This “Benchmark Project” (intro here) compares financial, demographic, and census data from ten Metro Detroit, upper-middle class suburban cities and school districts: Ann Arbor, Birmingham, Bloomfield, Farmington, Grosse Pointe, Northville, Novi, Plymouth-Canton, and Troy.
In my last entry I waded into property values among our ten benchmark districts and hinted at the complexity of correlating district characteristics with surrounding communities. Like others, I am interested in how school and municipal factors influence one another (e.g. enrollment, population trends, etc.).
The problem? In Michigan school district boundaries are almost never congruent with municipal boundaries. With so many different taxing authorities in the mix (school systems, ISDs, municipalities, villages, townships, counties, libraries and the state itself) making the comparisons is no simple task.
Let’s level set on just how tangled all these taxing authority boundaries are and why it matters.
Those of you with a Grosse Pointe Schools bias can identify with this issue. We have one Grosse Pointe Public School Systems and six different cities – each their own taxing authority: the five Grosse Pointes and part of Harper Woods. On top of this, the Shores straddles Wayne and Macomb Counties, different county and ISD taxes.
Furthermore when comparing school systems, data is commonly normalized “per pupil”, but municipal comparisons aren’t as simple. A population metric makes sense, but what happens when sections of cities split across different school district boundaries?
Grosse Pointe isn’t alone in this regard. Bloomfield Hills Schools has students from its titular city, but also Bloomfield Township. Birmingham draws students from parts of Southfield, Bingham Farms, and Franklin. Northville straddles Wayne and Oakland Counties and has small tracts of its border in Novi. Pretty much every district is like this.
All this introduces another benchmark impediment – the differences in various wealth factors among these contiguous but quite different communities (median income, home values) As you’ll see as we get rolling here, the differences within a school district’s boundaries are significant. Averages other blended data can obscure major differences that might be at the root of an enrollment or population trend.
To get readers oriented on this issue I built a slide for each of the benchmark districts showing the district borders overlaid across geographic borders. I then looked up the rough population of the primary cities contributing most of its student aged population to the district being examined and built a simple table to the right of each district map. If a city appears to have just a portion of its population in the district boundaries, I labeled it as “Secondary”.
I’ll move quickly into some data comparisons in my next few posts in the series.
(To expand the slide show, click the “square” icon that’s in the middle of the bottom gray bar below the slide.)