In the worlds of polling and data analytics, there should be a reassessment of how we can accurately determine who is likely to vote. Relying on vote-history data from voter files, rather than often-erroneous self-reports of voting history, is an essential element missing in most media polling. But leaning too heavily on data from previous elections can lead us to miss what’s different about this one. For instance, it wasn’t a given that Hillary Clinton would be able to reenergize the Obama coalition. Meanwhile, conventional likely-voter “screens” — the way pollsters try to filter out nonvoters and improve the representation of actual voters in a sample — have failed in two election cycles in a row, giving Republicans false hope in 2012 and doing the same for Democrats in 2016.
We also may need to look beyond polls alone for answers. Just as you shouldn’t fly a plane until multiple systems are checked and rechecked, we shouldn’t rely on polls alone to tell us how we’re doing. Indeed, there were signs this year that the Democrats’ electoral map was more fragile than the polls made it appear to be.
While the polls gave conflicting signals about the state of play in the upper Midwest — showing Iowa and Ohio leaning toward Trump, and Michigan and Wisconsin toward Clinton — demographically, these states are not very different, each having a comparatively high share of white voters without college degrees. Demographic modeling by the likes of David Wasserman of the Cook Political Report provided a more accurate view, showing the Rust Belt poised to move solidly toward Trump. Nationally, our own demographic model, relying on factors such as the percentage of white voters without college degrees, outperformed state polling averages in anticipating which states Trump had a chance of flipping on his way to an electoral college upset.
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