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I am curious about counties in America where either major candidate in 2016 (Clinton and Trump) got over 85% of the vote. I am interested in this because I want to find attributes that most or all of these places have in common.

  • 3
    Why exactly 85%? What attributes would 85%-counties have in common that 84%-counties don't? What attributes do they have in common? How would this influence your comparison's results and conclusions drawn? – I'm with Monica Apr 29 at 12:41
  • What is your question? – Kevin Apr 29 at 14:50
15

Using the MIT Election Data and Science Lab's County Presidential Election Returns 2000-2016 dataset, we can see that there were 110 counties where Trump received over 85% of the vote, and only 10 counties where Clinton did (despite Clinton winning the popular vote by almost 3 million). This is likely due to Republican-leaning counties being both less diverse and smaller than Democrat ones (see the small numbers fallacy) .

The Trump dominated counties were:

                             Trump  Clinton  Other  Total Trump % Clinton %
state         county                                                       
Texas         Roberts          524       20     10    554  94.58%     3.61%
              King             149        5      5    159  93.71%     3.14%
              Motley           566       40      9    615  92.03%     6.50%
Nebraska      Hayes            472       30     12    514  91.83%     5.84%
Texas         Shackelford     1378      103     23   1504  91.62%     6.85%
              Glasscock        553       34     17    604  91.56%     5.63%
              McMullen         454       40      5    499  90.98%     8.02%
Montana       Garfield         653       34     31    718  90.95%     4.74%
Nebraska      Grant            367       20     18    405  90.62%     4.94%
Texas         Wheeler         2087      194     25   2306  90.50%     8.41%
              Armstrong        924       70     27   1021  90.50%     6.86%
              Borden           330       31      4    365  90.41%     8.49%
Kansas        Wallace          721       46     31    798  90.35%     5.76%
South Dakota  Harding          695       38     37    770  90.26%     4.94%
Texas         Oldham           850       78     20    948  89.66%     8.23%
South Dakota  Haakon           936       77     31   1044  89.66%     7.38%
Nebraska      McPherson        257       14     16    287  89.55%     4.88%
Alabama       Winston         9228      872    213  10313  89.48%     8.46%
Kentucky      Leslie          4015      400     77   4492  89.38%     8.90%
Nebraska      Arthur           244       17     12    273  89.38%     6.23%
Alabama       Blount         22859     2156    573  25588  89.33%     8.43%
Oklahoma      Cimarron         963       71     45   1079  89.25%     6.58%
Texas         Loving            58        4      3     65  89.23%     6.15%
              Coke            1265      140     18   1423  88.90%     9.84%
Kentucky      Jackson         4889      482    130   5501  88.87%     8.76%
Texas         Hansford        1730      171     46   1947  88.85%     8.78%
Oklahoma      Beaver          1993      176     74   2243  88.85%     7.85%
Georgia       Glascock        1235      138     17   1390  88.85%     9.93%
Louisiana     La Salle        5836      605    128   6569  88.84%     9.21%
Nebraska      Banner           357       19     26    402  88.81%     4.73%
Texas         Jack            2973      314     63   3350  88.75%     9.37%
Nebraska      Keya Paha        460       40     19    519  88.63%     7.71%
Texas         Hartley         1730      173     49   1952  88.63%     8.86%
Kentucky      Martin          3503      363     87   3953  88.62%     9.18%
Texas         Throckmorton     715       84      9    808  88.49%    10.40%
              Archer          3786      394    103   4283  88.40%     9.20%
              Carson          2620      249     95   2964  88.39%     8.40%
Georgia       Brantley        5567      619    115   6301  88.35%     9.82%
Nebraska      Logan            400       32     21    453  88.30%     7.06%
Louisiana     Cameron         3256      323    113   3692  88.19%     8.75%
Oklahoma      Ellis           1611      155     61   1827  88.18%     8.48%
              Roger Mills     1547      151     61   1759  87.95%     8.58%
Georgia       Banks           6134      684    157   6975  87.94%     9.81%
Oklahoma      Harper          1318      134     47   1499  87.93%     8.94%
Mississippi   George          8696     1027    168   9891  87.92%    10.38%
Florida       Holmes          7483      853    178   8514  87.89%    10.02%
Texas         Gray            6500      701    204   7405  87.78%     9.47%
West Virginia Grant           4346      512     98   4956  87.69%    10.33%
Texas         Stephens        3034      348     79   3461  87.66%    10.05%
              Ochiltree       2628      274    100   3002  87.54%     9.13%
Nebraska      Thomas           344       30     19    393  87.53%     7.63%
Wyoming       Crook           3348      273    205   3826  87.51%     7.14%
Texas         Montague        7526      885    193   8604  87.47%    10.29%
Alabama       Cleburne        5764      684    145   6593  87.43%    10.37%
Oklahoma      Dewey           1965      222     61   2248  87.41%     9.88%
Texas         Clay            4377      536    105   5018  87.23%    10.68%
              Coleman         3177      388     78   3643  87.21%    10.65%
              Callahan        4865      569    145   5579  87.20%    10.20%
Kansas        Sheridan        1197      127     50   1374  87.12%     9.24%
Alabama       Cullman        32989     3798   1086  37873  87.10%    10.03%
Nebraska      Blaine           276       30     11    317  87.07%     9.46%
Texas         Lipscomb        1159      135     38   1332  87.01%    10.14%
Mississippi   Itawamba        8470     1117    150   9737  86.99%    11.47%
Texas         Kimble          1697      206     49   1952  86.94%    10.55%
              Mills           1951      243     51   2245  86.90%    10.82%
Alabama       Marion         11274     1432    278  12984  86.83%    11.03%
Nebraska      Chase           1648      171     79   1898  86.83%     9.01%
Kentucky      McCreary        5012      664    100   5776  86.77%    11.50%
Texas         Sterling         549       70     14    633  86.73%    11.06%
Nebraska      Brown           1385      153     59   1597  86.73%     9.58%
Wyoming       Campbell       15778     1324   1097  18199  86.70%     7.28%
Kentucky      Clay            5861      752    154   6767  86.61%    11.11%
Oklahoma      Major           2948      310    149   3407  86.53%     9.10%
Texas         Childress       1802      253     29   2084  86.47%    12.14%
Nebraska      Dundy            823       89     41    953  86.36%     9.34%
Texas         Hutchinson      7042      854    259   8155  86.35%    10.47%
Montana       Petroleum        278       30     14    322  86.34%     9.32%
Texas         Eastland        6011      776    176   6963  86.33%    11.14%
              Sherman          807       96     32    935  86.31%    10.27%
Montana       Carter           678       70     38    786  86.26%     8.91%
Texas         Hemphill        1462      181     52   1695  86.25%    10.68%
Georgia       Pierce          6302      903    106   7311  86.20%    12.35%
Montana       Fallon          1279      154     51   1484  86.19%    10.38%
Texas         Irion            660       90     16    766  86.16%    11.75%
Nebraska      Rock             687       70     41    798  86.09%     8.77%
Texas         Hardin         19606     2780    394  22780  86.07%    12.20%
Wyoming       Weston          3033      299    194   3526  86.02%     8.48%
Tennessee     Wayne           5036      717    104   5857  85.98%    12.24%
Texas         Sabine          3998      614     39   4651  85.96%    13.20%
              Runnels         3250      453     79   3782  85.93%    11.98%
              San Saba        2025      293     39   2357  85.91%    12.43%
              Leon            6391      909    139   7439  85.91%    12.22%
Utah          Piute            626       47     56    729  85.87%     6.45%
Kentucky      Monroe          4278      601    112   4991  85.71%    12.04%
Texas         Brown          12017     1621    388  14026  85.68%    11.56%
              Young           6601      876    230   7707  85.65%    11.37%
Oklahoma      Alfalfa         1933      216    109   2258  85.61%     9.57%
Mississippi   Tishomingo      7166      999    206   8371  85.61%    11.93%
Montana       Wibaux           463       55     23    541  85.58%    10.17%
Kentucky      Clinton         3809      547    106   4462  85.37%    12.26%
Georgia       Echols          1007      156     19   1182  85.19%    13.20%
Missouri      Mercer          1486      216     43   1745  85.16%    12.38%
Colorado      Kiowa            728       91     36    855  85.15%    10.64%
Kentucky      Casey           5482      767    190   6439  85.14%    11.91%
Nebraska      Hooker           355       40     22    417  85.13%     9.59%
North Dakota  Burke            895      119     38   1052  85.08%    11.31%
Nebraska      Holt            4354      531    233   5118  85.07%    10.38%
Missouri      Bollinger       4827      705    144   5676  85.04%    12.42%
Texas         Collingsworth    983      145     28   1156  85.03%    12.54%
Nebraska      Boyd             983      128     45   1156  85.03%    11.07%

