Tuna, I love your post on so many levels.
1.) Confirmation of simple mindedness....my side is smarter than your side; despite jack-shit evidence.
2.) Most people with an IQ number above room temperature know that the claim in your lame attempt at a 'joke' is counterintuitive.
Even when a dumb-ass like yourself is bantering with a fellow Trumpanzee in your favorite saloon you're thinking....I know, I know, a real stretch....this mfr is dumber than me!
3.) It's so dammed easy to marshal actual evidence to knock down your ill though out 'jokes', that I think I'll head back over to The Bridge for more of an intellectual challenge.
1.) Confirmation of simple mindedness....my side is smarter than your side; despite jack-shit evidence.
2.) Most people with an IQ number above room temperature know that the claim in your lame attempt at a 'joke' is counterintuitive.
Even when a dumb-ass like yourself is bantering with a fellow Trumpanzee in your favorite saloon you're thinking....I know, I know, a real stretch....this mfr is dumber than me!
3.) It's so dammed easy to marshal actual evidence to knock down your ill though out 'jokes', that I think I'll head back over to The Bridge for more of an intellectual challenge.
Quote:The robot brings the drink and asks, "What’s your IQ?"
This time the man answers, "50."
The robot leans in real close and slowly asks, "So, are you people still unhappy that Hillary lost?”
Quote:Education, Not Income, Predicted Who Would Vote For Trump
By Nate Silver
Filed under 2016 Election
Quote:◾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.
Clinton collapsed in the 50 least-educated counties
http://fivethirtyeight.com/features/education...for-trump/
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
Sources: American Community Survey, U.S. Election Atlas, ABC News, Alaska Division of Elections
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:
1
