2016 in Review

The Ghost of Blogging Present

There was a lot that happened this year, and as a result I have not had time to do a full break down of our predictions and the like. Besides the obvious—the election of Donald Trump in November—this year saw an alteration to Australia's upper-house voting system, a federal election, Britain's decision to leave the EU, elections in the NT and ACT, several overseas elections mentioned in passing including Austria, the Philippines and Switzerland, and the US Primaries.

It is useful to consider the results, which I will be doing hastily before [edit: apparently also after] midnight here, but I also think this might be a useful tradition in future years as a part-recap as well as picking up the many balls I will inevitably drop during the course of the years to come.

The Ghost of Predictions Past

Of the elections mentioned above, four races were given concrete predictions on this blog: the United States Primaries, the Brexit vote, the Australian Federal Election and the US Presidential Race.

US Primaries

My earliest predictions on the Republican candidacy in the primaries were problematic in that they consistently gave Trump a clear win in every seat. While the momentum of Trump support was high, it is unlikely he would have won every state. However, Trump's last contestants dropped out shortly after so in a sense the results were accurate.

The real predictions, however, were around the Democratic primaries and largely based on racial analysis. Some states had already been called, of course, as these were the source of our base data. The predictions were as follows:

STATE	PREDICTION	RESULT
ALASKA	SANDERS	SANDERS
CALIFORNIA	SANDERS	CLINTON
CONNECTICUT	SANDERS	CLINTON
DELAWARE	CLINTON	CLINTON
DISTRICT OF COLUMBIA	CLINTON	CLINTON
HAWAII	SANDERS	SANDERS
KENTUCKY	SANDERS	CLINTON
MARYLAND	CLINTON	CLINTON
MISSOURI	SANDERS	CLINTON
MONTANA	SANDERS	SANDERS
NEW JERSEY	CLINTON	CLINTON
NEW YORK	CLINTON	CLINTON
OHIO	CLINTON	CLINTON
OREGON	SANDERS	SANDERS
PENNSYLVANIA	SANDERS	CLINTON
RHODE ISLAND	SANDERS	SANDERS
SOUTH DAKOTA	SANDERS	CLINTON
UTAH	SANDERS	SANDERS
WASHINGTON	SANDERS	SANDERS
WEST VIRGINIA	SANDERS	SANDERS
WISCONSIN	SANDERS	SANDERS
WYOMING	SANDERS	SANDERS

This resulted in 16 correct predictions our of 22, or around 73%. While not a spectacular result, this is better than the 50:50 of random guessing (~11 correct predictions expected), or just giving every state to Clinton or to Sanders based on who was expected to win the most states (with 12 and 10 correct predictions respectively).

The main limitation on refining this data in the future is the absence of reliable state-by-state polling.

Brexit

There isn't a huge amount to summarise here. There was a single prediction. It was wrong.

I could point to the number of other predictions and polls that also got this one wrong, the general consensus that the Leave vote couldn't win leading it's opponents to vote in a contrary manner in protest or else stay home out of apathy, followed by a period referred to as Bregret. And in some ways it is true that the polling generally showed a strong chance for the Remain camp. But the polling was far from unanimous and it was evident that the Leave vote (in blue below) was gaining as time went on not only in many separate polls but also in my own aggregation.

While it is often convenient to simply say “the polls were wrong” (I'm looking at you too, US presidential results), the fact is that I chose to rely on that flawed data without any attempt to correct it and, more problematically, when the data led to a conclusion I found unlikely I dismissed the data too readily as flawed by its collection method.

Australian Federal Election

Two sets of predictions were made here: one set for the lower house and one for the upper. The latter of these was complicated by the introduction of a new voting system with multiple above-the-line votes and exhaustion introduced. This replaced the preference tickets that offered some reliability into the preference flow system, undercutting psephological predictions for the sake of “transparency” and “democracy” or some other idealistic notion.

The lower house was far more traditionally predictable though slightly hindered by the phasing out of tossups, with 133 correct predictions out of 150, or around 89%:

DIVISION	PREDICTION	RESULT
Adelaide	ALP	ALP
Aston	LIB	LIB
Ballarat	ALP	ALP
Banks	LIB	LIB
Barker	NXT	LIB
Barton	ALP	ALP
Bass	LIB	ALP
Batman	ALP	ALP
Bendigo	ALP	ALP
Bennelong	LIB	LIB
Berowra	LIB	LIB
Blair	ALP	ALP
Blaxland	ALP	ALP
Bonner	LNP	LNP
Boothby	LIB	LIB
Bowman	LNP	LNP
Braddon	LIB	ALP
Bradfield	LIB	LIB
Brand	ALP	ALP
Brisbane	LNP	LNP
Bruce	ALP	ALP
Burt	LIB	ALP
Calare	NAT	NAT
Calwell	ALP	ALP
Canberra	ALP	ALP
Canning	LIB	LIB
Capricornia	ALP	LNP
Casey	LIB	LIB
Chifley	ALP	ALP
Chisholm	ALP	LIB
Cook	LIB	LIB
Corangamite	LIB	LIB
Corio	ALP	ALP
Cowan	LIB	ALP
Cowper	NAT	NAT
Cunningham	ALP	ALP
Curtin	LIB	LIB
Dawson	LNP	LNP
Deakin	LIB	LIB
Denison	IND	IND
Dickson	LNP	LNP
Dobell	LIB	ALP
Dunkley	LIB	LIB
Durack	LIB	LIB
Eden-Monaro	ALP	ALP
Fadden	LNP	LNP
Fairfax	LNP	LNP
Farrer	LIB	LIB
Fenner	ALP	ALP
Fisher	LNP	LNP
Flinders	LIB	LIB
Flynn	LNP	LNP
Forde	LNP	LNP
Forrest	LIB	LIB
Fowler	ALP	ALP
Franklin	ALP	ALP
Fremantle	ALP	ALP
Gellibrand	ALP	ALP
Gilmore	LIB	LIB
Gippsland	NAT	NAT
Goldstein	LIB	LIB
Gorton	ALP	ALP
Grayndler	ALP	ALP
Greenway	ALP	ALP
Grey	NXT	LIB
Griffith	ALP	ALP
Groom	LNP	LNP
Hasluck	LIB	LIB
Herbert	LNP	ALP
Higgins	LIB	LIB
Hindmarsh	ALP	ALP
Hinkler	LNP	LNP
Holt	ALP	ALP
Hotham	ALP	ALP
Hughes	LIB	LIB
Hume	LIB	LIB
Hunter	ALP	ALP
Indi	IND	IND
Isaacs	ALP	ALP
Jagajaga	ALP	ALP
Kennedy	KAP	KAP
Kingsford Smith	ALP	ALP
Kingston	ALP	ALP
Kooyong	LIB	LIB
La Trobe	LIB	LIB
Lalor	ALP	ALP
Leichhardt	LNP	LNP
Lilley	ALP	ALP
Lindsay	LIB	ALP
Lingiari	ALP	ALP
Longman	LNP	ALP
Lyne	NAT	NAT
Lyons	ALP	ALP
Macarthur	LIB	ALP
Mackellar	LIB	LIB
Macquarie	ALP	ALP
Makin	ALP	ALP
Mallee	NAT	NAT
Maranoa	LNP	LNP
Maribyrnong	ALP	ALP
Mayo	NXT	NXT
McEwen	ALP	ALP
McMahon	ALP	ALP
McMillan	LIB	LIB
McPherson	LNP	LNP
Melbourne	GRN	GRN
Melbourne Ports	ALP	ALP
Menzies	LIB	LIB
Mitchell	LIB	LIB
Moncrieff	LNP	LNP
Moore	LIB	LIB
Moreton	ALP	ALP
Murray	LIB	NAT
New England	IND	NAT
Newcastle	ALP	ALP
North Sydney	LIB	LIB
O'Connor	LIB	LIB
Oxley	ALP	ALP
Page	ALP	NAT
Parkes	NAT	NAT
Parramatta	ALP	ALP
Paterson	ALP	ALP
Pearce	LIB	LIB
Perth	ALP	ALP
Petrie	ALP	LNP
Port Adelaide	ALP	ALP
Rankin	ALP	ALP
Reid	LIB	LIB
Richmond	ALP	ALP
Riverina	NAT	NAT
Robertson	LIB	LIB
Ryan	LNP	LNP
Scullin	ALP	ALP
Shortland	ALP	ALP
Solomon	ALP	ALP
Stirling	LIB	LIB
Sturt	LIB	LIB
Swan	LIB	LIB
Sydney	ALP	ALP
Tangney	LIB	LIB
Wakefield	ALP	ALP
Wannon	LIB	LIB
Warringah	LIB	LIB
Watson	ALP	ALP
Wentworth	LIB	LIB
Werriwa	ALP	ALP
Whitlam	ALP	ALP
Wide Bay	LNP	LNP
Wills	ALP	ALP
Wright	LNP	LNP

