By Stephen Fisher, 3^rd November 2020.

Americans are evenly divided in their expectations of who will win today’s presidential election. YouGov found 40% expect Joe Biden to win, 39% think Donald Trump will win and 21% are “Not sure” (see p59 here). By contrast, in 2016 a similar question about expectations of the outcome from New York Times/CBS News polls here had 56% expecting Hilary Clinton to win and just 33% thinking Donald Trump would. Differences in question wording means we should be cautious about detailed comparison, but it appears that Americans collectively are much more unsure of the outcome of this election than they were last time.

They may be more cautious because the majority of them were wrong last time. Then their expectations were in line with the polls and the forecasters, some of whom were clearly over-confident for Hilary Clinton, as I argued before the fact. 

The problems of the polls in 2016 were diagnosed and methodology has improved. In particular, many polls are now weighting their samples so that the numbers of people with different levels of education are represented in proportion to their prevalence in the population. Part of the problem with some of the state-level polls last time was that they did not do so. Without that education weighting the polls this year would be showing even bigger Biden leads.

Some of the most over-confident forecasters from 2016 are no longer forecasting (including the Huffington Post), while others, such as Sam Wang, have adjusted their models and de-emphasised the predicted probabilities. 

Nate Silver’s FiveThirtyEight forecasts did best in 2016, mainly by having a model with more prediction uncertainty and so a larger probability of Clinton winning. Despite that, one of the most prominent debates about the election forecasts this year has been prompted by the superb statistician Andrew Gelman querying whether the FiveThirtyEight model has included too much uncertainty. 

Andrew Gelman is part of The Economist forecasting team. Their model has consistently had more confidence in a Biden win. It now gives Biden a 97% chance, to FiveThirtyEight’s 89% chance, of winning. Their central forecasts are extremely close, it is their uncertainty levels that differ.

More recently, Andrew Gelman has posted this on the effects of using Normal versus t distributions for the tail probabilities, and this on the value of learning from experience as a forecaster, and a fair few interesting posts about election forecasting in between. I can see the case for the modelling choices in The Economist model, but somehow the published level of uncertainty from FiveThirtyEight feels more reasonable to me. Since there will be sources of uncertainty that are not measurable from the polling and historical data, I think it is okay to err on the side of allowing more uncertainty when making choices between different justifiable modelling options. 

Another forecaster that has adapted their methods since 2016 is YouGov. Their MRP model forecast has a slightly higher forecast electoral college tally for Biden (364) than does The Economist (356) or FiveThirtyEight (348). The difference is based on a slightly larger predicted Biden lead in votes (9 points for YouGov compared with 8 points for FiveThirtyEight). 

YouGov has not published a predicted probability for a Biden win, but from their graph of simulations it seems that they have in the order of a 10% chance of a Trump win. That is roughly the same as FiveThirtyEight despite a lower central forecast for Trump. Moreover, in 2016 the corresponding YouGov model put very little probability on a Trump win despite a much closer forecast for the national vote share margin. The additional uncertainty estimation this time seems like an improvement. 

It is worth noting that whereas the FiveThirtyEight, Economist and similar models depend on a plethora of polls funded by others, YouGov build their model on their own polling data. They might do a very large poll, but the YouGov sample size would still pale by comparison with the cumulative sample size of all the other campaign polls at the national and state levels. YouGov’s ability to refine their methodology is important for understanding the potential for getting effective forecasts from more modest amounts of polling data.

Using perhaps the most data of all, PollyVote aggregate not only polls but models, betting markets, citizen expectations and expert forecasts. That website has very helpful descriptions of how the different data sources and models work with links to research on how they have fared in the past. Their overall combined forecast is for a Biden victory, but with a smaller margin than the polls and poll-based models point to. This is largely because citizens suggest the race is close (as discussed above) and the betting markets and experts are cautious about the polls. 

One model that points in completely the opposite direction from nearly all others is Helmut Norpoth’s Primary Model. It predicts a comfortable Trump victory because he is an incumbent president (they usually win) and because Biden did not do well in early primary contests, suggesting he is a weak candidate. The Primary Model has a very good track record of successful forecasts. It at least provides a basis for claiming that a Biden win would be remarkable by historical experience. 

Overall, the evidence from the (apparently improved) polls is a very strong indication that Biden will win, and win very comfortably. The dearth of media coverage of election forecasts and the cautious coverage of opinion polls this campaign, as a result of their failure to anticipate Trump’s victory in 2016, seems to have led to public uncertainty. If the polls are right, and especially if Biden does a bit better than they suggest, it will come as a shock to many, especially Republican voters who mainly expect Trump to win (see p59 here).

