Cross-posted at Daily Kos Elections
Over the course of this presidential campaign, Daily Kos Elections has logged 1,371 state-level presidential polls into our database. All signs point to a Hillary Clinton victory.
Our forecasting model indicates that Clinton is highly likely to win key states including Colorado, Pennsylvania, New Hampshire, Virginia, and Wisconsin. In all five of these states, Clinton has never trailed in our average of the polls—and if she carries all of them, she would win the election over Donald Trump with 273 electoral votes, three more than the 270 required for victory. In addition, our model also favors Clinton in Florida, North Carolina, and Nevada. Together, those states contribute another 50 electoral votes.
That gives us our final prediction: Clinton 323 electoral votes, Trump 215.
Given that the forecast is based almost entirely on public polling data, how much can we trust the accuracy of the polls? As recently as one week ago, Clinton held such a commanding lead that our model placed her chances of winning as high as 96 percent. Since then, the race has tightened, and we currently estimate Clinton’s odds of victory at 88 percent. That’s enough of a change that a large and consistent polling error could make the difference for Trump. But the error would have to be very large, and very consistent. Going into Election Day, Clinton’s average lead in the polls is 3 points in New Hampshire, 4 points in Colorado and Pennsylvania, and 5 points in Wisconsin and Virginia.
Polling is never perfect, but systematic errors across multiple states in the same presidential election are historically not that large, or that common. Instead, the state-level errors form a distribution: In some states, one candidate outperforms the polls, and in other states, the other candidate does better. For example, in 2012, on average, the polls underestimated Obama’s vote share by a small amount; nevertheless, in 22 states, his polling was higher than his eventual vote share. Polling errors are less “correlated” across states than you might expect.
What about the magnitude of the state-level polling errors? Aggregating public polls usually produces forecasts that are very close to the actual outcome, especially in competitive states where pollsters have conducted larger numbers of polls. Again using 2012 as an example, there were 15 close states where a candidate won by 10 points or fewer (counting only the major-party vote). In seven of those states, the polls accurately predicted the margin of victory to within 1 percentage point. In another three states, the polls missed the actual margin of victory by under 2 points, and in four states, the polls were off by between 2 and 3 points. In only one state did the polls miss the margin by more than 3 points. And to reinforce our point above about correlated polling errors, Obama outperformed his polls in eight of the 15 close states; in the other seven states, Romney did better than expected.
So, while it’s possible for Trump to defy the polls and win the election, it is not likely. Our model estimates Trump’s chances at around 12 percent.
Stepping away from the polling data, there are reasons to think that the probability of a Trump victory isn’t even this high. None of these other factors are formally built into our model, and I haven’t analyzed them in any systematic or historical context, but consider everything below here informed conjecture. My Daily Kos colleague Stephen Wolf also examined some of these factors, and others, in a recent post exploring why the polls could be off.
First, our forecasting model takes the public polls essentially at face value: We apply a slight adjustment to polls conducted by partisan pollsters, and we make a few assumptions about how quickly to assimilate new polling data and how much to infer state trends from national trends. But we have no way to account for phenomena like differential partisan nonresponse, which may be responsible for the seemingly large swings in the presidential polls this year. If, contrary to some of the raw polling data, public opinion has been as stable as recent research suggests, then some of the more sophisticated online tracking surveys, like those from YouGov, NBC/SurveyMonkey, and Google—all of which have shown Clinton with a consistent lead—might have it right.
Our model also does not incorporate data on early voting, beyond what is implicitly captured by polls that include respondents who have already voted. Although there is disagreement about how much should be read into early vote totals, one state stands out: Nevada. Heavy Latino turnout in the Nevada early voting period appears to have put a significant dent in Trump’s chances of winning there—a must-win state for him where polls alone suggest he has at least a one-in-three chance of winning.
Related to this, there are a variety of reports indicating a large discrepancy in the size and quality of the Clinton and Trump campaigns’ voter turnout operations. In short, Clinton enjoys a significant advantage. Research suggests that her superior “ground game” could be worth up to 1 to 3 percent of the vote. This will not be picked up in the polls.
Finally, although the “fundamentals” of the presidential election have long been factored out of our forecast in favor of newer polling data, two key structural factors have actually gotten better for Clinton over the course of the campaign: President Obama’s job approval rating is on an upswing, and the national economy is growing at a faster rate than when we first accounted for these factors back in June.
There is one last caveat that gives me pause: The number of voters telling pollsters that they are still undecided, or are intending to vote for a third-party candidate, remains unusually high. We know that these respondents are disproportionately younger, white voters who would otherwise be likely to support Hillary Clinton. But we have no way of knowing for sure how these individuals will vote, or if they will turn out to vote at all. It’s something that I will be looking out for on Tuesday.
Happy Election Day.