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PaperclipPerfector  8mo ago (text post)   28929 thread views

Culture War Roundup for the week of August 7, 2023

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Is Education Spending Progressive?

This is mostly a specific data question. Brookings Institute reports that education spending is ever so slightly higher in poor districts than non-poor districts; this paper by two Stanford Professors seems to find the same thing; the Urban Institute (with one of the same authors from the Brookings report) seems to show that on the state level funding is frequently progressive. I’m not doing full justice here to these papers, which are more nuanced and actually argue that funding to poor districts is still frequently lacking. This makes sense to me - it stands to reason if you spend basically the same on rich students and poor students the former will still do better based on all their other advantages. But I’m more wondering about the empirical claim itself because the Economic Policy Institute finds the exact opposite results. They agree using national averages school spending is a little progressive, but find that when you break things up at the district level wealthier districts receive more funding.

The funny thing here is that they’re using the same data source as the other papers: the Department of Education’s Digest of Education Statistics / National Center for Education Statistics 2017–2018 Local Education Agency Finance Survey. But when EPI looks at it they find:

While state revenues are a significant portion of funding, they only modestly counter the large locally based inequities. And while federal funding, by far the smallest source of revenue, is being deployed as intended (to reduce inequities), it inevitably falls short of compensating for a system grounded in highly inequitable local revenues as its principal source of funding. As such, although states provide their highest-poverty districts with $1,550 more per student than to their lowest-poverty districts, and federal sources provide their highest-poverty districts with $2,080 more per student than to their lowest-poverty districts, states and the federal government jointly compensate for only about half of the revenue gap for high-poverty districts (which receive a per-student average of $6,330 less in property tax and other local revenues). That large gap in local funding leaves the highest-poverty districts still $2,710 short per student relative to the lowest-poverty districts, reflecting the 14.1% revenue gap shown in Figure C. Even though high-poverty districts get more in federal and state dollars, they get so much less in property taxes that it still puts them in the negative category overall.

And when the Stanford guys look at it they find:

Poor spending exceeds nonpoor spending in 56 percent of districts; 63 percent of the FRPL population attend schools in these districts. Black spending exceeds white spending in 71 percent of districts; 70 percent of black students attend schools in these districts. Hispanic spending exceeds white spending in 60 percent of districts; 61 percent of Hispanic students attend schools in these districts. Overall, larger districts (by enrollment) also tend to be those that spend more on disadvantaged students; therefore, more disadvantaged students are concentrated in districts with greater inequality.

What gives? Am I misunderstanding and they’re actually measuring different things that cause their results to be different?

This makes sense to me - it stands to reason if you spend basically the same on rich students and poor students the former will still do better based on all their other advantages.

I can’t imagine the spending disparity at which that wouldn’t be the case or the upper/middle classes ever allowing us to actually approach it. I maintain the state is an ersatz parent at best and that arguing over the exact allocation of property taxes is barking up the wrong tree.

I have no idea what if anything would actually make poor and rich students equal, but here at least they'ree suggesting a more modest metric of hitting "funding required for adequate test scores," as estimated by the the Department of Education's National Education Cost Model.

Medium- and high-poverty districts are spending, respectively, $700 and $3,078 per student less than what would be required. For the highest-poverty districts, that gap is $5,135, meaning districts there are spending about 30% less than what would be required to deliver an adequate level of education to their students. (Conversely, the two low-poverty quintiles are spending more than they need to reach that benchmark, another indication that funds are being poorly allocated.)

I don't understand the cost model well enough to know if it makes sense or not.

The "adequacy" metric -- the National Education Cost Model -- should be regarded as political and useless. It assumes it costs more to teach poor kids to the same level, so if you have two adjacent districts with the same per-pupil funding, one rich and one poor, the poor one might be regarded as having "inadequate" funding and the rich one as having more than "adequate" funding.

Why would be that be useless? That seems obviously true to me. Put a fast person and a slow person and the same starting line and you expect the fast person to always pull ahead. Per their calculations, it takes more funding to get a poor kid to baseline than a rich kid, which is what you'd expect.

Still, the EPI paper doesn't say the districts are funded the same but the poor districts lose out only on adequacy, they say poorer districts are lower funded in absolute terms as well.

Per their calculations, it takes more funding to get a poor kid to baseline than a rich kid, which is what you'd expect.

That's not a calculation, that's an assumption.

The core purpose of the NECM is to account for the fact, long established in the research literature, that the cost of providing a given level of education is not uniform across districts (Duncombe and Yinger 2007). Perhaps most importantly, districts that serve larger shares of high-need students (e.g., higher Census child poverty rates) will have higher costs. In addition, other factors, such as labor costs (e.g., districts in areas with higher costs of living will need to pay their employees more), size (economies of scale), and population density, all affect the “value of the education dollar.” The model, therefore, first estimates the relationships between district spending and these important factors, including testing outcomes. Importantly, the model accounts for the fact that school funding both affects and is affected by testing outcomes (For example, a district with higher test scores will tend to have higher property values than a district with lower scores. This high valuation allows the former district to collect more property tax revenues, which, in turn, boosts spending and positively affects testing outcomes. The NECM uses econometric methods to account for this endogeneity and tease out the causal relationship between spending and outcomes.)

This initial model yields a kind of “relationship inventory” of how each factor is related to spending. We then use the “inventory” to predict the cost (spending levels) of achieving a common outcome level (e.g., national average math and reading test scores) for each individual district, based on that district’s configuration of characteristics (in a sense, by comparing each district to other similar districts). These “required spending” estimates can then be compared with actual spending levels (total spending, direct to elementary and secondary education) in each district (this same basic process also yields our state-level estimates, which are aggregated district-level estimates). The difference between actual and required spending is a measure of adequacy relative to the common goal of national average scores.

The core purpose of the NECM is to account for the fact, long established in the research literature, that the cost of providing a given level of education is not uniform across districts (Duncombe and Yinger 2007).

They cannot actually demonstrate that a "given level of education" can be provided at any cost, at least for levels of education that are not bottom-percentile.

Perhaps most importantly, districts that serve larger shares of high-need students (e.g., higher Census child poverty rates) will have higher costs.

...And will demonstrate minimal or no gain in outcomes, despite these higher costs.

Their entire logic rests on the assumption that the higher spending is causing higher test scores. That isn't actually true, and so everything they base that assumption on is garbage-in, garbage out.

I'm willing to endorse any level of educational spending for one of these low-outcome schools, provided that the educators are volunteers, and that failure to educate means the people doing the educating and their supporters are personally on the hook for every single thin dime spent on the failure. They cannot do the job. They either know they cannot do the job, or they are so incompetent and deluded that they cannot be trusted with any level of responsability. Their entire system is built around lying about the undeniable facts that have accrued through fifty years of nation-wide policy.

Their entire logic rests on the assumption that the higher spending is causing higher test scores.

If they're taking two neighborhoods and controlling for income, cost of living, demographics, population, pop density, and so on, and find that the difference in the better performing school is more funding per student, this is a reasonable argument to make. As far as I can tell you haven't made a counterargument here. If anyone has any actual objections with the adequacy model they're welcome to raise it, but the entire thing is besides the point because, again, the EPI paper isn't saying "funding is equal but they should be given more for the adequacy score," they're saying "poor districts are funded worse, period." It's also besides the point because my OP isn't some philosophical argument about who deserves what or what's the best way to fund schools; I'm asking a pretty specific question about how these two different think tanks found different conclusions from the same data.

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