How the 'diploma divide' helped steer Trump back to the White House
"Democrats have long struggled to reach voters without a college education. That growing blind spot played an important role in Harris' loss.
Among voters without college degrees, Kamala Harris fared worse than Joe Biden four years ago.
That’s a big problem for Democrats. And it has become more of a liability for the party as it struggles to grasp the working class appeal it once enjoyed.
The GOP won big last week thanks in part to white women unswayed by Donald Trump's link to the end of Roe v. Wade, a realignment of Latino voters and some Black men in swing states. But the wins also resulted from the expanding political differences between voters with college degrees and those without them.
That chasm, nicknamed the “diploma divide,” has long been an issue for Democrats. It appears to have worsened last week: CNN exit polls (which are only a snapshot of the electorate and aren’t always accurate) showed Harris outperformed Biden’s 2020 numbers among white voters with college degrees. Meanwhile, exit polling from NBC News gave Republicans a 9-point gain with voters who never attended college. Exit polling from The Associated Press and The Washington Post provided similar evidence.
"The diploma divide continued and extended a bit from previous elections," said Matt Grossmann, a political scientist at Michigan State University who has studied the trend.
Going to college is a privilege. Not everyone can go, and perhaps more importantly, not everyone wants to go. Yet, in recent decades, a degree has arguably become more of an economic necessity than ever.
As having a college education became a standard expectation among many employers around the turn of the century, the cost of college rose. Student loan debt ballooned. Students accused some schools of knowingly ripping them off. Then came student loan forgiveness, which has been highly politicized and criticized by Americans who don’t want to be left unfairly footing the bill for other people’s financial risks.
All those circumstances have clashed with the two major American political parties, shaping the public's choices about whom to support.
For years, Democrats have accepted the fact that they can’t win over voters who aren't college educated, said Joan Williams, a professor at the University of California College of the Law, San Francisco, and author of the forthcoming book “Outclassed: How the Left Lost the Working Class and How to Win Them Back.”
“The assumption has been that Democrats would make up for that by winning overwhelmingly among voters of color. But that didn’t work,” she said. “This strategy, of resigning yourself to losing among white noncollege voters, is gone with the wind.”
Two-thirds of white men without a college degree supported Trump this election, according to exit polling data from The Washington Post. So did 60% of white women who didn't go to college.
College-educated voters haven’t always been Democrats
For decades, Republicans have accused colleges of “liberal indoctrination.” But experts say college-educated voters weren't always a left-leaning majority.
Throughout the 20th century, Americans with bachelor’s degrees were more likely to align with the GOP. Historically, higher education was disproportionately available to young people from richer families, and those families were often Republicans, according to Grossmann, who co-authored the book “Polarized by Degrees: How the Diploma Divide and the Culture War Transformed American Politics.”
The presidency of Ronald Reagan in the 1980s started to lure white voters without college degrees away from the Democrats. A reversal seemed underway, according to the writings of the historian Thomas Frank. More traditional blue-collar Democrats were seeing less of themselves reflected in a party that favored a more connected world and began to be perceived as more "elite."
Working class voters started to feel looked down upon, Frank says in his 2004 book, "What's the Matter With Kansas." GOP leaders learned how to "align themselves with the common people, rising up righteously against the puffed-up know-it-all," he writes.
By the 2008 election, something had fundamentally changed. That year, Illinois Sen. Barack Obama defeated Sen. John McCain, an Arizona Republican, and his running mate, Alaska Gov. Sarah Palin (whose populist appeal, some think, paved the way for Trump’s rise).
Research shows it also marked the first time holding a bachelor’s degree became a significant predictor that people would vote for a Democrat.
By 2016, white voters without college degrees flocked to the Republican Party. Roughly two-thirds identified or leaned toward the GOP, according to Grossmann and Hopkins. Trump successfully tapped into the populist appeal quickly becoming the GOP's brand. His disregard for political correctness and disinterest in the nitty-gritty of policy resonated with voters who felt a cultural connection with him.
The diploma divide persisted into 2020, and last week the gap seemed to widen even more.
Put simply, Democrats “do not have an image as a party of the working class anymore," Grossmann said.
