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PaperclipPerfector  1yr ago (text post)   29463 thread views

Culture War Roundup for the week of August 7, 2023

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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.

Umm, why shouldn't we assume it costs more to teach poor kids than rich ones? That seems self-evident if you don't assume poor kids are just as smart as white kids.

If you assume it, you cannot legitimately say that your study demonstrates that you need more money to educate the poor people -- you're just pulling your own assumptions out.

Because there are absolute mountains of evidence that some kids you can't teach at all at any observed price. By assuming that it "costs more", you're ignoring that evidence to frame the problem as a fiscal one, which it is not. Demonstrate that you actually can teach these kids, and what the price is, and then we can talk about whether it's worth it. In the meantime, anyone claiming that the problem is money should be presumed to be a filthy liar.

To be clear, I think we should impose a 25% budget cut on every school district in America, then hang an administrator from each every month until results improve. Then we should repeat the process. Money isn't the problem, and that would be true even if administrators weren't embezzling it- schools have enough.

But all else being equal, you would expect a population with an average IQ of 110 to be much cheaper to educate to the same level than a population with an average IQ of 95, which in turn costs less to educate than a population with an average IQ of 85. Yes, there's lots of kids in the last who are incapable of learning algebra, but even the ones who are capable of it are probably going to cost more to educate because it takes more time. And I don't think that schools having more money than they actually need in all three scenarios changes the fundamental calculus of "it costs more to teach kids that have more trouble learning".

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).

Note that Duncombe and Yinger 2007 was about reducing costs through consolidation.

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

The buried assumption here is that putting more money into schools with larger shares of poor students will improve their education. But that's exactly what we were trying to determine! This is circular.

The buried assumption here is that putting more money into schools with larger shares of poor students will improve their education. But that's exactly what we were trying to determine! This is circular.

I think you're imagining researchers comparing a poor neighborhood to a rich neighborhood and assuming the difference in outcomes is down to funding. They're not, they're comparing poor neighborhoods and finding that the stand out difference between them (after controlling for income, cost of living, demographics, population density) is the better performing poor school has more funding per student. This is a reasonable conclusion. I'm sure there are counterarguments or complaints to be made about their data or something but no one here is providing them

They're not, they're comparing poor neighborhoods and finding that the stand out difference between them (after controlling for income, cost of living, demographics, population density) is the better performing poor school has more funding per student.

The methodology is here I don't think that's what they're doing. And the numbers they come out with are large enough that I don't think they could possibly be doing that, because no high-poverty district would be "adequately funded" by their measures. They determine that the cost difference between a 100% poverty district and a 0% poverty district is, in their preferred model, $41,000 per pupil.

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).

Per their own words, the 41k number is using the most aggressive model they designed. The very next sentence says their alternative model cuts that almost in half to $26,000. Even still those reflect extreme hypotheticals parameters of 0% poverty and 100% poverty that we don't see in the real world. Again in the same paragraph:

the practical range of child poverty is from near 0% to between 40 and 50%, so the differences in costs per pupil from the actual lowest to highest poverty districts are only about half the estimated range. And, the two models are not strikingly different. For high poverty districts, the more aggressive model (which has more desirable statistical properties) leads to per pupil cost estimates that are about $5,000 per pupil higher in the highest poverty districts (or about 20% higher).

The numbers you get when looking at actual poverty ratios are below, from the EPI paper:

Highest Poverty (poorest)

Actual Spending: $13,093

Required Spending: $18,231

High Poverty

Actual Spending: $10,850

Required Spending: $13,928

Medium Poverty

Actual Spending: $10,499

Required Spending: $11,199

Low Poverty

Actual Spending: $10,532

Required Spending: $9917

Lowest Poverty (Affluent)

Actual Spending: $10,239

Required Spending: $8313

The difference in required spending between the lowest and highest poverty districts is more than half again still, less than 10k. And the amount required to raise the highest poverty districts to hit their required threshold is half again yet again, only 5k, or 1/8th the most extreme hypothetical estimate.

As I've said several times though, I don't really care. I'm just trying to figure out how they got different results from the same data set.

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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