Where has the swing to Biden come from?

Evidence from the polls seems to be that partisan polarization between self-described liberals and Democrats on the one side and conservatives and Republicans on the other has worsened since 2016, with both groups more strongly backing their candidate than they did in 2016. What is helping Biden most is a big swing among those who describe themselves as independent and/or moderate. Since that big swing is mainly among whites and it is towards the Democrats the ethnic divide has narrowed as white vote intention moves closer to the strongly Democratic Latino and solidly Democratic Black vote.

At a macro level it looks like swing at the state level might be fairly uniform. The table below shows my quick calculations for the change in the lead in some of the larger more marginal states. The average lead for Biden in them is a bit bigger for YouGov than FiveThirtyEight, in keeping with the one-point larger lead YouGov expects in the national share of the vote. Relative to Hilary Clinton’s actual lead over Donald Trump in 2016, the predicted change this year is relatively similar in these states. There is variation between states in the extent of the swing expected, but the differences are not huge and they are not all consistent between the two forecasters. 

Table: FiveThirtyEight and YouGov predictions for selected swing states

	Biden lead		Biden lead – actual Clinton lead
	538	YouGov	538	YouGov
Arizona	2.6	5.4	6.1	8.9
Florida	2.5	4.3	3.7	5.5
Georgia	0.9	3.1	6.0	8.2
Michigan	8.0	7.9	8.2	8.1
North Carolina	1.7	2.4	5.4	6.1
Pennsylvania	4.7	6.8	5.4	7.5
Wisconsin	8.3	6.8	9.1	7.6
Average	4.1	5.2	6.3	7.4

That said, both YouGov and FiveThirtyEight expect bigger than average swings in Michigan and Wisconsin, and a smaller than average swing in Florida. This would be the same as in 2016 but in the opposite direction. I have not had time to analyse whether there is a broader pattern of negative correlation between 2012-16 swing and 2016-20 swing, but it would not be surprising if the places that swung most to Trump in 2016 swing most heavily away from Trump today. 

House of Representatives

The House is largely expected to stay Democrat controlled. FiveThirtyEight give the party a 97% chance of retaining control. In 2018, the Democrats won 235 seats to the Republicans 199 on an 8.6 point lead in the popular vote, quite a margin to overturn. Currently, the Real Clear Politics generic congressional vote poll average has a Democrat lead of 6.8 points. Similarly, FiveThirtyEight predict a popular vote margin of 6.3 points for the Democrats. Since this is down what it was on 2018 it is remarkable that the Democrats are also predicted to win slightly more seats (239). 

If this comes to pass, I for one will be wondering whether the failure of a swing to the Republicans to yield any net seat gain will be due to incumbency advantage, especially “sophomore surges” given that most of the Republican target seats are being defended by first-term Democrat incumbents. 

While a swing to the Republicans in the House without net Republican seat gains would seem to be a sign of problematic unresponsiveness of the electoral system, it would represent a partial unwinding of a pro-Republican bias in the distribution of seats given votes and so arguably a sign of the system working more fairly.

Senate

There is much more uncertainty as to whether the Senate will flip from Republican to Democrat. Of the 35 Senate seats up for election this time 21 are begin defended by Republican incumbents, and incumbency effects in US Senate elections are very strong. However, the seats up this time were last fought in 2014, which was a midterm year when Obama was president. The big c.9.7 point swing to the Republicans then appears to be being partially undone. Also, the Democrats have more of the seats not up for election: 35 out of 65 including two independents caucusing with the Democrats.

Sam Wang’s Princeton Election Consortium is predicting the Senate to go 53 D 47 R, while Fivethirtyeight’s central prediction is 51.5 D 48.5 R, with a 75% chance of Democratic control. 

Overall, based on the polling evidence the Democrats are likely win control all three branches of government, with the Senate the least certain to go their way.

Further links:

PS: Political Science & Politics academic forecasts (from September 2020) and related articles: https://www.cambridge.org/core/journals/ps-political-science-and-politics/2020-presidential-election-forecasting-symposium

The review, by Alfred Cuzán, of previous forecasts in that series is especially worth a read.

Pluralvote.com claims to have a powerful model based on polls and media tracking that improves on polls only. 

Patrick English has a very confident model for Biden here.