Who elected Trump?
We started with a question: which demographic got Donald Trump elected? From the analysis I have done, it appears straightforward: disengaged voters, primarily noncollege, primarily white people who voted for a Democrat in the past (Obama), but didn’t show up to vote the same way for Hillary (or voted Trump). Often, these people reside in places hit hard by declining American manufacturing, where noncollege high-wage jobs (like mining) are disappearing.
It is imperative for Democrats to figure out what wins those voters back. While it was trendy after the election to compare small loss margins to other small factors, like marginal changes in minority voting or likely voter suppression numbers, the vanished cohort of Obama voters dwarfs those other estimates across many battleground states. If a tiny factor (like marginal minority voting) is theoretically important, something 5-15x as big is much more important.
Why does this matter?
Focusing on what matters
This conclusion is important because it shuts off distractions. While it may feel good and important to rail against racist Republican voters, they are not the ones that made the biggest difference to turn the tide in key states; in several states, including Iowa and Wisconsin, the Republican vote was almost totally unchanged. Indulging those feelings at the expense of building a platform and a party that can win back lost voters may be a recipe for disaster.
Winning back votes
Further, because appealing to disengaged noncollege voters is important, it tells Democrats what to emphasize and what to discourage. Democrats should place more of a premium on candidates that can go to neutral/hostile places and persuade, and less on the candidates that merely reinforce the feelings of committed Democratic voters. Elizabeth Warren, Bernie Sanders, and Pete Buttigieg at least have gone to non-friendly territory and been successful. Democrats should also discourage candidates that drive away uncommitted/swing voters with more ardent appeals to the base or attacks to stay in the race; this part may be increasingly relevant as some candidates slide in popularity, and would harm the party’s overall prospects to keep a small chance in the race for themselves.
I’ve generally avoided speculation on the “why” of people’s votes. This is intentional: more than anything, I am advocating for engaging with reality, instead of a fiction intended to model reality. In “Shattered”, a book reporting about the internals of the Clinton campaign, senior staff like Robby Mook were famously enamored of a model of the world instead of the world as it was:
“Our analytics models were just really off. Time to go back to traditional polling. This happened in the primaries as well. They just put too much faith in analytics. We did not do any tracking by pollsters for the last month. Just maddening.” - Shattered
Democrats must not make the same mistake again, in a campaign or in everyday planning. Instead of hypothesizing, conjecturing, and guessing based on what “sounds right”, politicians and voters alike must be in touch with facts: what happened, what changed, what impact a given choice actually has. In a world of overabundant information, it is inexcusable to not consider the data.
One example to consider is this recent study. The authors found that 9% of the Obama coalition flipped to Trump, and that those people generally felt racism was not a big problem in the US and illegal immigration was a serious problem. If we believe 50% of the party is die-hard Democrats, then 9% of the total means roughly 1⁄5 of the marginal voters might react badly to strong racism-based messaging. Democrats discard those votes at their own peril.
Dems should not merely support the people that feel best to them specifically. They should support the candidates that can win the most votes, especially the votes that could have won the 2016 election.
Deeper, clearer analysis
I created this project in my spare time, with no budget, because I was deeply dissatisfied with the quality of analysis being passed around about the 2016 election. I wanted to get the best understanding I could from the data, figure out which explanations for the election didn’t make any sense, and make that clear + accessible to anyone with a computer. To make it easier for other people to do the same, the code for this site is open source in this github repo..
My secret hope is that if I can do this with no resources outside work hours, I can motivate the professionals to step up their game. Fox, CNN, and the New York Times have more budget, staff, and resources than I do, and I want to see them produce analysis more like this, but better. Liberal or conservative, we don’t have the time for shallow, basic attentionmongering when there are real problems that merit deep understanding.
If you are from those institutions and want to do this kind of thing better, you can put your email in the free-text part of my feedback poll and I’ll teach you ;) .