Democrats shift messaging on the value of college
Since 2016, the left's vulnerabilities among voters who didn't go to college have likely played a role in broadening Democrats' messaging about the value of a college degree.
Barack Obama set a goal at the beginning of his first term for the U.S. to have the highest proportion of college graduates in the world. But that “college for all” rhetoric has since been de-emphasized, particularly as efforts to make community college free fizzled out. Expanding other educational pathways, such as vocational programs and apprenticeships, has become a more comfortable talking point for Democrats in recent years.
Just before Election Day, Kamala Harris promised that on her first day in office, she would get rid of degree requirements for some positions in the federal government. It's unclear how many caught on to that pledge, but days later, many who could've benefited from it opposed her at the ballot box.
Without the kind of visibility the Oval Office affords, Democrats will have a lot more work to do to win back the kinds of voters they’ve lost, said Williams, the San Francisco professor.
“Noncollege voters feel like they’ve really gotten screwed economically," she said. “They’re not that interested in preserving a system that, as they see it, ate up the American Dream and spat it out in their face.”
Zachary Schermele is an education reporter for USA TODAY. You can reach him by email at zschermele@usatoday.com. Follow him on X at @ZachSchermele.
(Just like 2016 the ignorant, the uneducated voted and elected Trump)
"Sometimes statistical analysis is tricky, and sometimes a finding just jumps off the page. Here’s one example of the latter.
I took a list of all 981 U.S. counties1 with 50,000 or more people2 and sorted it by the share of the population3 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.
| COUNTY | COLLEGE DEGREE | MEDIAN HOUSEHOLD INCOME | OBAMA 2012 | CLINTON 2016 | SHIFT |
|---|---|---|---|---|---|
| Average | 51.4% | $77,768k | +17.3 | +25.9 | +8.5 |
| Arlington, VA | 72.0 | 105,120 | +39.8 | +60.1 | +20.3 |
| Alexandria, VA | 61.5 | 87,319 | +43.5 | +59.0 | +15.5 |
| Howard, MD | 60.4 | 110,133 | +22.0 | +33.5 | +11.5 |
| New York, NY | 59.3 | 71,656 | +68.8 | +77.2 | +8.4 |
| Fairfax, VA | 59.2 | 112,102 | +20.5 | +36.2 | +15.7 |
| Boulder, CO | 58.2 | 69,407 | +41.8 | +48.7 | +6.9 |
| Loudoun, VA | 58.0 | 123,966 | +4.5 | +16.8 | +12.3 |
| Montgomery, MD | 57.4 | 98,704 | +43.9 | +55.6 | +11.7 |
| Orange, NC | 56.2 | 57,261 | +42.2 | +51.0 | +8.8 |
| Douglas, CO | 55.9 | 102,626 | -25.8 | -18.1 | +7.7 |
| Hamilton, IN | 55.6 | 84,635 | -34.3 | -19.6 | +14.7 |
| Marin, CA | 54.8 | 91,529 | +51.3 | +62.8 | +11.5 |
| Williamson, TN | 54.1 | 91,743 | -46.5 | -35.5 | +11.0 |
| District of Columbia | 53.4 | 69,235 | +83.6 | +88.7 | +5.1 |