The Clinton dominated counties were:

                                           Trump  Clinton  Other   Total Trump % Clinton %
state                county                                                               
District of Columbia District of Columbia  12723   282830  15715  311268   4.09%    90.86%
New York             Bronx                 37797   353646   8079  399522   9.46%    88.52%
Maryland             Prince George's       32811   344049  13525  390385   8.40%    88.13%
Rhode Island         Federal Precinct         53      637     38     728   7.28%    87.50%
Virginia             Petersburg             1451    12021    314   13786  10.53%    87.20%
Mississippi          Claiborne               540     3708     24    4272  12.64%    86.80%
New York             New York              64929   579013  24997  668939   9.71%    86.56%
Mississippi          Jefferson               490     3337     33    3860  12.69%    86.45%
South Dakota         Oglala Lakota           241     2510    154    2905   8.30%    86.40%
California           San Francisco         37688   345084  23020  405792   9.29%    85.04%
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  • 8
    Interesting that Trump won 11 times as many counties by 85%+ than Clinton (10 Clinton vs. 110 Trump), but, by population, Clinton won about 5 times as many counties by 85%+ than Trump (2,201,457 Clinton vs. 444,480 Trump). – Nat Apr 29 at 11:49
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    @Nat - Yeah, that's the kind of anomaly you see when you consider the country by surface area rather than by population. A surprising amount of the conflict in US history can be made to boil down to "Should land get to vote?" – T.E.D. Apr 29 at 13:12
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    @T.E.D. ...or conversely, should a few large populations of urban dwellers overrule what vast majority of the country wants. This is the classic argument that led to the Electoral College (and obviously still justified today), to prevent (as James Madison argued) "an interested and overbearing majority". – mharr Apr 29 at 18:28
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    @mharr - James has it right. But I find the rephrasing you used quite interesting. Most democratic countries do tend to believe, as a matter of principle, that the side that has the most votes should win. The fact that one gets push-back on this concept when talking specifically about US politics is really telling. – T.E.D. Apr 29 at 18:59
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    @mharr If the urban population would win in a straight vote, how could you possibly say "overrule what a vast majority of the country wants"? Since, by definition, the majority has won. – Azor Ahai Apr 29 at 19:26

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