The senate result...

AUSTRALIAN CAPITAL TERRITORY

Predicted: 1 ALP, 1 LIB

Result: 1 ALP, 1 LIB

Discussion: Exactly as predicted. In the territories, there are only two seats. It's pretty much a given that one major party will win the first, and very rare that the other major party cannot scrape together the rest.

NEW SOUTH WALES

Predicted: 5 ALP, 4 LIB, 1GRN, 1 LDP, 1 PUP

Result: 4 ALP, 3 LIB, 2 NAT, 1 GRN, 1 DLP, 1 ONP

Discussion: 9 right, 3 wrong. One Nation took the predicted Palmer United seat, from a combination of Palmer's personal unpopularity and whatever you personally believe motivates Pauline Hanson supporters: national pride or racism. Labor and Liberal both performed worse than expected, with the Nationals picking up the seats in a state that has always been very strong for them.

NORTHERN TERRITORY

Predicted: 1 ALP, 1 LIB

Result: 1 ALP, 1 LIB

Discussion: Exactly as predicted. In the territories, there are only two seats. It's pretty much a given that one major party will win the first, and very rare that the other major party cannot scrape together the rest.

QUEENSLAND

Predicted: 5 LNP, 4 ALP, 1 GRN, 1 GLT, 1 KAP

Result: 5 LNP, 4 ALP, 2 ONP, 1 GRN

Discussion: While correctly calling the major parties and greens, the minor parties were wrongly predicted. I had considered One Nation a possible contender to take Glen Lazarus's senate seat, their home-state advantages canceling each other out and more or less decided by coin flip. I was genuinely surprised the ONP managed to also dislodge Katter's candidate in their home state too.

SOUTH AUSTRALIA

Predicted: 4 ALP, 4 LIB, 2 NXT, 1 FFP, 1 GRN

Result: 4 LIB, 3 ALP, 3 NXT, 1 FFP, 1 GRN

Discussion: The only correction required would be to switch out a Labor seat for a Xenophon seat. This just goes to show you should never underestimate Nick Xenophon in his home state.

TASMANIA

Predicted: 5 ALP, 4 LIB, 2 GRN, 1 JLN

Result: 5 ALP, 4 LIB, 2 GRN, 1 JLN

Discussion: Called it!

VICTORIA

Predicted: 5 ALP, 5 LIB, 2 GRN

Result: 4 ALP, 4 LIB, 2 GRN, 1 DHJ, 1 NAT

Discussion: Hand one Liberal prediction to their Coalition partners and one Labor to Derryn Hinch. In the first case I should have given more credit to the Nationals in such a traditionally strong state for that party. In the second, I completely underestimated the Derryn Hinch Justice Party who, probably, also got a little luck from the random rollings of the senate's deep ballots.

WESTERN AUSTRALIA

Predicted: 5 LIB, 4 ALP, 1 GRN, 1 NAT, 1 PUP

Result: 5 LIB, 4 ALP, 2 GRN, 1 ONP

Discussion: Looks like I underestimated One Nation across the board. It's the party that just won't die. It's tempting to assume One Nation replaced the waning other minor party from Queensland, the Palmer United Party, which would mean the Greens snatched a seat from the Natss in something of a coup for the left wing of politics. In reality, though, the PUP protest vote probably fell back to the Greens on the whole and the NAT vote stayed on the right with Pauline Hanson. If anything, this is not a left-swing but rather a shift away from the centre to more peripheral or extreme views.

TOTAL ERRORS: 10

ACCURACY: 66/76 (~87%)

I'm honestly quite happy with that result, especially since most of those errors were with regards to the distribution of minor parties which is always a fickle thing. Oh, for the days of the tossup...

All in all those were quite decent predictions in both houses, I feel.

