After months of speculation about who will run, many debates, and discussion of candidates’ chances, tomorrow, February 1st, we get to learn the first results of the election cycle as Iowa heads to the caucuses tomorrow evening. Although we will be providing rolling analysis as results come in tomorrow, we have also put together this article to help you know what to watch for tomorrow.
Arguably the most important thing to watch for tomorrow is how the candidates do relative to expectations. The first way to track expectations is by looking at win probabilities; if a candidate who is given a small chance of winning the caucuses wins, that win will mean much more than an expected win. FiveThirtyEight has put together what they call a “polls-plus” model1 to assign a probability of winning Iowa to each candidate. PredictWise uses betting market data to derive a similar probability for each candidate.
These two methods produce fairly similar results, but both are worth noting. FiveThirtyEight’s probabilities represent what the polls are showing may happen, so those who follow polling closely will build their expectations to be similar to that model’s. On the other hand, PredictWise uses betting markets, so those probabilities more closely represent the “common wisdom” of what will happen. Because the “common wisdom” is affected so much by media portrayals and how the media spins Iowa will be crucial, it is especially worth paying attention to.
So, what do these probabilities actually tell us? Well, on the Republican side they tell us mainly that Donald Trump is a favorite to win the caucuses. That means that Trump needs to win Iowa or else his campaign will be seen as falling apart. Similarly, despite close polling between Hillary Clinton and Bernie Sanders, Clinton is the overwhelming favorite to win Iowa. However, because she has only a small lead in the polls, a loss by Clinton would probably not affect her as much as a loss by Trump would affect him.
In addition to looking at the probabilities of winning, another key part of playing the expectations game is how a candidate does relative to their polling. To understand this effect, we looked at all candidates in the last three cycles (2004, 2008, 2012) and averaged the three Iowa polls released closest to the Iowa caucuses. We then compared these to actual results of Iowa result to get an “Iowa Result against Expectation.”2 We did a similar three poll average of New Hampshire polls prior to Iowa held their caucuses and a three poll average of New Hampshire for the days after Iowa held their caucuses. This allows us to detect a change in New Hampshire’s polling which can, in part, be attributed to the results in Iowa3. Using this data4, we created the following chart. In this chart, the dot size indicates the actual share of Iowa vote that each candidate won.
While not incredibly strong, there is clearly a positive correlation between Iowa results against expectation and changes in New Hampshire polls. By looking in the upper left quadrant, it is also very clear that candidates who beat expectations significantly and win a fairly large share of the vote tend to get the strongest boost in New Hampshire. However, to quantify this relationship a bit more, we can run a linear regression through this data.
By running this linear regression, we find that a candidate who beats expectations by 1 percentage point in Iowa can expect, on average, to improve their standing in New Hampshire by 0.583 percentage points5. So, the effect is not huge, but it is distinct. Certainly, Trump’s and Sanders’ leads in New Hampshire polling are large enough that it seems unlikely for this boost from Iowa to propel anyone ahead of them. However, in the Republican race, Ted Cruz, John Kasich, Marco Rubio, Jeb Bush, and Chris Christie are all so tightly packed that a stronger than expected showing in Iowa (or a weaker than expected one) could change the race significantly.
So, tomorrow when the results start to come in, while everyone will be watching mainly to see who wins, make sure to pay attention to how the candidates perform relative to current polling in order to see how New Hampshire and the rest of the race may shake up. And, of course, join us for our liveblog!
Footnotes [ + ]
|1.||↵||Their model incorporates state polling, endorsements and national polling. For their full methodology, check here.|
|2.||↵||For example, an average of the last three polls before Iowa showed Rick Santorum sitting at 17% in 2012. Santorum ended up winning 24.6%, so his result against expectation is 24.6-17=+7.6%. On the other hand, Michelle Bachmann was polling at a 7% average before Iowa and ended up winning 5%, so her result against expectation is 5-7=-2%.|
|3.||↵||Certainly, there are many potential confounding factors, but because we are limiting the number of days between the first NH average and the second, we hope to cut down on as many of these as possible.|
|4.||↵||Which is available in whole in this spreadsheet.|
|5.||↵||We also find that by comparing pre-Iowa polling in New Hampshire to the New Hampshire results, beating expectations by 1 percentage point improves standing in New Hampshire on average by 0.55 percentage points, meaning the effect possibly wears off a bit. However, we decided that the difference in time over the three cycles between Iowa and New Hampshire made the methodological inconsistency too great and so we stick to pre-Iowa and post-Iowa polling in New Hampshire.|