| San Francisco, CA | 52.9 | 78,378 | +70.5 | +75.7 | +5.2 |
| Johnson, KS | 52.1 | 75,017 | -17.4 | -2.7 | +14.7 |
| Albemarle, VA | 52.1 | 67,958 | +12.0 | +25.0 | +13.0 |
| Somerset, NJ | 52.0 | 100,903 | +5.6 | +12.5 | +6.9 |
| Washtenaw, MI | 51.8 | 60,805 | +35.9 | +41.5 | +5.6 |
| Johnson, IA | 51.7 | 54,985 | +35.5 | +38.2 | +2.7 |
| Benton, OR | 51.4 | 49,338 | +28.5 | +33.8 | +5.3 |
| Middlesex, MA | 51.3 | 83,488 | +27.1 | +38.9 | +11.8 |
| Delaware, OH | 51.1 | 91,936 | -23.2 | -16.1 | +7.1 |
| Morris, NJ | 50.6 | 99,142 | -10.8 | -4.4 | +6.4 |
| Tompkins, NY | 50.3 | 52,836 | +40.6 | +42.1 | +1.5 |
| Norfolk, MA | 49.9 | 86,469 | +15.2 | +31.6 | +16.4 |
| Broomfield, CO | 49.5 | 80,430 | +6.0 | +14.1 | +8.1 |
| Douglas, KS | 49.4 | 50,732 | +24.6 | +32.7 | +8.1 |
| Collin, TX | 49.4 | 84,233 | -31.5 | -17.0 | +14.5 |
| Chester, PA | 48.8 | 86,093 | -0.2 | +9.3 | +9.5 |
| Fulton, GA | 48.6 | 56,642 | +29.8 | +42.1 | +12.3 |
| Story, IA | 48.5 | 51,270 | +13.8 | +12.2 | -1.6 |
| Hunterdon, NJ | 48.3 | 106,519 | -17.8 | -13.8 | +4.0 |
| Wake, NC | 48.3 | 66,579 | +11.4 | +20.5 | +9.1 |
| Chittenden, VT | 48.0 | 64,243 | +41.6 | +47.4 | +5.8 |
| Boone, MO | 47.7 | 49,059 | +3.1 | +5.9 | +2.8 |
| Dane, WI | 47.6 | 62,303 | +43.5 | +48.0 | +4.5 |
| Santa Clara, CA | 47.3 | 93,854 | +42.9 | +52.3 | +9.4 |
| Eagle, CO | 47.3 | 73,774 | +14.9 | +19.9 | +5.0 |
| King, WA | 47.1 | 73,035 | +40.6 | +50.5 | +9.9 |
| DuPage, IL | 46.7 | 79,016 | +1.1 | +14.1 | +13.0 |
| Gallatin, MT | 46.7 | 54,298 | -5.0 | +1.0 | +6.0 |
| Ozaukee, WI | 46.4 | 75,643 | -30.3 | -19.3 | +11.0 |
| Hennepin, MN | 46.4 | 65,033 | +27.0 | +35.3 | +8.3 |
| Madison, MS | 46.3 | 63,156 | -15.7 | -16.0 | -0.3 |
| Montgomery, PA | 46.2 | 79,926 | +14.3 | +21.1 | +6.8 |
| James City, VA | 46.1 | 76,705 | -12.0 | -5.1 | +6.9 |
| Bergen, NJ | 46.1 | 83,686 | +11.3 | +12.0 | +0.7 |
| Westchester, NY | 46.0 | 83,422 | +25.1 | +32.8 | +7.7 |
| Durham, NC | 45.6 | 52,038 | +52.8 | +60.4 | +7.6 |
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.
Now here’s the opposite list: The 50 counties (minimum population of 50,000) where the smallest share of the population has bachelor’s degrees:
| COUNTY | COLLEGE DEGREE | MEDIAN HOUSEHOLD INCOME | OBAMA 2012 | CLINTON 2016 | SHIFT |
|---|---|---|---|---|---|
| Average | 13.3% | $41,108 | -19.3 | -30.5 | -11.3 |
| Liberty, TX | 8.8 | 47,722 | -53.3 | -58.0 | -4.7 |
| Starr, TX | 9.6 | 25,906 | +73.3 | +60.1 | -13.2 |
| Acadia, LA | 9.9 | 37,684 | -49.8 | -56.7 | -6.9 |
| Apache, AZ | 10.1 | 32,396 | +34.3 | +36.9 | +2.6 |
| Duplin, NC | 10.4 | 34,787 | -11.6 | -19.2 | -7.6 |
| Walker, AL | 10.7 | 36,712 | -52.8 | -67.5 | -14.7 |
| Edgecombe, NC | 10.7 | 33,892 | +36.2 | +32.2 | -4.0 |
| St. Mary, LA | 11.1 | 41,956 | -18.8 | -27.6 | -8.8 |