US Presidential

My predictions, however, were not quite so decent here. In short, I backed the wrong horse. However, the predictions were on a state-by-state basis, so lets look at those:

STATE	PREDICTION	RESULT
ALABAMA	TRUMP	TRUMP
ALASKA	TRUMP	TRUMP
ARIZONA	TRUMP	TRUMP
ARKANSAS	TRUMP	TRUMP
CALIFORNIA	CLINTON	CLINTON
COLORADO	CLINTON	CLINTON
CONNECTICUT	CLINTON	CLINTON
DELAWARE	CLINTON	CLINTON
DISTRIC OF COLUMBIA	CLINTON	CLINTON
FLORIDA	CLINTON	TRUMP
GEORGIA	TRUMP	TRUMP
HAWAII	CLINTON	CLINTON
IDAHO	TRUMP	TRUMP
ILLINOIS	CLINTON	CLINTON
INDIANA	TRUMP	TRUMP
IOWA	TRUMP	TRUMP
KANSAS	TRUMP	TRUMP
KENTUCKY	TRUMP	TRUMP
LOUISIANA	TRUMP	TRUMP
MAINE	CLINTON	CLINTON
MARYLAND	CLINTON	CLINTON
MASSACHUSETTS	CLINTON	CLINTON
MICHIGAN	CLINTON	TRUMP
MINNESOTA	CLINTON	CLINTON
MISSISSIPPI	TRUMP	TRUMP
MISSOURI	TRUMP	TRUMP
MONTANA	TRUMP	TRUMP
NEBRASKA	TRUMP	TRUMP
NEVADA	TRUMP	CLINTON
NEW HAMPSHIRE	CLINTON	CLINTON
NEW JERSEY	CLINTON	CLINTON
NEW MEXICO	CLINTON	CLINTON
NEW YORK	CLINTON	CLINTON
NORTH CAROLINA	TRUMP	TRUMP
NORTH DAKOTA	TRUMP	TRUMP
OHIO	CLINTON	TRUMP
OKLAHOMA	TRUMP	TRUMP
OREGON	CLINTON	CLINTON
PENNSYLVANIA	CLINTON	TRUMP
RHODE ISLAND	CLINTON	CLINTON
SOUTH CAROLINA	TRUMP	TRUMP
SOUTH DAKOTA	TRUMP	TRUMP
TENNESSEE	TRUMP	TRUMP
TEXAS	TRUMP	TRUMP
UTAH	TRUMP	TRUMP
VERMONT	CLINTON	CLINTON
VIRGINIA	CLINTON	CLINTON
WASHINGTON	CLINTON	CLINTON
WEST VIRGINIA	TRUMP	TRUMP
WISCONSIN	CLINTON	TRUMP
WYOMING	TRUMP	TRUMP

Now the good news, psephologically speaking, is I got 44 states right (88%), and 51/57 if you count Maine 1-4 and Nebraska 1-5 as separate races. The bad news is that I got some critical states wrong, such as Florida (29 electoral college seats) and Pennsylvania (20 seats) which would have brought the margin between the candidates within 7 seats of a reversal.

As I said before regarding the Brexit vote, it's very easy to blame bad polling. And there is no doubt that there was a hidden Trump vote for one reason or another. But if the polling is so unreliable then we should either find a better source of data or, at the very least, not rely on it so heavily. Still, 88% is not a terrible result.

The Ghost of Elections Yet to Come

The Infographinomicon will be back in 2017, with a much lighter load of elections to cover; domestically only WA has an election officially scheduled for 2017 (March 11, 2017 to be exact), although both the Queensland and Tasmanian state elections may be held before their 2018 deadlines.

Internationally there are the normal, annual fixtures like the UK local elections and US Gubernatorial cycle. Closer to home, New Zealand will hold a general election some time in 2017. We're also due for another Indian election, and while I didn't cover it last time I do remember the scale of it. Is it odd that I can say “I feel like we just had an Indian presidential election” honestly? Then there are the South Korean and Iranian presidential elections, both of which will be interesting to watch with regards to the political landscape that Donald Trump will be interacting with in the South China Sea and Middle East respectively. Plus, of course, the interesting impeachment revelations of the South Korean president add an interesting kick to the election.

Normally I don't get time to look at these elections, but with a light load next year, who knows?

The Demise of the Demise of Donald J Trump

Without having looked back at this years posts, I am aware there are at least several elections I have not properly recapped and analysed my predictions. I intend to redress this towards the end of the year with a post considering each election covered by this blog. And there are several elections ahead that I probably will not cover, but I will at least allow myself space to do so. Switzerland will hold a referendum on November 27 regarding the phase-out of nuclear power plants. Austria had a two-stage election for president this year, with the second controversial round being annulled and rescheduled for December 4. And the Gaza Strip could still have its on-again, off-again municipal elections this year.

Despite this, I think the 2016 US presidential election deserves some specific analysis. A full breakdown of my predictions and their accuracy will occur at the end of the year (we are, after all, still awaiting the results of Michigan, pending a possible recount). This will include a discussion of an error I should, in the interest of not hiding my mistakes, point out at the earliest opportunity (i.e. now). Maine and Nebraska were stated to split their electoral college votes proportionally during my predictions. This is not the case; both states deliver two electoral college seats (representing their senators) on a winner-takes-all basis, and the rest by first-past-the-post in their congressional districts (representing their members of the house).

What I will be looking at here, instead, will be the large scale result that surprised me as much as the other pollsters relying on US polling data: the victory of Donald Trump. In the minds of some, this is a result of racist, sexist and generally hateful voters. For others this is a rejection of politics as usual and a desire for change. For yet more, this is a far stronger emotion--revenge upon those who have made America not-great. I'll be comparing these three proposed rationalisations against the results to see which fit the data.

RACISM

There are many metrics that can be used to estimate which US states are the most racist. The most popular have been by analysis of twitter posts and google searches (the latter having first been used in psephological analysis of Obama's electoral prospects), however these are unreliable for certain reasons (including access and use of internet due to varying demographics between states and the fact that many terms flagged as offensive have been reclaimed by minorities and used in a positive context). Instead I'll be using the FBI Hate Crimes Statistics 2015, which while also flawed (such as relying on crimes being reported) is somewhat more rigorous.

