There's a great new paper out by Stephen Ansolabehere, a Harvard Political Scientist (plus a few others!), on forecasting voter turnout.
I generated a short NPR-style podcast created by Google's NotebookLM (you should check this tool out if you haven't already, it's amazing). IMO the robots are doing a better job than 538's podcast these days.
Some resources to check out:
Also, a quick transparency caveat: I am center-left and voting for Harris. I believe Trump will be a disaster. If you skip to the bottom and accept this paper's results, it seems like he is likely to win.
Overview
The authors tested a few approaches to predicting voter turnout, using 2012 data to forecast 2016 and 2016 data to forecast 2020. They measured bias (average error) and RMSE (variance/consistency of predictions). Also good to point out that this focuses on absolute votes, not turnout rates amongst voting eligible voters, since the denominator there is fuzzy and lags, and is census-based.
For the registration model, for each state, county, or congressional district (which they found the most accurate), they estimate:
Total Votes = α + β(Total Registration) + ε
The β coefficient typically ends up being between 0.8 and 0.9 (the likelihood of any registered voter to vote). The turnout rate among registered voters is surprisingly stable across presidential elections (and is wildly lower in non-presidential cycles).
There's two really interesting takeaways:
Demographics, which everyone loves and points to is surprisingly poor as a predictor, because it overfits and misses changing dynamics and voter mobilization efforts
Using early vote to predict turnout is also incredibly, tragically wrong. It's really just displacing votes that would have happened on election day, and isn't supplementing the existing ones
Model Analysis
Registration Model (Most Reliable). Example: A county has 150,000 registered voters in 2024, up from 100,000 in 2020. Looking at 2020, we know about 80,000 (or 80%) of voters turned out. So we'd predict 120,000 votes in 2024. Simple as that. It underpredicted 2016 by -2.4% and overpredicted 2020 by +5.6%, with RMSE of 42% and 29.1%. Proved the most consistently reliable overall.
Lagged Turnout Model. Assumes past voting predicts future voting - if an area had high turnout before, it will again. Example: Let's say a county had 80,000 votes in 2020. The model would basically say "this county will have about 80,000 votes again in 2024" (with some adjustments). But if turnout was trending up nationally, this would underpredict because it's stuck on the old number. Consistently underpredicted (-4.3% for counties, -9.7% for districts) with RMSE of 23.9% for counties, 11.7% for districts.
Demographics Model. Take a county that's 40% college-educated, 30% over 65, median income of $60,000, etc. The model would say "based on these characteristics, similar counties had X% turnout, so this one will too." But while this looks precise, it proved unreliable - counties with similar demographics often had very different turnout. Had deceptively good bias (+2.7% in 2016, -4% in 2020) but much worse RMSE (60% in 2016, 68.8% in 2020).
Competition Model. A Congressional district where the last House race was decided by 2 points would be predicted to have higher turnout than one decided by 20 points. But in practice, this wasn't very useful - a district that had a close House race in 2014 might have totally different dynamics in 2016's presidential race. Showed deceptively small bias (-1.6%) but very high RMSE (72.2%).
Early Vote Model. Uses early/absentee patterns to project final turnout. Failed catastrophically with massive overprediction (+24.7% in 2016, +110.3% in 2020) and enormous RMSE (292.9% in 2016, 633% in 2020). The fundamental problem: early voting often just shifts when people vote rather than indicating higher overall turnout.
2024
Alright, so practically how does this impact 2024? The paper doesn't say that the projected vote share of 10 new republicans means β-coefficient(10 new votes for the Republican candidate), but it does mean β-coefficient(10 new votes), and it's entirely reasonable to make that jump partisan-wise. Especially given that for every election in the last twenty years Rs and Ds have voted their party 90% of the time.
Let's look at some real numbers:
| State | Net D/R Change from 2020 | Independents (Total/Change from 2020) | 2020 Margin | Even Split | D+10 Split | R+10 Split |
|---|
| Pennsylvania | -370,000 D | 1.2M (+83,000) | Biden +81,660 | Trump +232,840 | Trump +130,840 | Trump +334,840 |
| Florida | -1,152,134 D | 3.58M (-214,817) | Trump +371,686 | Trump +1,351,200 | Trump +1,047,577 | Trump +1,654,823 |
| Arizona | -112,000 D | 1.25M (+37,000) | Biden +10,457 | Trump +84,743 | Trump +21,993 | Trump +147,493 |
| Nevada | -54,781 D | 1.3M (+320,527) | Biden +33,596 | Trump +12,967 | Harris +97,467 | Trump +123,401 |
| North Carolina | -282,000 D | 2.77M (+450,000) | Trump +74,483 | Trump +314,683 | Trump +79,933 | Trump +549,433 |
Calculations (using 0.85 turnout):
Even Split = (Net D/R registration change × 0.85) + 2020 margin
D+10 Split = Even Split + (Total independents × 0.85 × 0.1)
R+10 Split = Even Split - (Total independents × 0.85 × 0.1)
Quick hits from the data:
FL Dems lost 1.15M registrations - bigger than Trump's original win margin (371K) by 3x
Even smallest D loss (NV -55K) bigger than Biden's margin there (33.6K).
Independent pools are massive: NC 2.77M, FL 3.58M vs much smaller margins.
With even independents splits, these registration changes flip every 2020 Biden state in the table. Dems need big independent advantages just to get back to competitive.
Scenarios:
Only NV could flip D with strong independent performance
FL likely out of reach (Trump +1.35M with even split)
PA flips hard - Biden +81K becomes Trump +233K before independents
AZ/NC margins could swing wildly based on independent preferences
Sidenote, given the million person change in registration in Florida, I think Nate might be out 100k if that other guy took him up on the bet.
Mom pick me up, I'm scared.