| DeKalb, AL | 11.3 | 37,977 | -54.7 | -69.4 | -14.7 |
| Anderson, TX | 11.3 | 42,511 | -52.1 | -58.1 | -6.0 |
| McKinley, NM | 11.4 | 29,812 | +46.9 | +39.5 | -7.4 |
| Henry, VA | 11.5 | 34,344 | -14.7 | -29.2 | -14.5 |
| Putnam, FL | 11.6 | 32,714 | -24.5 | -36.6 | -12.2 |
| Darke, OH | 11.6 | 43,323 | -44.4 | -61.2 | -16.8 |
| Halifax, NC | 11.9 | 32,834 | +32.3 | +26.9 | -5.4 |
| Laurel, KY | 11.9 | 35,746 | -63.6 | -69.1 | -5.5 |
| Sampson, NC | 12.1 | 35,731 | -10.9 | -16.7 | -5.8 |
| Maverick, TX | 12.1 | 32,536 | +58.1 | +55.8 | -2.3 |
| Mohave, AZ | 12.2 | 38,456 | -42.1 | -51.5 | -9.4 |
| Blount, AL | 12.3 | 44,409 | -73.9 | -81.4 | -7.5 |
| Robeson, NC | 12.4 | 30,581 | +17.4 | -4.8 | -22.2 |
| Kings, CA | 12.5 | 47,341 | -14.9 | -17.4 | -2.5 |
| Talladega, AL | 12.5 | 35,896 | -16.0 | -25.5 | -9.5 |
| Pike, KY | 12.5 | 32,571 | -50.5 | -62.7 | -12.2 |
| Marion, OH | 12.5 | 42,904 | -6.4 | -34.4 | -28.0 |
| Lea, NM | 12.6 | 55,248 | -49.8 | -48.3 | +1.5 |
| Columbus, NC | 12.7 | 34,597 | -7.8 | -22.1 | -14.3 |
| Terrebonne, LA | 12.9 | 49,932 | -41.2 | -48.4 | -7.2 |
| Wilkes, NC | 12.9 | 32,157 | -42.4 | -55.2 | -12.8 |
| Jackson, AL | 12.9 | 36,874 | -41.8 | -62.5 | -20.7 |
| Le Flore, OK | 12.9 | 35,970 | -41.1 | -58.7 | -17.6 |
| Merced, CA | 13.0 | 43,066 | +8.7 | +7.9 | -0.8 |
| Hawkins, TN | 13.0 | 37,432 | -46.9 | -63.4 | -16.5 |
| Vermilion, LA | 13.0 | 47,344 | -52.8 | -59.6 | -6.8 |
| St. Landry, LA | 13.1 | 33,928 | -4.3 | -11.9 | -7.6 |
| Rockingham, NC | 13.1 | 38,946 | -21.1 | -30.0 | -8.9 |
| Huron, OH | 13.1 | 49,315 | -8.3 | -36.4 | -28.1 |
| Clearfield, PA | 13.2 | 41,510 | -28.9 | -49.5 | -20.6 |
| Tulare, CA | 13.3 | 42,863 | -15.0 | -16.2 | -1.2 |
| Rusk, TX | 13.3 | 46,924 | -51.1 | -56.6 | -5.5 |
| Ashtabula, OH | 13.4 | 40,304 | +12.8 | -19.0 | -31.8 |
| Imperial, CA | 13.4 | 41,772 | +32.0 | +41.8 | +9.7 |
| Bullitt, KY | 13.4 | 56,199 | -35.7 | -49.8 | -14.1 |
| Caldwell, NC | 13.4 | 34,853 | -35.5 | -50.6 | -15.1 |
| Montcalm, MI | 13.4 | 40,739 | -8.6 | -34.0 | -25.4 |
| Madera, CA | 13.5 | 45,490 | -17.1 | -17.3 | -0.2 |
| Dickson, TN | 13.5 | 45,056 | -28.4 | -45.7 | -17.3 |
| Tuscola, MI | 13.5 | 44,017 | -10.8 | -38.0 | -27.2 |
| Pearl River, MS | 13.5 | 40,997 | -59.3 | -66.7 | -7.4 |
| Columbiana, OH | 13.6 | 43,707 | -11.8 | -41.6 | -29.8 |
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 also a reasonably diverse list of counties. While some of them are poor, a few others — such as Bullitt County, Kentucky, and Terrebonne Parish, Louisiana — have average incomes. There’s also some racial diversity on the list: Starr County, Texas, is 96 percent Hispanic, for example, and Clinton underperformed Obama there (although she still won it by a large margin). Edgecombe County, North Carolina, is 57 percent black and saw a shift toward Trump.