STATE	POPULATION			AGENCIES			POPULATION REPORTED %	RACIAL HATE CRIMES
	TOTAL	EXAMINED	% EXAMINED	TOTAL	REPORTING	% REPORTING		REPORTED	CALCULATED	PER MILLION
Alabama	4,858,979	1,252,146	25.8%	34	3	8.8%	2.3%	8	352	72.41
Alaska	738,432	734,820	99.5%	33	4	12.1%	12.1%	7	58	78.59
Arizona	6,828,065	6,622,880	97.0%	101	21	20.8%	20.2%	162	803	117.64
Arkansas	2,978,204	2,754,543	92.5%	279	4	1.4%	1.3%	3	226	75.97
California	39,144,818	39,137,326	100.0%	730	213	29.2%	29.2%	427	1464	37.39
Colorado	5,456,574	5,445,853	99.8%	234	42	17.9%	17.9%	65	363	66.50
Connecticut	3,590,886	3,399,068	94.7%	95	44	46.3%	43.8%	62	141	39.38
Delaware	945,934	945,934	100.0%	60	7	11.7%	11.7%	9	77	81.55
D. C.	672,228	672,228	100.0%	2	2	100.0%	100.0%	23	23	34.21
Florida	20,271,272	5,356,877	26.4%	38	36	94.7%	25.0%	44	176	8.67
Georgia	10,214,860	7,991,234	78.2%	473	7	1.5%	1.2%	32	2764	270.58
Hawaii	1,431,603	0	0.0%	0	0	0.0%	0.0%	0	N/A	N/A
Idaho	1,654,930	1,654,475	100.0%	112	19	17.0%	17.0%	14	83	49.88
Illinois	12,859,995	12,501,008	97.2%	741	43	5.8%	5.6%	59	1046	81.33
Indiana	6,619,680	3,224,755	48.7%	168	18	10.7%	5.2%	43	824	124.45
Iowa	3,123,899	3,105,094	99.4%	237	5	2.1%	2.1%	3	143	45.80
Kansas	2,911,641	2,741,323	94.2%	345	34	9.9%	9.3%	46	496	170.27
Kentucky	4,425,092	4,402,368	99.5%	403	83	20.6%	20.5%	113	551	124.63
Louisiana	4,670,724	3,711,824	79.5%	148	15	10.1%	8.1%	22	273	58.48
Maine	1,329,328	1,329,328	100.0%	184	14	7.6%	7.6%	16	210	158.19
Maryland	6,006,401	6,006,401	100.0%	154	11	7.1%	7.1%	22	308	51.28
Massachusetts	6,794,422	6,566,279	96.6%	342	85	24.9%	24.0%	198	824	121.33
Michigan	9,922,576	9,834,270	99.1%	617	127	20.6%	20.4%	198	971	97.81
Minnesota	5,489,594	5,218,435	95.1%	319	27	8.5%	8.0%	58	721	131.32
Mississippi	2,992,333	763,830	25.5%	43	0	0.0%	0.0%	0	N/A	N/A
Missouri	6,083,672	6,079,483	99.9%	628	28	4.5%	4.5%	70	1571	258.25
Montana	1,032,949	1,023,807	99.1%	101	13	12.9%	12.8%	28	219	212.48
Nebraska	1,896,190	1,821,196	96.0%	227	3	1.3%	1.3%	10	788	415.48
Nevada	2,890,845	2,890,845	100.0%	53	6	11.3%	11.3%	36	318	110.00
New Hamp.	1,330,608	1,267,715	95.3%	168	9	5.4%	5.1%	8	157	117.80
New Jersey	8,958,013	8,956,395	100.0%	508	123	24.2%	24.2%	169	698	77.93
New Mexico	2,085,109	574,972	27.6%	18	2	11.1%	3.1%	8	261	125.22
New York	19,795,791	19,766,342	99.9%	575	60	10.4%	10.4%	136	1305	65.94
North Carolina	10,042,802	10,041,690	100.0%	532	52	9.8%	9.8%	106	1085	108.00
North Dakota	756,927	756,927	100.0%	112	19	17.0%	17.0%	29	171	225.84
Ohio	11,613,423	9,781,677	84.2%	595	109	18.3%	15.4%	309	2003	172.44
Oklahoma	3,911,338	3,896,985	99.6%	351	29	8.3%	8.2%	27	328	83.86
Oregon	4,028,977	1,671,416	41.5%	130	16	12.3%	5.1%	41	803	199.31
Pennsylvania	12,802,503	12,550,581	98.0%	1,436	26	1.8%	1.8%	40	2254	176.03
Rhode Island	1,056,298	1,056,298	100.0%	49	8	16.3%	16.3%	6	37	34.79
South Carolina	4,896,146	4,826,241	98.6%	436	40	9.2%	9.0%	44	487	99.37
South Dakota	858,469	782,152	91.1%	121	9	7.4%	6.8%	9	133	154.70
Tennessee	6,600,299	6,600,299	100.0%	463	61	13.2%	13.2%	157	1192	180.55
Texas	27,469,114	27,390,337	99.7%	1,026	62	6.0%	6.0%	107	1776	64.65
Utah	2,995,919	2,966,781	99.0%	133	22	16.5%	16.4%	29	177	59.09
Vermont	626,042	626,042	100.0%	90	5	5.6%	5.6%	5	90	143.76
Virginia	8,382,993	8,380,278	100.0%	414	58	14.0%	14.0%	108	771	91.99
Washington	7,170,351	7,163,444	99.9%	256	79	30.9%	30.8%	160	519	72.38
West Virginia	1,844,128	1,494,503	81.0%	229	12	5.2%	4.2%	28	659	357.53
Wisconsin	5,771,337	5,580,752	96.7%	395	25	6.3%	6.1%	27	441	76.44
Wyoming	586,107	564,577	96.3%	57	1	1.8%	1.7%	2	118	201.92

With the exception of the 2015 state populations, derived from the US Census Bureau's population estimates (XLSX, CSV), all data is taken from the FBI report (specifically tables 12 and 13) or calculated therefrom:

• Population: % examined ('Population: examined'/'Population: total' x 100%)
• the proportion of the population covered by the FBI's research 
• Agencies: % reporting ('Agencies: reporting'/'Agencies: total' x 100%)
• the proportion of affiliated agencies (which cover the population examined) providing data
• Population reported % ('Population: % examined' x 'Agencies: % reporting')
• the estimated proportion of the population for which data is available
• does not account for variation in agency size
• Racial hate crimes: calculated ('Racial hate crimes: reported' x 100%/'population reported %')
• number of hate crimes in each state, extrapolated from the estimated proportion of the population for which data is available
• does not account for variation in reporting practices
• Racial hate crimes: per million ('Racial hate crimes: calculated'/'Population total' x 1,000,000)
• number of hate crimes calculated to have occurred, per million people residing in the state

On these numbers, the most racist states are Nebraska, West Virginia, Georgia, Missouri and North Dakota. Data is not fully available for Hawaii and Mississippi. This data differs from the previously linked data, although West Virginia is also over-represented in anti-black tweets and racist google searches.

STATE	HATE CRIMES PER MILLION	TRUMP SUPPORT
Alabama	72.41	62.9%
Alaska	78.59	52.9%
Arizona	117.64	49.5%
Arkansas	75.97	60.4%
California	37.39	33.2%
Colorado	66.50	44.4%
Connecticut	39.38	41.7%
Delaware	81.55	41.9%
District of Columbia	34.21	4.1%
Florida	8.67	49.1%
Georgia	270.58	51.3%
Hawaii	N/A	30.1%
Idaho	49.88	59.2%
Illinois	81.33	39.4%
Indiana	124.45	57.2%
Iowa	45.80	51.8%
Kansas	170.27	57.2%
Kentucky	124.63	62.5%
Louisiana	58.48	58.1%
Maine	158.19	45.2%
Maryland	51.28	60.5%
Massachusetts	121.33	33.5%
Michigan	97.81	47.6%
Minnesota	131.32	45.4%
Mississippi	N/A	39.7%
Missouri	258.25	57.1%
Montana	212.48	56.5%
Nebraska	415.48	60.3%
Nevada	110.00	45.5%
New Hampshire	117.80	47.3%
New Jersey	77.93	41.8%
New Mexico	125.22	40.0%
New York	65.94	37.5%
North Carolina	108.00	50.5%
North Dakota	225.84	64.1%
Ohio	172.44	52.1%
Oklahoma	83.86	65.3%
Oregon	199.31	41.1%
Pennsylvania	176.03	48.8%
Rhode Island	34.79	39.8%
South Carolina	99.37	54.9%
South Dakota	154.70	61.5%
Tennessee	180.55	61.1%
Texas	64.65	52.6%
Utah	59.09	46.6%
Vermont	143.76	32.6%
Virginia	91.99	45.0%
Washington	72.38	38.3%
West Virginia	357.53	68.7%
Wisconsin	76.44	47.9%
Wyoming	201.92	70.1%
Pearson's Correlation	0.41

While there is a low correlation between primary votes for Trump ~(+.41) there are some obvious outliers; in particular the least racist state by our metrics is Florida (which many may disagree with) yet this was famously won by Trump (though by less than 50% of the popular vote).

