On the “reasons why” side, Nate Silver gave a long rundown explaining that the amount of people with a college education in a community had a whole lot to do with whether a place voted for Dem Hillary Clinton or GOP Donald Trump. Silver first noted that highly educated communities shifted hard towards Clinton- to the point that the election would seem poised to be a Dem landslide if you only looked at this group of people.
I took a list of all 981 U.S. counties with 50,000 or more people and sorted it by the share of the population that had completed at least a four-year college degree. Hillary Clinton improved on President Obama’s 2012 performance in 48 of the country’s 50 most-well-educated counties. And on average, she improved on Obama’s margin of victory in these countries by almost 9 percentage points, even though Obama had done pretty well in them to begin with….This trend held up regardless of whether the county is Dem-leaning or GOP-leaning. In Wisconsin, two counties qualified under this top 50 best-educated list- us socialist hippies in Dane County and the dead-red Charles Sykes types in Ozaukee County. I'll also throw in the county with the 3rd-highest percentage of people 25+ with a college degree, Waukesha County (aka the Bizarro World Heart of WisGOP). In all three cases, the counties shifted toward Hillary Clinton.
Although they all have highly educated populations, these counties are otherwise reasonably diverse. The list includes major cities, like San Francisco, and counties that host college towns, like Washtenaw, Michigan, where the University of Michigan is located. It also includes some upper-middle-class, professional counties such as Johnson County, Kansas, which is in the western suburbs of Kansas City. It includes counties in states where Clinton did poorly: She improved over Obama in Delaware County, Ohio, for example — a traditionally Republican stronghold outside Columbus — despite her numbers crashing in Ohio overall. It includes extremely white counties like Chittenden County, Vermont (90 percent non-Hispanic white), and more diverse ones like Fulton County, Georgia, where African-Americans form the plurality of the population. If a county had high education levels, Clinton was almost certain to improve there regardless of the area’s other characteristics.
Dem vs GOP presidential margin, 2012 vs 2016
2012 Dem +43.5
2016 Dem +48.0 (Dem +4.5)
2012 GOP +30.3
2016 GOP +19.3 (Dem +11.0)
2012 GOP +34.5
2016 GOP +28.1 (Dem +6.4)
That seems like a recipe for a Dem blowout. So how did Wisconsin end up red on Election Night and why are we faced with the prospect of (shudder) President Trump? Because Silver points out that if a county had a lot of non-college educated people, it was very likely to shift towards Trump.
These results are every bit as striking: Clinton lost ground relative to Obama in 47 of the 50 counties — she did an average of 11 percentage points worse, in fact. These are really the places that won Donald Trump the presidency, especially given that a fair number of them are in swing states such as Ohio and North Carolina. He improved on Mitt Romney’s margin by more than 30 points (!) in Ashtabula County, Ohio, for example, an industrial county along Lake Erie that hadn’t voted Republican since 1984.And this is true to stunning levels in Wisconsin, as the 10 counties with the lowest amount of people with college degrees shifted to Trump by an average of 27.6%. Here’s that amazing stat and the counties included in graph form.
There was another subset of counties that Trump did very well in- majority-white places with relatively high incomes and low education levels. These are largely exurbs of big cities in the North, and the Twin Cities area was especially susceptible.
Trump improved on Romney’s performance in 23 of 30 counties where median incomes are $70,000 or higher but less than 35 percent of the population have college degrees and the majority of the population is white. For example, Trump won by a much larger margin than Romney in Calvert County, Maryland, which has some commonalities with Long Island. And he substantially improved on Romney’s performance in Chisago County, Sherburne County and Wright County in the Minneapolis exurbs, even though Clinton made major gains in Minneapolis’ Hennepin County. There’s probably some degree of cultural self-sorting at play here. These communities have plenty of nice homes and good schools — they’re not cheap to live in — but they have fewer cultural amenities or pretensions (think big-box retail as opposed to boutiques) than you usually find in nearer-in suburbs and small towns such as those in Westchester County.St. Croix County, Wisconsin is also on this list, another Twin Cities exurb with plenty of rural in its eastern half. St. Croix went from Romney +12.3 to Trump 18.4, a notable shift, although one that’s actually below the current 7.8% swing to the GOP president in Wisconsin for 2016.
Silver notes that education levels seemed to be a clear indicator in 2016’s vote, and that it has changed what a “red” or “blue” area might be.
In short, it appears as though educational levels are the critical factor in predicting shifts in the vote between 2012 and 2016. You can come to that conclusion with a relatively simple analysis, like the one I’ve conducted above, or by using fancier methods. In a regression analysis at the county level, for instance, lower-income counties were no more likely to shift to Trump once you control for education levels. And although there’s more work to be done, these conclusions also appear to hold if you examine the data at a more granular level, like by precinct or among individual voters in panel surveys.And this difference in votes by education is why Silver was on Twitter yesterday saying that he didn’t think Wisconsin’s vote was hacked, as he produced regressions showing that education and race differences explained almost all of the voting disparities between Wisconsin counties with paper ballots and counties with electronic voting machines.
A lot of what Silver says adds up, but I don’t think it answers the questions that nag me as I look at the Wisconsin data from this election. The shifts in rural Western and Central Wisconsin are the big flag for me, and given that Trump’s “lead” in the state has already dropped from over 27,000 to 22,500 with only a few counties finalized, are we sure those are the right numbers? And somehow 130,000 fewer ballots were cast for president in Wisconsin compared to 2012 after record early voting??? That doesn’t add up to me. Besides, we already have a recount going on in Western Wisconsin in the Jen Shilling- Dan Kapanke State Senate race. It won’t take too much extra time and effort for to have those same people take a look at the presidential race in that area, so why not have the Clinton campaign or the Wisconsin Dems ante up for the additional presidential recount and see if there’s a discrepancy?
After all, Vernon and Crawford Counties were 2 of the 5 counties that Russ Feingold won, but Hillary Clinton allegedly lost on November 8, so this would be good information to confirm. If the numbers add up, then I’m going to be willing to shrug and admit that Trump won the state. But if there are weird numbers near La Crosse, Viroqua, and Prairie du Chien, then it throws the rest of the state figures into doubt, and that seems important to know before the state’s 10 electoral votes are officially cast next month. Or maybe my skepticism is a reflection of me not wanting us to slide into a fascist Idiocracy, where uneducated, resentful masses elect ignorant and arrogant politicians who have no respect for the institutions they control or the people they govern, and they steamroll over the more educated, decent people.
Sure that’s an elitist statement, but doesn’t knowledge, respect and a bigger-picture mentality seem like something that should be valued in governance? Those qualities seem like the last thing we are on the verge of getting if these 2016 election results hold up, and that goes double for overly white, less-educated places like Wisconsin that are getting overrun by today’s anti-education, anti-fairness GOP.