How do we know that education levels drove changes in support — as opposed to income levels, for example? It’s tricky because there’s a fairly strong correlation between income and education.4 Nonetheless, with the whole country to pick from, we can find some places where education levels are high but incomes are average or below average. If education is the key driver of changes in the electorate, we’d expect Clinton to hold steady or gain in these counties. If income matters more, we might see her numbers decline.
As it happens, I grew up in one of these places: Ingham County, Michigan, which is home to Michigan State University and the state capital of Lansing, along with a lot of auto manufacturing jobs (though fewer than there used to be). The university and government jobs attract an educated workforce, but there aren’t a lot of rich people in Ingham County. How did Clinton do there? Just fine. She won it by 28 percentage points, the same as Obama did four years ago, despite her overall decline in Michigan.
And in most places that fit this description, Clinton improved on Obama’s performance. I identified 22 counties5 where at least 35 percent of the population has bachelor’s degrees but the median household income is less than $50,0006 and at least 50 percent of the population is non-Hispanic white (we’ll look at what happened with majority-minority counties in a moment, so hang tight). Clinton improved on Obama’s performance in 18 of the 22 counties, by an average of about 4 percentage points:
| COUNTY | COLLEGE DEGREE | MEDIAN HOUSEHOLD INCOME | OBAMA 2012 | CLINTON 2016 | SHIFT |
|---|---|---|---|---|---|
| Average | 40.2% | $43,862 | +4.8 | +8.8 | +4.0 |
| Brazos, TX | 38.3 | 39,060 | -35.3 | -23.6 | +11.7 |
| Champaign, IL | 42.5 | 46,680 | +7.0 | +18.4 | +11.4 |
| Clarke, GA | 39.3 | 33,430 | +28.8 | +38.0 | +9.2 |
| Harrisonburg, VA | 35.6 | 38,807 | +13.4 | +21.9 | +8.5 |
| Fayette, KY | 40.2 | 48,667 | +1.0 | +9.4 | +8.4 |
| Riley, KS | 45.5 | 44,522 | -12.0 | -4.5 | +7.5 |
| Davidson, TN | 36.5 | 47,434 | +18.6 | +26.0 | +7.4 |
| Benton, OR | 51.4 | 49,338 | +28.5 | +33.8 | +5.3 |
| Alachua, FL | 40.8 | 42,045 | +17.4 | +22.6 | +5.2 |
| Watauga, NC | 38.0 | 35,491 | -3.1 | +1.5 | +4.6 |
| Monroe, IN | 44.2 | 41,857 | +19.1 | +23.7 | +4.6 |
| Boone, MO | 47.7 | 49,059 | +3.1 | +5.9 | +2.8 |
| Buncombe, NC | 35.1 | 45,642 | +12.5 | +14.6 | +2.1 |
| Montgomery, VA | 44.3 | 44,810 | -0.3 | +1.3 | +1.6 |
| Leon, FL | 44.3 | 46,620 | +23.6 | +25.1 | +1.5 |
| Lafayette, MS | 36.9 | 41,343 | -15.3 | -14.8 | +0.5 |
| New Hanover, NC | 37.2 | 49,582 | -4.6 | -4.1 | +0.5 |
| Payne, OK | 36.4 | 37,637 | -28.4 | -28.3 | +0.1 |
| Ingham, MI | 36.5 | 45,278 | +27.8 | +27.7 | -0.1 |
| Monongalia, WV | 38.8 | 46,166 | -9.5 | -10.4 | -0.9 |
| Tippecanoe, IN | 35.2 | 44,474 | -3.6 | -5.7 | -2.1 |
| Missoula, MT | 40.2 | 47,029 | +17.8 | +15.7 | -2.1 |
Are these so-called “white working-class” counties? You could argue for it: They’re mostly white, and they have average or below-average incomes. But, of course, “class” is a slippery term, and definitions vary. It is worth noting that many of the counties on the list are home to major colleges or universities, although there are some exceptions. Clinton made substantial gains in Nashville, Tennessee (Davidson County), and modest gains in Asheville, North Carolina (Buncombe County), for instance, and both places have reputations as intellectual and cultural havens but aren’t really college towns.7
There are also some counties where incomes are high but residents aren’t particularly well-educated. Take Suffolk County, New York, for instance, which comprises the eastern three-quarters of Long Island. The median household income there is around $88,000, but only about a third of the population has college degrees (as compared to a national average of around 30 percent). Suffolk County turned into Trump Territory, voting for him by 8 percentage points after Obama had won it by 4 points in 2012. Trump made even larger gains in Staten Island, New York (Richmond County), winning it by 17 points after Obama won it by 3 points in 2012.