SEXISM

Sexism has largely been studies by the same means as racism in the past, particularly through twitter posts. Since the FBI also records hate crimes motivated by gender, it is possible to perform the same analysis as above. However, there is such limited data that this does not provide much useful data:

STATE	POPULATION			AGENCIES			POPULATION REPORTED	RACIAL HATE CRIMES
	TOTAL	EXAMINED	% EXAMINED	TOTAL	REPORTING	% REPORTING		REPORTED	CALCULATED	PER MILLION
Alabama	4,858,979	1,252,146	25.8%	34	3	8.8%	2.3%	0	0	0.00
Alaska	738,432	734,820	99.5%	33	4	12.1%	12.1%	0	0	0.00
Arizona	6,828,065	6,622,880	97.0%	101	21	20.8%	20.2%	0	0	0.00
Arkansas	2,978,204	2,754,543	92.5%	279	4	1.4%	1.3%	0	0	0.00
California	39,144,818	39,137,326	100.0%	730	213	29.2%	29.2%	1	3	0.09
Colorado	5,456,574	5,445,853	99.8%	234	42	17.9%	17.9%	0	0	0.00
Connecticut	3,590,886	3,399,068	94.7%	95	44	46.3%	43.8%	0	0	0.00
Delaware	945,934	945,934	100.0%	60	7	11.7%	11.7%	0	0	0.00
D.C.	672,228	672,228	100.0%	2	2	100.0%	100.0%	0	0	0.00
Florida	20,271,272	5,356,877	26.4%	38	36	94.7%	25.0%	0	0	0.00
Georgia	10,214,860	7,991,234	78.2%	473	7	1.5%	1.2%	0	0	0.00
Hawaii	1,431,603	0	0.0%	0	0	0.0%	0.0%	0	N/A	N/A
Idaho	1,654,930	1,654,475	100.0%	112	19	17.0%	17.0%	0	0	0.00
Illinois	12,859,995	12,501,008	97.2%	741	43	5.8%	5.6%	1	18	1.38
Indiana	6,619,680	3,224,755	48.7%	168	18	10.7%	5.2%	1	19	2.89
Iowa	3,123,899	3,105,094	99.4%	237	5	2.1%	2.1%	0	0	0.00
Kansas	2,911,641	2,741,323	94.2%	345	34	9.9%	9.3%	1	11	3.70
Kentucky	4,425,092	4,402,368	99.5%	403	83	20.6%	20.5%	0	0	0.00
Louisiana	4,670,724	3,711,824	79.5%	148	15	10.1%	8.1%	0	0	0.00
Maine	1,329,328	1,329,328	100.0%	184	14	7.6%	7.6%	0	0	0.00
Maryland	6,006,401	6,006,401	100.0%	154	11	7.1%	7.1%	0	0	0.00
Massachusetts	6,794,422	6,566,279	96.6%	342	85	24.9%	24.0%	15	62	9.19
Michigan	9,922,576	9,834,270	99.1%	617	127	20.6%	20.4%	1	5	0.49
Minnesota	5,489,594	5,218,435	95.1%	319	27	8.5%	8.0%	0	0	0.00
Mississippi	2,992,333	763,830	25.5%	43	0	0.0%	0.0%	0	N/A	N/A
Missouri	6,083,672	6,079,483	99.9%	628	28	4.5%	4.5%	0	0	0.00
Montana	1,032,949	1,023,807	99.1%	101	13	12.9%	12.8%	0	0	0.00
Nebraska	1,896,190	1,821,196	96.0%	227	3	1.3%	1.3%	0	0	0.00
Nevada	2,890,845	2,890,845	100.0%	53	6	11.3%	11.3%	0	0	0.00
New Hamp.	1,330,608	1,267,715	95.3%	168	9	5.4%	5.1%	0	0	0.00
New Jersey	8,958,013	8,956,395	100.0%	508	123	24.2%	24.2%	0	0	0.00
New Mexico	2,085,109	574,972	27.6%	18	2	11.1%	3.1%	0	0	0.00
New York	19,795,791	19,766,342	99.9%	575	60	10.4%	10.4%	0	0	0.00
North Carolina	10,042,802	10,041,690	100.0%	532	52	9.8%	9.8%	0	0	0.00
North Dakota	756,927	756,927	100.0%	112	19	17.0%	17.0%	0	0	0.00
Ohio	11,613,423	9,781,677	84.2%	595	109	18.3%	15.4%	0	0	0.00
Oklahoma	3,911,338	3,896,985	99.6%	351	29	8.3%	8.2%	0	0	0.00
Oregon	4,028,977	1,671,416	41.5%	130	16	12.3%	5.1%	0	0	0.00
Pennsylvania	12,802,503	12,550,581	98.0%	1,436	26	1.8%	1.8%	0	0	0.00
Rhode Island	1,056,298	1,056,298	100.0%	49	8	16.3%	16.3%	0	0	0.00
South Carolina	4,896,146	4,826,241	98.6%	436	40	9.2%	9.0%	0	0	0.00
South Dakota	858,469	782,152	91.1%	121	9	7.4%	6.8%	0	0	0.00
Tennessee	6,600,299	6,600,299	100.0%	463	61	13.2%	13.2%	1	8	1.15
Texas	27,469,114	27,390,337	99.7%	1,026	62	6.0%	6.0%	1	17	0.60
Utah	2,995,919	2,966,781	99.0%	133	22	16.5%	16.4%	0	0	0.00
Vermont	626,042	626,042	100.0%	90	5	5.6%	5.6%	0	0	0.00
Virginia	8,382,993	8,380,278	100.0%	414	58	14.0%	14.0%	0	0	0.00
Washington	7,170,351	7,163,444	99.9%	256	79	30.9%	30.8%	3	10	1.36
West Virginia	1,844,128	1,494,503	81.0%	229	12	5.2%	4.2%	0	0	0.00
Wisconsin	5,771,337	5,580,752	96.7%	395	25	6.3%	6.1%	1	16	2.83
Wyoming	586,107	564,577	96.3%	57	1	1.8%	1.7%	0	0	0.00

For the curious, there is a negligible negative correlation between this limited data and support for Trump (~-0.15).

