I think this is all very reasonable and I have been working under the assumption of one votes in PA leading to a 1 in 2 million chance of flipping the election. That said, I think this might be too conservative, potentially by a lot (and maybe I need to update my estimate).
Of the past 6 elections 3 were exceedingly close. Probably in the 95th percentile (for 2016 & 2020) and 99.99th percentile (for 2000) for models based off polling alone. For 2020 this was even the case when the popular vote for Biden was +8-10 points all year (so maybe that one would also have been a 99th percentile result?). Seems like if the model performs this badly it may be missing something crucial (or it’s just a coincidental series of outliers).
I don’t really understand the underlying dynamics and don’t have a good guess as to what mechanisms might explain them. However, it seems to suggest that maybe extrapolating purely from polling data is insufficient and there’s some background processes that lead to much tighter elections than one might expect.
Some incredibly rough guesses for mechanisms that could be at play here (I suspect these are mostly wrong but maybe have something to them):
Something something polarization, steady voting blocs for Rep & Dem aren’t shifting much year to year. This means we should expect similar margins this year as 2016 & 2020.
Some balancing out process where politicians are adjusting their platform, messaging, etc to react to their adversary and this ends up increasing how close elections get.
Maybe something where voters have local information on whether the person they don’t like is more likely to win and they then feel more motivated to vote? Turns out, in aggregate, this local information is pretty accurate and leads to tighter-than-expected elections.
Maybe political parties/donors observe how much their adversary spends in a given state and are consistently able to spend to counteract their efforts. This maybe provides a balancing effect that tightens the race. This would have the unfortunate consequence that visible spending is much less effective—but maybe implies that smaller, more under-the-radar, projects are better.
Thanks for those thoughts! Upvoted and also disagree-voted. Here’s a slightly more thorough sketch of my thought in the “How close should we expect 2024 to be” section (which is the one we’re disagreeing on):
I suggest a normal distribution with mean 0 and standard deviation 4-5% as a model of election margins in the tipping-point state. If we take 4% as the standard deviation, then the probability of any given election being within 1% is 20%, and the probability of at least 3⁄6 elections being within 1% is about 10%, which is pretty high (in my mind, not nearly low enough to reject the hypothesis that this normal distribution model is basically right). If we take 5% as the standard deviation, then that probability drops from 10% to 5.6%.
I think that any argument that actually elections are eerily close needs to do one of the following:
Say that there was something special about 2008 and 2012 that made them fall outside of the reference class of close elections. I.e. there’s some special ingredient that can make elections eerily close and it wasn’t present in 2008-2012.
I’m skeptical of this because it introduces too many epicycles.
Say that actually elections are eerily close (maybe standard deviation 2-3% rather than 4-5%) and 2008-2012 were big, unlikely outliers.
I’m skeptical of this because 2008 would be a quite unlikely outlier (and 2012 would also be reasonably unlikely).
Say that the nature of U.S. politics changed in 2016 and elections are now close, whereas before they weren’t.
I think this is the most plausible of the three. However, note that the close margins in 2000 and 2004 are not evidence in favor of this hypothesis. I’m tempted to reject this hypothesis on the basis of only having two datapoints in its favor.
(Also, just a side note, but the fact that 2000 was 99.99th percentile is definitely just a coincidence. There’s no plausible mechanism pushing it to be that close as opposed to, say, 95th percentile. I actually think the most plausible mechanism is that we’re living in a simulation!)
I think it’s very reasonable to say that 2008 and 2012 were unusual. Obama is widely recognized as a generational political talent among those in Dem politics. People seem to look back on, especially 2008, as a game-changing election year with really impressive work by the Obama team. This could be rationalization of what were effectively normal margins of victory (assuming this model is correct) but I think it matches the comparative vibes pretty well at the time vs now.
As for changes over the past 20+ years, I think it’s reasonable to say that there’s been fundamental shifts since the 90s:
Polarization has increased a lot
The analytical and moneyball nature of campaigns has increased by a ton. Campaigns now know far more about what’s happening on the ground, how much adversaries spend, and what works.
Trump is a highly unusual figure which seems likely to lead to some divergence
The internet & good targeting have become major things
Agree that 5-10% probability isn’t cause for rejection of the hypothesis but given we’re working with 6 data points, I think it should be cause for suspicion. I wouldn’t put a ton of weight on this but 5% is at the level of statistical significance so it seems reasonable to tentatively reject that formulation of the model.
Trump vs Biden favorability was +3 for Trump in 2020, Obama was +7 on McCain around election day (average likely >7 points in Sept/Oct 2008). Kamala is +3 vs Trump today. So that’s some indication of when things are close. Couldn’t quickly find this for the 2000 election.