Long Island and Staten Island might be peculiar cases because voters there may have a cultural affinity with Trump, who grew up in Queens. Even so, they reveal something about how cultural and educational fault lines can mean more than economic circumstances. Clinton improved over Obama’s performance in suburban Westchester County, New York, for instance, which has broadly similar income levels to Long Island and Staten Island but higher education levels and a different mix of occupations.8 (Staten Island is famous for its large population of police and firefighters, but you’ll meet a lot more journalists who have homes in Westchester.9)
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.10 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.
| COUNTY | COLLEGE DEGREE | MEDIAN HOUSEHOLD INCOME | OBAMA 2012 | CLINTON 2016 | SHIFT |
|---|---|---|---|---|---|
| Average | 30.4% | $76,701 | -11.0 | -15.8 | -4.8 |
| Richmond, NY | 30.6 | 74,043 | +2.6 | -16.8 | -19.4 |
| Chisago, MN | 21.5 | 70,223 | -12.6 | -30.6 | -18.0 |
| Sherburne, MN | 26.2 | 73,621 | -22.0 | -37.1 | -15.1 |
| Litchfield, CT | 33.7 | 72,068 | -3.6 | -16.0 | -12.3 |
| Orange, NY | 28.6 | 70,794 | +5.7 | -6.4 | -12.1 |
| Suffolk, NY | 33.5 | 88,323 | +3.7 | -8.2 | -11.9 |
| Wright, MN | 27.4 | 73,085 | -21.7 | -33.2 | -11.5 |
| Gloucester, NJ | 28.7 | 76,213 | +10.8 | -0.5 | -11.3 |
| Calvert, MD | 29.3 | 95,425 | -7.5 | -18.4 | -10.9 |
| Warren, NJ | 29.5 | 70,934 | -15.5 | -25.6 | -10.1 |
| St. Mary’s, MD | 29.8 | 88,190 | -14.8 | -24.6 | -9.8 |
| Sussex, NJ | 33.1 | 87,397 | -21.4 | -30.2 | -8.8 |
| Dutchess, NY | 33.4 | 72,471 | +7.5 | -1.1 | -8.6 |
| Anoka, MN | 27.3 | 70,464 | -2.6 | -9.7 | -7.1 |
| Livingston, MI | 33.0 | 73,694 | -23.3 | -29.6 | -6.3 |
| St. Croix, WI | 32.4 | 70,313 | -12.1 | -18.4 | -6.3 |
| Harford, MD | 33.4 | 81,016 | -18.4 | -24.5 | -6.1 |
| Spotsylvania, VA | 28.3 | 78,505 | -11.5 | -16.8 | -5.3 |
| Fauquier, VA | 34.3 | 92,078 | -19.9 | -24.7 | -4.8 |
| Carroll, MD | 32.7 | 85,532 | -32.9 | -36.9 | -4.0 |
| Chesapeake, VA | 29.4 | 70,176 | +1.0 | -1.3 | -2.3 |
| Ascension, LA | 25.8 | 70,207 | -34.3 | -36.0 | -1.7 |
| Elko, NV | 17.5 | 72,280 | -53.2 | -54.7 | -1.5 |
| Will, IL | 32.6 | 76,142 | +5.5 | +5.6 | +0.1 |
| McHenry, IL | 32.2 | 76,345 | -8.8 | -8.0 | +0.8 |
| Kendall, IL | 34.3 | 83,844 | -3.3 | -1.5 | +1.8 |
| Plymouth, MA | 34.0 | 75,816 | +4.2 | +10.1 | +5.9 |
| Napa, CA | 31.9 | 70,925 | +28.7 | +35.3 | +6.6 |
| Kane, IL | 31.8 | 70,514 | +1.1 | +9.0 | +7.9 |
| Davis, UT | 34.6 | 70,388 | -61.9 | -22.9 | +39.0 |
Education levels are also increasingly dividing majority-minority communities from one another. For example, let’s look at a set of counties that were a sweet spot for the Obama coalition — those that are both diverse and highly educated. In particular, there are 24 counties (minimum population 50,000) in the U.S. where at least 35 percent of the population has college degrees and less than half the population is non-Hispanic white. Obama did really well in these counties in 2012, winning them by an average of 41 percentage points. But Clinton did even better, winning them by 47 points, on average. The only two such counties that Obama had lost, Clinton won: Fort Bend County, Texas, in suburban Houston, which voted for a Democrat for the first time since 1964, and Orange County, California, which hadn’t voted Democratic since 1936.