Instead, I'll be using the rankings produced by WalletHub, which has combined a number of metrics more effectively than I could. For the large number of people that do not understand that the actual enemy of feminism is not men but the enforcement of gender roles, and that metrics that represent male disadvantage (e.g. incarceration rates and duration, length of working hours, child custody rates, life span etc.) are a direct result of these roles which, although disadvantaging men in these instances, have historically been enforced to limit female empowerment, allow me to point out that these metrics have been combined into this study even though the data is billed as regarding "women's equality". In other words, even though the majority of Trump's rhetoric relevant to the claim that Trump's support was supported by sexism has been directed against women, the inclusion of data highlighting examples of male disadvantage is relevant as it indicates more traditional, gender-role enforcing attitudes in the relevant states.

Interestingly, the correlation is again negative, but low at ~0.30:

STATE	WALLETHUB SCORE	TRUMP SUPPORT
Alabama	50.61	62.9%
Alaska	72.46	52.9%
Arizona	57.31	49.5%
Arkansas	51.57	60.4%
California	69.1	33.2%
Colorado	57.37	44.4%
Connecticut	51.46	41.7%
Delaware	54.22	41.9%
District of Columbia	N/A	4.1%
Florida	55.73	49.1%
Georgia	44.46	51.3%
Hawaii	81.67	30.1%
Idaho	50.29	59.2%
Illinois	58.59	39.4%
Indiana	56.35	57.2%
Iowa	58.37	51.8%
Kansas	53.92	57.2%
Kentucky	47.49	62.5%
Louisiana	45.45	58.1%
Maine	69.49	45.2%
Maryland	65.09	60.5%
Massachusetts	58.65	33.5%
Michigan	55.5	47.6%
Minnesota	66.1	45.4%
Mississippi	46.89	39.7%
Missouri	59.22	57.1%
Montana	51.58	56.5%
Nebraska	57.86	60.3%
Nevada	58.05	45.5%
New Hampshire	66.4	47.3%
New Jersey	44.97	41.8%
New Mexico	58.33	40.0%
New York	63.32	37.5%
North Carolina	50.42	50.5%
North Dakota	61.37	64.1%
Ohio	48.83	52.1%
Oklahoma	48.3	65.3%
Oregon	60.18	41.1%
Pennsylvania	45.15	48.8%
Rhode Island	49.08	39.8%
South Carolina	45.8	54.9%
South Dakota	57.86	61.5%
Tennessee	52.8	61.1%
Texas	49.77	52.6%
Utah	33.7	46.6%
Vermont	68.16	32.6%
Virginia	48.67	45.0%
Washington	61.93	38.3%
West Virginia	59.72	68.7%
Wisconsin	62.75	47.9%
Wyoming	57.11	70.1%
Pearson's Correlation	-0.30

POLITICAL DISCONTENT

Measuring voter discontent is difficult in the current election. Normally these votes emerge as protest votes or failure to vote. A higher 3rd party vote, for example, may indicate dissatisfaction with either candidate but (ignoring that dissatisfaction in this election was high in part due to Trump's candidacy) there is little to be learned by comparing Trump support with 3rd party results. Does a positive correlation indicate Trump is popular where voters are discontent? Or does a negative correlation indicate that he is absorbing the discontent vote?

Two proxy measures of discontent present themselves: 3rd party voting and non-voting in 2012 (inherently assuming this discontent is not the result of the last 4 years and was present in that data) and 3rd party voting in each state's other federal races this November. Both are problematic, of course. The former assumes little or no change in almost half a decade. The latter ignores the possible swell of Trump voters who turned out to vote (and who normally would not) due to the fervent support the candidate produced but didn't care about other races and therefore deliberately "wasted" their vote. I have gone with the former for a number of reasons, including the nature of Senate elections that mean not all states have a candidate for us to consider, and the frequent absence of any third party candidate in many HoR seats.

STATE	2012 TURNOUT	TRUMP SUPPORT		STATE	3RD PARTY	TRUMP SUPPORT
Alabama	58.60%	62.9%		Alabama	1.09%	62.9%
Alaska	58.70%	52.9%		Alaska	4.39%	52.9%
Arizona	52.60%	49.5%		Arizona	1.76%	49.5%
Arkansas	50.70%	60.4%		Arkansas	2.55%	60.4%
California	55.10%	33.2%		California	2.64%	33.2%
Colorado	69.90%	44.4%		Colorado	2.38%	44.4%
Connecticut	61.30%	41.7%		Connecticut	1.21%	41.7%
Delaware	62.30%	41.9%		Delaware	1.41%	41.9%
District of Columbia	61.50%	4.1%		District of Columbia	1.81%	4.1%
Florida	62.80%	49.1%		Florida	0.86%	49.1%
Georgia	59.00%	51.3%		Georgia	1.22%	51.3%
Hawaii	44.20%	30.1%		Hawaii	1.61%	30.1%
Idaho	59.80%	59.2%		Idaho	2.85%	59.2%
Illinois	58.90%	39.4%		Illinois	1.67%	39.4%
Indiana	55.20%	57.2%		Indiana	1.94%	57.2%
Iowa	70.30%	51.8%		Iowa	1.83%	51.8%
Kansas	56.90%	57.2%		Kansas	2.30%	57.2%
Kentucky	55.70%	62.5%		Kentucky	1.71%	62.5%
Louisiana	60.20%	58.1%		Louisiana	1.64%	58.1%
Maine	68.20%	45.2%		Maine	2.75%	45.2%
Maryland	66.60%	60.5%		Maryland	2.13%	60.5%
Massachusetts	65.90%	33.5%		Massachusetts	1.84%	33.5%
Michigan	64.70%	47.6%		Michigan	1.08%	47.6%
Minnesota	76.00%	45.4%		Minnesota	2.39%	45.4%
Mississippi	59.30%	39.7%		Mississippi	0.92%	39.7%
Missouri	62.20%	57.1%		Missouri	1.86%	57.1%
Montana	62.50%	56.5%		Montana	2.95%	56.5%
Nebraska	60.30%	60.3%		Nebraska	2.17%	60.3%
Nevada	56.40%	45.5%		Nevada	1.96%	45.5%
New Hampshire	70.20%	47.3%		New Hampshire	1.62%	47.3%
New Jersey	61.50%	41.8%		New Jersey	1.03%	41.8%
New Mexico	54.60%	40.0%		New Mexico	4.17%	40.0%
New York	53.10%	37.5%		New York	1.48%	37.5%
North Carolina	64.80%	50.5%		North Carolina	1.26%	50.5%
North Dakota	59.80%	64.1%		North Dakota	2.99%	64.1%
Ohio	64.50%	52.1%		Ohio	1.64%	52.1%
Oklahoma	49.20%	65.3%		Oklahoma	0.00%	65.3%
Oregon	63.10%	41.1%		Oregon	3.61%	41.1%
Pennsylvania	59.50%	48.8%		Pennsylvania	1.44%	48.8%
Rhode Island	58.00%	39.8%		Rhode Island	2.06%	39.8%
South Carolina	56.30%	54.9%		South Carolina	1.35%	54.9%
South Dakota	59.30%	61.5%		South Dakota	2.24%	61.5%
Tennessee	51.90%	61.1%		Tennessee	1.44%	61.1%
Texas	49.60%	52.6%		Texas	1.45%	52.6%
Utah	55.50%	46.6%		Utah	2.46%	46.6%
Vermont	60.70%	32.6%		Vermont	2.46%	32.6%
Virginia	66.10%	45.0%		Virginia	1.56%	45.0%
Washington	64.80%	38.3%		Washington	2.55%	38.3%
West Virginia	46.30%	68.7%		West Virginia	2.16%	68.7%
Wisconsin	72.90%	47.9%		Wisconsin	1.28%	47.9%
Wyoming	58.60%	70.1%		Wyoming	3.54%	70.1%
Pearson's Correlation	-0.17			Pearson's Correlation	0.03

