Clinton’s Coalition of the Affluent, I Mean Educated

It has been well established Hillary Clinton lost the election because she failed to reestablish the Obama coalition of Northern whites and blacks, failed to recognize white/Obama voters were flocking to Trump and that her margins in pre-election polls were driven by huge margins in major cities in red/blue states.  But, Clinton also improved on Obama’s margins among the affluent, er, I mean the educated (college degree or better).

Fortunately, better minds than I have already put this phenomenon to the test.  Fivethirtyeight’s Nate Silver was the first to note this underlying trend.  He did it by looking at the 50 most educated counties in the US and Clinton’s margins vs. Obama’s in 2012.  In 48 of the 50 counties Clinton performed better than Obama and overall averaged an additional 8.5 percent in all 50 counties (only Story, IA and Madison, MS were immune to the trend).

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
Clinton’s margin surged in the 50 most-educated counties
SOURCES: AMERICAN COMMUNITY SURVEY, U.S. ELECTION ATLAS, ABC NEWS

Of course, education is increasingly correlated with diversity.  Many of the 50 counties included large, diverse metropolitan areas and had major universities nestled within their boundaries.

But, in politics, as in life, for every reaction there is an equal and opposite reaction. In the 50 least educated counties Clinton’s support collapsed.  By collapsed I mean imploded.  She performed worse than Obama in 47 of the 50 counties and Trump averaged an 11.3 percent increase from Romney’s performance in 2012.  Representing the racial and geographic divide in the nation only Imperial, CA, majority Hispanic Starr, TX and Apache, AZ gave Clinton bigger margins than Obama.

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
Clinton collapsed in the 50 least-educated counties
SOURCES: AMERICAN COMMUNITY SURVEY, U.S. ELECTION ATLAS, ABC NEWS, ALASKA DIVISION OF ELECTIONS

Breaking down income vs. education can be tricky because the two are so closely correlated with each other.  But there are specific examples that illustrate education drove voting patterns more than race, income or geography.  For example, Starr, TX is a whopping 96 percent Hispanic.  Trump’s campaign staked a claim to being anti-illegal immigration and it would fit the pattern Clinton would do better than Obama in places such as this.  Or she would improve in places like McKinley, New Mexico.  Instead, she did not or barely did.

Fortunately, Fivethirtyeight saved me the trouble of creating my own table and assembled a list of 35 highly educated, median income majority white counties and their results.  Clinton performed on average 4 points better than Obama in the 35 counties.

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
High-education, medium-income white counties shifted to Clinton
*Counties shown have a population of at least 50,000. At least 50 percent of residents are non-Hispanic whites, at least 35 percent of the age-25-and-older population has a bachelor’s degree or higher, and the median household income is below $50,000.
SOURCES: AMERICAN COMMUNITY SURVEY, U.S. ELECTION ATLAS, ABC NEWS

But, in a comparative list of 35 medium educated, medium income majority white counties her support cratered.  Trump performed on average 4.8 points better than Romney in these counties.

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
High-income, medium-education white counties shifted to Trump
*Counties shown have a population of at least 50,000. At least 50 percent of residents are non-Hispanic whites, less than 35 percent of the age-25-and-older population has a bachelor’s degree or higher, and the median household income is above $70,000.
SOURCES: AMERICAN COMMUNITY SURVEY, U.S. ELECTION ATLAS, ABC NEWS

 

Further illustrating the power education had in this election was its ability to even drive racial preferences.  Never has this happened before.  Clinton performed better than Obama in highly educated, majority-minority communities like DeKalb, Georgia and Mecklenburg, North Carolina.  But, in majority-minority communities with less education she performed worse than Obama by just over 3 percent.

On the surface such a trade-off appears positive for Democrats.  Except, many highly educated voters still retained loyalty to Republicans down-ballot and few, highly educated counties are situated in the Electoral College rich Midwest.  Further, it provides more evidence that 2016 was a realigning election to some degree and that old political coalitions in existence for decades were toppled this year.

On a practical level this makes sense.  Trump staked his electoral hopes on highly charged, emotional issues like immigration and trade.  Both of these issues have more salience to non-college educated voters regardless of whether they are unionized, white, black, gay, man or woman.

Secondly, it reflects the political choice of the Clinton campaign to decide to make their candidate into a highly cerebral and policy focused nominee.  Her detail orientated campaign speeches spoke to a stern and steady temperament in the White House but it failed to show any concern to the emotional concerns of many down-scale voters.

Lastly, education levels tend to correlate with jobs at risk of being shipped overseas or not.  For example, a white-collar worker in CA is unlikely to see their job moved to China.  But, a non-college educated white worker in Central Ohio cannot say the same.

Of course, this analysis and many others are based on exit polls and voter results.  We don’t know exactly who showed up to vote, their income levels, education, etc.  As more Voter File data becomes available we will know more.  But, for now, it looks like Clinton assembled a coalition of the affluent, er, I mean educated.  It just was not enough to hand her the election.

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