| COUNTY | COLLEGE DEGREE | NON-HISPANIC WHITE | OBAMA 2012 | CLINTON 2016 | SHIFT |
|---|---|---|---|---|---|
| Average | 42.9% | 41.9% | +41.2 | +47.5 | +6.3 |
| Fort Bend, TX | 42.3 | 35.5 | -6.8 | +6.6 | +13.4 |
| Fulton, GA | 48.6 | 40.6 | +29.8 | +42.1 | +12.3 |
| Montgomery, MD | 57.4 | 47.4 | +43.9 | +55.6 | +11.7 |
| Orange, CA | 37.3 | 42.9 | -6.2 | +5.2 | +11.4 |
| San Mateo, CA | 45.0 | 41.2 | +46.7 | +57.2 | +10.5 |
| San Diego, CA | 35.1 | 47.5 | +7.6 | +17.1 | +9.5 |
| Santa Clara, CA | 47.3 | 34.1 | +42.9 | +52.3 | +9.4 |
| New York, NY | 59.3 | 47.4 | +68.8 | +77.2 | +8.4 |
| Yolo, CA | 38.3 | 48.8 | +34.0 | +42.1 | +8.1 |
| DeKalb, GA | 40.3 | 29.7 | +56.8 | +64.7 | +7.9 |
| Suffolk, MA | 41.0 | 47.1 | +56.7 | +64.6 | +7.9 |
| Contra Costa, CA | 39.4 | 46.6 | +35.2 | +42.9 | +7.7 |
| Durham, NC | 45.6 | 42.1 | +52.8 | +60.4 | +7.6 |
| Mecklenburg, NC | 41.5 | 49.6 | +22.4 | +29.9 | +7.5 |
| Richmond, VA | 35.4 | 39.7 | +57.3 | +63.8 | +6.5 |
| San Francisco, CA | 52.9 | 41.4 | +70.5 | +75.7 | +5.2 |
| District of Columbia | 53.4 | 35.4 | +83.6 | +88.7 | +5.1 |
| Prince William, VA | 38.1 | 47.0 | +16.0 | +20.1 | +4.1 |
| Alameda, CA | 42.1 | 33.3 | +60.7 | +64.4 | +3.7 |
| Cook, IL | 35.3 | 43.4 | +49.4 | +53.0 | +3.6 |
| Richland, SC | 36.2 | 44.6 | +32.0 | +32.9 | +0.9 |
| Santa Fe, NM | 39.9 | 43.4 | +51.1 | +50.8 | -0.3 |
| Hudson, NJ | 36.8 | 29.6 | +56.1 | +51.9 | -4.2 |
| Middlesex, NJ | 40.7 | 47.0 | +27.6 | +19.7 | -7.9 |
By contrast, Clinton struggled (relatively speaking) in majority-minority communities with lower education levels. Among the 19 majority-minority countries where 15 percent or less of the population has a bachelor’s degree, she won by an average of only 7 percentage points, less than Obama’s 10-point average margin of victory in 2012. We need to be slightly careful here because of the potential ecological fallacy — it’s not clear if minority voters shifted away from Clinton in these counties or if the white voters who live there did. Still, Trump probably gained overall among Latino and black voters compared to Romney, and it’s worth investigating divisions within those communities instead of treating their votes as monolithic.