Both non-voting and 3rd-party voting in 2012 show little correlation to support for Trump in 2016, though both nominally in the direction that would suggest Trump served as a vessel for protest votes: a slight negative correlation with 2012 voter turnout and slight positive correlation with 2012 3rd party results. For voter turnout, I'm using the eligible voter highest office data from the United States Electoral Project.

REVENGE OF ANTIGLOBALISTS

This last rationalisation is the one that is often cited in the media as the rise of "angry white men". This rationalisation, though largely ignored in the earlier campaign, is now the dominant focus of media attention. This rationalisation is often portrayed as white, middle-aged men--the pale, stale males--enraged at the rule of a Black President, furious at the prospect of a female one and livid at the shift of American culture towards one of inclusiveness and multiculturalism. However, there's another side to this view. One in which, despite their privilege, these men have faced genuine hardships. One in which their anger is not directed in violence against the non-white, non-male population which we considered earlier. This is a view that, in my opinion, was best explained by documentary filmmaker Michael Moore in clarity I could not hope to equal.

In this view, all of this anger (if we accept that it is distinct from the racism and sexism analysed above) comes from financial hardship and the social effects it has had. Despite the disparity in real living conditions between White and Non-White on average, White men are angrier than Black and feel as though they were made a promise that was then ripped away from them. What matters isn't necessarily that these individuals are doing it tough, but that they feel they are doing it tough, or tougher than they are owed. For a long time America has persisted on the lie that if you work hard and deserve success you can achieve it. The dark corollary to this assertion, unchallenged and patriotically insisted upon, is that if you don't have success it's because you don't deserve it. And when that lack of success falls on minorities, that rationalisation follows easily. It also deprived any impetus for support to the underprivileged. even with minimum welfare, no socialised healthcare and poverty conditions that rival those found in third-world countries, it was easy to feel no obligation for government support. Now that poverty has come for the angry white men too; the ones who dreamed of better things, and the unfairness is revealed. But enough leftist socialist communist bleeding-heart liberal wishy-washy nonsense from me. How does Trump support correlate with job loss and redundancy from, among other things, competition in international manufacture?

Comparisons with state poverty rates and unemployment were negligible. The strongest correlation so far, at ~-0.57, demonstrates a moderate negative correlation between per capita income and support for Trump.

STATE	TRUMP SUPPORT	POVERTY LEVEL	UNEMPLOYMENT	INCOME PER CAPITA
Alabama	62.9%	18.5%	5.4%	$23,606
Alaska	52.9%	10.3%	6.9%	$33,062
Arizona	49.5%	17.4%	5.5%	$25,715
Arkansas	60.4%	19.1%	4.0%	$22,883
California	33.2%	15.3%	5.5%	$30,441
Colorado	44.4%	11.5%	3.6%	$32,357
Connecticut	41.7%	10.5%	5.4%	$39,373
Delaware	41.9%	12.4%	4.3%	$30,488
District of Columbia	4.1%	17.3%	6.1%	$45,877
Florida	49.1%	15.7%	4.7%	$26,582
Georgia	51.3%	17.0%	5.1%	$25,615
Hawaii	30.1%	10.6%	3.3%	$29,736
Idaho	59.2%	15.1%	3.8%	$23,938
Illinois	39.4%	13.6%	5.5%	$30,417
Indiana	57.2%	14.5%	4.5%	$25,140
Iowa	51.8%	12.2%	4.2%	$28,361
Kansas	57.2%	13.0%	4.4%	$27,870
Kentucky	62.5%	18.5%	5.0%	$23,684
Louisiana	58.1%	19.6%	6.4%	$24,800
Maine	45.2%	13.4%	4.1%	$27,978
Maryland	60.5%	9.7%	4.2%	$36,338
Massachusetts	33.5%	11.5%	3.6%	$36,593
Michigan	47.6%	15.8%	4.6%	$26,613
Minnesota	45.4%	10.2%	4.0%	$32,638
Mississippi	39.7%	22.0%	6.0%	$21,036
Missouri	57.1%	14.8%	5.2%	$26,126
Montana	56.5%	14.6%	4.3%	$25,989
Nebraska	60.3%	12.6%	3.2%	$27,446
Nevada	45.5%	14.7%	5.8%	$25,773
New Hampshire	47.3%	8.2%	2.9%	$34,691
New Jersey	41.8%	10.8%	5.3%	$37,288
New Mexico	40.0%	20.4%	6.7%	$23,683
New York	37.5%	15.4%	5.0%	$33,095
North Carolina	50.5%	16.4%	4.7%	$25,774
North Dakota	64.1%	11.0%	3.0%	$33,071
Ohio	52.1%	14.8%	4.8%	$26,937
Oklahoma	65.3%	16.1%	5.2%	$25,229
Oregon	41.1%	15.4%	5.5%	$27,646
Pennsylvania	48.8%	13.2%	5.7%	$29,220
Rhode Island	39.8%	13.9%	5.6%	$30,830
South Carolina	54.9%	16.6%	4.9%	$24,596
South Dakota	61.5%	13.7%	2.9%	$26,959
Tennessee	61.1%	16.7%	4.6%	$24,922
Texas	52.6%	15.9%	4.8%	$27,125
Utah	46.6%	11.3%	3.4%	$24,877
Vermont	32.6%	10.2%	3.3%	$29,178
Virginia	45.0%	11.2%	4.0%	$34,052
Washington	38.3%	12.2%	5.6%	$31,841
West Virginia	68.7%	17.9%	5.8%	$22,714
Wisconsin	47.9%	12.1%	4.1%	$28,213
Wyoming	70.1%	11.1%	5.3%	$29,698
Pearson's Correlation		0.11	-0.13	-0.57

