, economies of scale curves like this always bring up the argument: why don't we force some of the smaller districts to consolidate, thus moving them down the cost curve. Obviously some economies of scale could be created this way. On the other hand, some of these districts are located in such rural areas that structurally prohibit some of the economies of scale available to an urban district. We will deal with this in a future post.
ADDITIONALLY THIS WEEK
This week we're looking at the impact that poverty has on education outcomes, current education spending, and its use in a current policy. So why do we look at poverty:
- From an a priori perspective, current education literature points to kids in poverty having more issues in the education system. A reasonable review of that is found here. There is the question of whether those difficulties can be solved with more money for public schools, however, there are some proven ways to defeat these issues (such as stronger preschools and early intervention) that cost money. As such, it's plausible that creating programs to deal with childhood poverty would increase necessary spending for high-poverty districts.
- From a Kansas perspective, we know that Kansas schools with higher poverty rates perform more poorly, many researchers have proven this in the past. Actually, I'll do it again just for fun:
This is a regression against average 11th grade performance measures (weighted by log(FTE) to smooth out any low end volatility for all of you nerds out there). The negative coefficient on the percent_free_lunch variable is the important part here, because it demonstrates that the more kids in poverty (eligible for free lunches through federal programs) the lower education outcomes.
This free lunch variable is the traditional way to measure poverty in schools, largely due to ease of measurement. There is some argument that this allows school districts to commit fraud and "game" the system by pushing the program harder to students, but for the purposes of these estimates "% free lunch" should be directionally correct.
Following this analysis, we jumped back to our original equation from the prior post. We tried a couple of different transformations of the free lunch variable, and found the one that best fit the data was raw entry (not logged). Here's what that looks like:
The positive coefficient on the percent_free_lunch tells us that costs increase as the number of students on free lunch increases. Because this is a log-linear equation the interpretation isn't easy to understand, but it's effectively a 0.2% increase in costs for every 1% point increase in free lunches.
here's what that looks like graphed:
What does this mean for our funding formula? For every 10 percentage point increase in poverty, school districts will spend between $250 and $350 per fte more.
(Yes we are still concerned about the ispo facto nature of this being included in the old funding formula, thus driving old spending rates from which our empirical data is derived. We will deal with the complications of that in a future post).
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.
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.
- Ability to Pay, Equality, and Efficient Spending
- Avoiding the Ipso Facto: making sure our regression equation doesn't mirror old funding formula