Finding a way to fix school funding in Connecticut Finding a way to fix school funding in Connecticut

New NEPPC report proposes state aid formulas to improve equity and adequacy in education funding New NEPPC report proposes state aid formulas to improve equity and adequacy in education funding

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February 4, 2021

Despite recent revisions, the current state aid formula for Connecticut’s K–12 public education system has been criticized for failing to provide sufficient funding to districts with the fewest resources and the highest education costs. In a new report from the New England Public Policy Center, “Reforming Connecticut’s Education Aid Formula to Achieve Equity and Adequacy across School Districts,” Boston Fed senior economist Bo Zhao proposes a series of state aid formulas that could improve equity and adequacy in the state’s education funding. Here, he discusses his research and describes those formulas.

In your report, you identity certain factors that determine a school district’s education cost. What are those factors?

They are student and school district characteristics that both economically and statistically significantly affect school spending. These characteristics are outside the direct control of local officials at any given point in time. I use statistical analysis to identify four cost factors for Connecticut schools: the percentage of school-aged children living in poverty; the percentage of students living in single-parent or non-family households; whether the enrollment is larger or smaller than 2,000 students, which is a proxy for an economy or diseconomy of scale; and whether a district is a regional or local district. Regional districts may have to spend more because of the additional coordination costs relative to local districts.

Why do those first two factors increase education costs?

There are simple, intuitive explanations. For example, students from families living in poverty or students living in single-parent or nonfamily households often receive less time and attention at home – and less financial support from their families – to help with their schoolwork. And they tend to live in unstable housing situations and less safe neighborhoods.

The formulas you propose are based on a district’s “cost-capacity gap.” Please explain what that is.

The cost-capacity gap is the difference between the education cost, which I calculate based on the cost factors, and the revenue capacity, which is mostly based on the property tax base per pupil in each school district. I use the cost-capacity gap as an indicator of each district’s need for state education aid. When you have a higher value for the gap, it means you don’t have enough locally sourced revenue to fully fund the education cost for reaching a common student test performance target.

Are the cost-capacity gaps largest in the big, low-income districts?

Yes, because large, urban, low-income districts are hit with what you could call a double whammy: They tend to have higher education costs, mostly because they have higher values in the two most important cost factors – single-parent and nonfamily households and student poverty – and at the same time they tend to have a much lower property tax base per pupil. Therefore, they have lower revenue capacity per pupil.

How well does Connecticut’s current state aid formula address these gaps that you identify?

My study finds that the current formula does play an equalizing role to some extent. We do see that districts with larger cost-capacity gaps tend to receive more state education grants than small-gap districts. However, the relationship between the grants and the cost-capacity gap measures is not that tight. I also find that the districts with very large cost-capacity gaps haven’t received enough to fully close those gaps. Therefore, to improve the state education aid distribution, I propose a series of new formulas that allocate aid based on the cost-capacity gap measure.

Under your formulas, if a district has a gap with a very small or negative value, would it potentially receive no aid or less aid than it’s currently receiving? If so, how does that affect the political feasibility of your formulas?

The suggested formulas are not just the result of a pure academic exercise. When developing the aid formula alternatives, I considered their political feasibility and impact on all school districts. So, I provide policymakers with various policy tools to be incorporated in the gap-based formulas. For example, the state could choose to guarantee a small amount of aid per pupil to negative-gap and small-gap districts, or it could even hold the existing aid harmless, which means the districts receive at least the same amount of aid that they currently receive.

Are there additional costs associated with these tools?

As we economists say, there’s no such thing as a free lunch. When the gap-based formula does not include any of these policy tools, the distribution of state education grants is perfectly aligned with cost-capacity gaps across school districts. However, it would result in local resources being taken away from the more affluent districts, which not only makes that formula politically unpopular, it could also have a negative impact on these districts, especially on low-income and special education students residing there.

You face different problems when incorporating a tool to increase the formula’s political feasibility. One, you will not see a perfect alignment between the distribution of state education grants and cost-capacity gaps across school districts, if, for example, there’s a minimum level of aid, or you hold existing aid harmless, which would mostly benefit negative-gap and small-gap districts. Two, incorporating these tools would raise the financial requirement for the state. If you reserve a portion of the state aid pool for the negative-gap and small-gap districts, that money is not being used to address large-gap school districts’ financial need. In the end, it is up to policymakers to decide on how to balance these tradeoffs.

This interview was conducted by Larry Bean, economics editor for the Research Department at the Federal Reserve Bank of Boston.