But all of these metrics are too blunt. Measures of general hardship in a state will generally include a large proportion of the Non-White population due to the historical inequality with which these groups were treated. Data from the US Census Bureau allows a state by state breakdown of poverty, unemployment and income for "white alone":

 

STATE	TRUMP SUPPORT	POVERTY LEVEL	UNEMPLOYMENT	INCOME PER CAPITA
Alabama	62.9%	13.7%	5.5%	$28,235
Alaska	52.9%	6.6%	5.9%	$41,133
Arizona	49.5%	15.2%	6.0%	$29,115
Arkansas	60.4%	15.5%	4.9%	$25,864
California	33.2%	14.1%	6.8%	$35,523
Colorado	44.4%	10.4%	4.8%	$35,676
Connecticut	41.7%	7.9%	5.6%	$44,134
Delaware	41.9%	9.6%	5.5%	$34,522
District of Columbia	4.1%	7.1%	3.1%	$81,474
Florida	49.1%	13.6%	6.0%	$30,582
Georgia	51.3%	12.5%	5.2%	$31,583
Hawaii	30.1%	10.1%	5.3%	$40,010
Idaho	59.2%	14.4%	5.1%	$24,935
Illinois	39.4%	10.1%	5.2%	$35,754
Indiana	57.2%	12.2%	5.0%	$27,907
Iowa	51.8%	10.7%	3.6%	$29,973
Kansas	57.2%	11.2%	4.2%	$30,533
Kentucky	62.5%	17.1%	6.1%	$25,942
Louisiana	58.1%	13.2%	5.5%	$30,792
Maine	45.2%	12.7%	5.1%	$29,112
Maryland	60.5%	7.1%	4.2%	$44,016
Massachusetts	33.5%	9.2%	5.2%	$41,556
Michigan	47.6%	12.3%	5.5%	$30,171
Minnesota	45.4%	7.8%	3.5%	$35,930
Mississippi	39.7%	13.6%	5.8%	$26,281
Missouri	57.1%	12.5%	4.6%	$29,155
Montana	56.5%	12.7%	3.9%	$29,352
Nebraska	60.3%	10.6%	2.8%	$30,708
Nevada	45.5%	12.8%	7.4%	$30,382
New Hampshire	47.3%	7.9%	4.1%	$36,589
New Jersey	41.8%	8.2%	5.6%	$41,743
New Mexico	40.0%	17.6%	6.4%	$26,722
New York	37.5%	11.3%	5.1%	$40,483
North Carolina	50.5%	12.7%	5.4%	$30,430
North Dakota	64.1%	9.0%	2.1%	$35,895
Ohio	52.1%	11.6%	5.2%	$30,077
Oklahoma	65.3%	13.2%	4.7%	$28,919
Oregon	41.1%	14.5%	6.5%	$30,269
Pennsylvania	48.8%	10.3%	5.2%	$32,865
Rhode Island	39.8%	10.9%	5.2%	$34,749
South Carolina	54.9%	12.0%	5.6%	$29,899
South Dakota	61.5%	9.6%	2.9%	$30,461
Tennessee	61.1%	14.5%	5.2%	$28,338
Texas	52.6%	14.8%	5.2%	$30,067
Utah	46.6%	10.0%	3.7%	$27,132
Vermont	32.6%	9.7%	3.7%	$31,747
Virginia	45.0%	9.1%	4.5%	$38,687
Washington	38.3%	11.0%	5.6%	$35,872
West Virginia	68.7%	17.4%	7.0%	$23,840
Wisconsin	47.9%	9.6%	3.5%	$31,605
Wyoming	70.1%	10.8%	4.6%	$32,735
Pearson's Correlation		0.33	-0.07	-0.66

Focusing solely on white poverty levels sees the correlation in the data more than double to a point where it is only eclipsed by the racism and per-capita income metrics. However, at 0.33, this is only slightly stronger than the negative correlation between sexism and Trump support at 0.30, and no one is suggesting that Trump won on the back of feminist support.

Using only the White unemployment rates, the correlation becomes even more negligible.

However, there is (by psephological standards) a high negative correlation between white median wages and support for Trump. And while it's true that this merely points out the existence of a certain stereotype--the working class, White Trump voter--understanding voter demographics is important to understanding why Trump got elected contrary to all conventional wisdom. This was a section of the community that turned out to vote, and felt the need to stand up for something.

CONCLUSION

While much of this data operates as a proxy for some other social issue, such as racism or anger at economic leadership, It is important to realise that there are several reasons for Trump's support. A candidate does not get elected by a single demographic. Not solely by a racist element, or by disillusioned working class voters, or conspiracy theorists who think Hillary Clinton orchestrated World War I from her Russian space-dreadnaught. Of the various reasons studied two comparatively strong correlations emerged.

For those who like to argue Trump supporters are not racist, it is worth noting that states prone to racial violence tend to be more pro-Trump. This should hardly need pointing out for a candidate endorsed by the Klan, but some not-insignificant part of Trump's supporter base comes from a xenophobia that has not been given a political vent as prominent or blatant as Trump before.

On the other hand, for those who, on the other hand, argue that all Trump supporters are hateful racists, it is noteworthy that being from a poor state, and particularly a state with a poor White population, is a better indicator of support for Trump than being from a state with more racially motivated Hate Crimes; some might, of course, argue that voting for Trump in and of itself should be a hate crime.

It is hard for some people to understand how anyone could support a candidate who has voiced the opinions Trump has and not inherently be a biggot. To these people I would suggest the voters need not agree with Trump's statements, only to rationalise them as 'speaking his mind', 'not what he meant', 'locker-room talk' and so forth. People who voted for Trump committed to one aspect or another of his personality or platform--as did Clinton supporters--and forgave the many flaws of their preferred candidate while exaggerating the other's.

There are, of course, many other factors--Trump's ability to seize the zeitgeist, Clinton's many issues with engaging voters, the vast free publicity news outlets offered Trump while underestimating his potential, Trump's ability to give vague and often contradictory promises worded so as to let each listener take what they liked and disregard the rest and so forth. But there are several reasons voters latched on to Trump, some good and some less so.

TL;DR: There is some evidence that Trump had support due to his racist statements. He also appealed to a class of disenfranchised voter with legitimate (though arguably misdirected) grievances against the current system to the extend that they'd happily burn the whole thing down. There would have been other reasons as well, but the portrayal of Trump supporters as rampant racists or as righteous rebels are both partially correct, and partially incorrect.