Given educational polarization, what if you tried to predict 2020 2-party margin based on nothing other than education split by race (white college, white non-college etc)?

You'd still get a pretty strong correlation. The resulting over/underperformance map is interesting to see
Some thoughts...

[1/] Secular/non-evangelical voters are ones that Democrats perform really well with, which obviously isn't captured with just educational data
[2/] An area's baseline partisanship plays a huge role in determining immediate margins, so I wouldn't take this as gospel or whatever (none of the "OMG Wisconsin's sliding 20 points right in 2022" dooming) -- just a fun experiment to see what'd happen if the trend continues
[3/] Dems strength in the midwest is offset by their weakness in the South, relative to education/race. Probably explains why you saw Texas and Georgia snap hard to the left as Ohio began sliding right. I'd bet on that continuing
[4/] I didn't weight by population in the county regression, so the population skew is a bit of an issue, obviously, given the uneven distribution across counties.

But this is just something I thought would be fun to take a look at.
also LOL Vermont, with Biden performing 49 points better than expected in it.

and the Rio Grande Valley being a Dem overperformance based on educational splits is...well, interesting, to say the least.

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More from @lxeagle17

26 Jun
I've reconstructed the 2014 and 2018 electorates by demographic, thanks to @DKElections and @Catalist_US data and a lot of math

-Midterms are whiter, more educated (~2pt Dem boost with whites on education-based turnout differential)
-Minority turnout is a crucial wildcard

[1/] Image
Midterms are generally whiter + more educated; whites are ~2% more favorable to Dems on educational splits alone. This was more pronounced in 2018 than it was in 2014. If R voters are increasingly tied to Trump ballot presence, it could complicate things for the GOP. [2/] Image
I calculate the electorate demographic composition for 2014/2018 myself and project 2020 support by demographic onto each electorate to get an idea of what its partisanship would be now. 2012/16/20 demographic composition & 2020 2-way support by demographic are from Catalist [3/]
Read 11 tweets
23 Jun
THREAD: Simulating an RCV election in Alaska, we see that running a Democrat probably helps Kelly Tshibaka more than anything. But the value Lisa Murkowski provides to a Democratic majority is minimal, and the expected value of running a Democrat is still higher, IMO
Let’s construct a grossly simplified scenario where we have Tshibaka (R) at 40%, Galvin (D) at 30%, Murkowski (R) at 30%, and Murkowski loses the second spot by a hair to Alyse Galvin. Now you go to the H2H...
Does Galvin get 66% of Murkowski’s voters to back her as the second choice? Possible...but a tall order...so you’ve just given Tshibaka a huge boost here.

Conversely, would 66% of Galvin’s voters rank Murkowski as a second choice? That’s much easier to imagine.
Read 11 tweets
22 Jun
If the goal of the Democratic Party is to retain the majority in 2022, then funding or helping Lisa Murkowski makes absolutely no sense, because the value-over-replacement she provides to a Senate Democratic majority is minimal.
If Democrats think they've certainly lost the Senate in 2022, then helping to keep Murkowski might make a lot of sense.

If they think they've got a good chance to retain the Senate (as they do, given the map they're playing in), then keeping Murkowski provides no utility at all.
Any bill Murkowski goes for, all Democrats would have already supported, including Manchin. There is no use to a Democratic *majority* here, especially when you have a lane to elect Galvin, who'd be at the party median and cut the reliance on Manchin/Sinema by a fair bit.
Read 4 tweets
22 Jun
Murkowski's 2022 odds are honestly not nearly as high as everyone thinks they are and I think it's not unreasonable to say that come November, she may not be the favorite to make it out of the field.

You can call Alaska likely/safe R, but it's not likely/safe Murkowski.
.@EScrimshaw breaks it down here, but because of the way RCV works, Tshibaka poses a very, very serious threat, especially given the amount of campaigning Trump will do for her against Murkowski.
scrimshawunscripted.substack.com/p/2022-murkows…
That *does* open up an outside lane for a Democrat (I think @ElpisActual has discussed it as well) in which you could have Tshibaka (R) at 40%, Galvin (D) at 30%, Murkowski at 25%, and a random Independent at 5%.

And Galvin could edge out Tshibaka in a H2H there with RCV.
Read 4 tweets
19 Jun
The best way to illustrate how the 2020 electorate was way more R-favorable than any recent election, midterm or presidential, is this:

2020 was D+4.46. Applying 2020 latent demographic partisanship and turnout to...
2010: D+7.7
2012: D+5.5
2014: D+4.8
2016: D+5.4
2018: D+8.1
Also if you like things like this go follow @notkavi and our bot @bot_2024 — kavi does a lot of great modeling and work and programmed the bulk of that bot.
As I said in the replies to the original thread, I think the lack of demographics available to our bot (because of a lack of data) makes this estimation a bit susceptible to favoring Dems too much in some elections, but the overall picture is largely correct.
Read 4 tweets
18 Jun
Let's put this in simpler terms.

The two-way vote share, per Catalist, was ~R+12 with white voters in 2020. The white vote in a midterm would probably be ~R+10 or thereabouts, if we adjusted for voting propensity and assumed zero vote switching.
That *does* help Democrats a bit! But to take advantage of it, you need to make sure your base turns out, and this is still prone to the issue that white college voters who are Democratic may turn out at different rate from white college voters that are Republicans.
This is not meant to be a hard and fast quantification of everything. It's just meant to show that there is a real, somewhat quantifiable educational turnout edge based on recent history for Democrats, and that they could certainly use this to their benefit.
Read 4 tweets

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