I think this is all very reasonable and I have been working under the assumption of one votes in PA leading to a 1 in 2 million chance of flipping the election. That said, I think this might be too conservative, potentially by a lot (and maybe I need to update my estimate).
Of the past 6 elections 3 were exceedingly close. Probably in the 95th percentile (for 2016 & 2020) and 99.99th percentile (for 2000) for models based off polling alone. For 2020 this was even the case when the popular vote for Biden was +8-10 points all year (so maybe that one would also have been a 99th percentile result?). Seems like if the model performs this badly it may be missing something crucial (or it’s just a coincidental series of outliers).
I don’t really understand the underlying dynamics and don’t have a good guess as to what mechanisms might explain them. However, it seems to suggest that maybe extrapolating purely from polling data is insufficient and there’s some background processes that lead to much tighter elections than one might expect.
Some incredibly rough guesses for mechanisms that could be at play here (I suspect these are mostly wrong but maybe have something to them):
Something something polarization, steady voting blocs for Rep & Dem aren’t shifting much year to year. This means we should expect similar margins this year as 2016 & 2020.
Some balancing out process where politicians are adjusting their platform, messaging, etc to react to their adversary and this ends up increasing how close elections get.
Maybe something where voters have local information on whether the person they don’t like is more likely to win and they then feel more motivated to vote? Turns out, in aggregate, this local information is pretty accurate and leads to tighter-than-expected elections.
Maybe political parties/donors observe how much their adversary spends in a given state and are consistently able to spend to counteract their efforts. This maybe provides a balancing effect that tightens the race. This would have the unfortunate consequence that visible spending is much less effective—but maybe implies that smaller, more under-the-radar, projects are better.
Thanks for those thoughts! Upvoted and also disagree-voted. Here’s a slightly more thorough sketch of my thought in the “How close should we expect 2024 to be” section (which is the one we’re disagreeing on):
I suggest a normal distribution with mean 0 and standard deviation 4-5% as a model of election margins in the tipping-point state. If we take 4% as the standard deviation, then the probability of any given election being within 1% is 20%, and the probability of at least 3⁄6 elections being within 1% is about 10%, which is pretty high (in my mind, not nearly low enough to reject the hypothesis that this normal distribution model is basically right). If we take 5% as the standard deviation, then that probability drops from 10% to 5.6%.
I think that any argument that actually elections are eerily close needs to do one of the following:
Say that there was something special about 2008 and 2012 that made them fall outside of the reference class of close elections. I.e. there’s some special ingredient that can make elections eerily close and it wasn’t present in 2008-2012.
I’m skeptical of this because it introduces too many epicycles.
Say that actually elections are eerily close (maybe standard deviation 2-3% rather than 4-5%) and 2008-2012 were big, unlikely outliers.
I’m skeptical of this because 2008 would be a quite unlikely outlier (and 2012 would also be reasonably unlikely).
Say that the nature of U.S. politics changed in 2016 and elections are now close, whereas before they weren’t.
I think this is the most plausible of the three. However, note that the close margins in 2000 and 2004 are not evidence in favor of this hypothesis. I’m tempted to reject this hypothesis on the basis of only having two datapoints in its favor.
(Also, just a side note, but the fact that 2000 was 99.99th percentile is definitely just a coincidence. There’s no plausible mechanism pushing it to be that close as opposed to, say, 95th percentile. I actually think the most plausible mechanism is that we’re living in a simulation!)
I think it’s very reasonable to say that 2008 and 2012 were unusual. Obama is widely recognized as a generational political talent among those in Dem politics. People seem to look back on, especially 2008, as a game-changing election year with really impressive work by the Obama team. This could be rationalization of what were effectively normal margins of victory (assuming this model is correct) but I think it matches the comparative vibes pretty well at the time vs now.
As for changes over the past 20+ years, I think it’s reasonable to say that there’s been fundamental shifts since the 90s:
Polarization has increased a lot
The analytical and moneyball nature of campaigns has increased by a ton. Campaigns now know far more about what’s happening on the ground, how much adversaries spend, and what works.
Trump is a highly unusual figure which seems likely to lead to some divergence
The internet & good targeting have become major things
Agree that 5-10% probability isn’t cause for rejection of the hypothesis but given we’re working with 6 data points, I think it should be cause for suspicion. I wouldn’t put a ton of weight on this but 5% is at the level of statistical significance so it seems reasonable to tentatively reject that formulation of the model.
Trump vs Biden favorability was +3 for Trump in 2020, Obama was +7 on McCain around election day (average likely >7 points in Sept/Oct 2008). Kamala is +3 vs Trump today. So that’s some indication of when things are close. Couldn’t quickly find this for the 2000 election.