| COUNTY | COLLEGE DEGREE | NON-HISPANIC WHITE | OBAMA 2012 | CLINTON 2016 | SHIFT |
|---|---|---|---|---|---|
| Average | 12.8% | 30.3% | +10.1 | +7.0 | -3.1 |
| Robeson, NC | 12.4 | 26.7 | +17.4 | -4.8 | -22.2 |
| Cumberland, NJ | 13.8 | 49.0 | +24.2 | +5.3 | -18.9 |
| Starr, TX | 9.6 | 3.4 | +73.3 | +60.1 | -13.2 |
| McKinley, NM | 11.4 | 10.1 | +46.9 | +39.5 | -7.4 |
| Crittenden, AR | 14.6 | 44.7 | +14.9 | +8.9 | -6.0 |
| Halifax, NC | 11.9 | 39.3 | 32.3 | 26.9 | -5.4 |
| Edgecombe, NC | 10.7 | 37.2 | +36.2 | +32.2 | -4.0 |
| San Patricio, TX | 14.8 | 41.0 | -20.7 | -24.0 | -3.3 |
| Kings, CA | 12.5 | 34.5 | -14.9 | -17.4 | -2.5 |
| Maverick, TX | 12.1 | 3.1 | +58.1 | +55.8 | -2.3 |
| Tulare, CA | 13.3 | 31.3 | -15.0 | -16.2 | -1.2 |
| Merced, CA | 13.0 | 30.5 | +8.7 | +7.9 | -0.8 |
| Madera, CA | 13.5 | 36.8 | -17.1 | -17.3 | -0.2 |
| Navajo, AZ | 14.5 | 43.0 | -7.8 | -7.9 | -0.1 |
| Lea County, NM | 12.6 | 40.6 | -49.8 | -48.3 | +1.5 |
| Apache, AZ | 10.1 | 19.6 | +34.3 | +36.9 | +2.6 |
| Yuma, AZ | 14.0 | 34.0 | -12.6 | -5.5 | 7.1 |
| Ector, TX | 14.3 | 38.3 | -48.9 | -40.6 | +8.3 |
| Imperial, CA | 13.4 | 13.0 | +32.0 | +41.8 | +9.7 |
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.11 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.
But although this finding is clear in a statistical sense, that doesn’t mean the interpretation of it is straightforward. It seems to me that there a number of competing hypotheses that are compatible with this evidence, some of which will be favored by conservatives and some by liberals:
- Education levels may be a proxy for cultural hegemony. Academia, the news media and the arts and entertainment sectors are increasingly dominated by people with a liberal, multicultural worldview, and jobs in these sectors also almost always require college degrees. Trump’s campaign may have represented a backlash against these cultural elites.
- Educational attainment may be a better indicator of long-term economic well-being than household incomes. Unionized jobs in the auto industry often pay reasonably well even if they don’t require college degrees, for instance, but they’re also potentially at risk of being shipped overseas or automated.
- Education levels probably have some relationship with racial resentment, although the causality isn’t clear. The act of having attended college itself may be important, insofar as colleges and universities are often more diverse places than students’ hometowns. There’s more research to be done on how exposure to racial minorities affected white voters. For instance, did white voters who live in counties with large Hispanic populations shift toward Clinton or toward Trump?
- Education levels have strong relationships with media-consumption habits, which may have been instrumental in deciding people’s votes, especially given the overall decline in trust in the news media.
- Trump’s approach to the campaign — relying on emotional appeals while glossing over policy details — may have resonated more among people with lower education levels as compared with Clinton’s wonkier and more cerebral approach.
So data like this is really just a starting point for further research into the campaign. Nonetheless, the education gap is carving up the American electorate and toppling political coalitions that had been in place for many years."
How did Trump win the election? The 'diploma divide' helped.
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