2016 White and Non-White Vote by County Project

[Antonio the Sixth]

This is really great (though also depressing)! Thanks Reagente and Adam.

I know you've discussed it a bit already, but would you mind explaining your respective methodologies and how they differ to a beginner?

[Antonio the Sixth]

I built in a elasticity to the non-White vote into my model, operating under the assumption that non-Whites in more Republican areas will be more inclined to support the GOP.

Do we have any empirical evidence for this? It seems reasonable to make this assumption for Asians and Hispanics, but my guess would be that Blacks vote pretty uniformly everywhere in the country.

[OneJ]

I built in a elasticity to the non-White vote into my model, operating under the assumption that non-Whites in more Republican areas will be more inclined to support the GOP.

Do we have any empirical evidence for this? It seems reasonable to make this assumption for Asians and Hispanics, but my guess would be that Blacks vote pretty uniformly everywhere in the country.

That's what I think. When you look at exit polls, you will see that Blacks voted like 88-91% in most states for Hillary (7-10% for Trump). On the other hand, Latinos and Asians' results varied a bit more depending on which state they lived.

[Chunk Yogurt for President!]

Winston County, AL on the non-white map was interesting.  IIRC that county has always been a Republican stronghold and wanted to remain in the Union in the ACW.

[Adam Griffin]

Any chance you can make the original sheet(s) you used available for access, where each county is available by row? I tried to download the set from the maps themselves but it formats the data into something that is not usable. I'm not sure if it's a sharing setting that has it hidden via the link or something else. Sometimes you can just link to it directly by using the link available when editing it (as opposed to the sharing link for embeds, etc). Usually the sheets in my projects have something like "Row 1", "Card 1", and (by default) "map of geometry". That first one is the one to which I'm referring. 

[Adam Griffin]

This is really great (though also depressing)! Thanks Reagente and Adam.

I know you've discussed it a bit already, but would you mind explaining your respective methodologies and how they differ to a beginner?

I could be wrong, but beyond questions of where we got our data for the electorate composition, I think the primary difference is how we tabulate the Non-White Vote (which has resultant implications for the White Vote).

I built in a elasticity to the non-White vote into my model, operating under the assumption that non-Whites in more Republican areas will be more inclined to support the GOP. In counties with large minority populations, this has the potential to impact the White vote quite a bit.

On the whole, I think my model for the most part would lead to reduced Trump margins among White Voters in very Republican areas, and correspondingly increased Trump margins among White voters in very Democratic areas when compared to Fmr. Pres. Griffin's model

Yes, this may be the primary difference. My initial effort used a pretty uniform formula for non-white voters (not completely uniform) in my first pass for each state. I think reagente's formula may shift from county to county, depending; mine was initially customized at the state level as far as turnout rates and persuasion go. For instance, black voters could vary between 85-95% D depending on the state and what exit polling showed in 2008/2012. The same adjustments were also made with regard to other non-white groups, but it was not customized initially on a county-by-county basis.

I then proceeded on several revision binges in a handful of states where I could tell that the numbers (usually among Latinos) from such a uniform approach were off by quite a bit in various areas. This was where I began tweaking with various counties, but even then, it was only in a few states. Texas was a nightmare, and even when I finally "finished", I was not very happy with the output in both North Texas and the Rio Grande Valley; I see reagente's model also shows unusually high Democratic white support in the Valley as well, which I concluded - while likely a real phenomenon based on a broader occurrence of whites being more Democratic in very heavily minority areas (rural or urban) - could still be skewed.

So the baseline was built off of Census data, voter registration statistics by race (where available), exit polling data and something else that now slips my mind in order to determine both turnout and persuasion. Certain elements (such as the percentage of a county's Latino population that likely voted, affected by citizenship and age) were customized based on county or region. Outliers were adjusted based on what made sense with regard to the path of least resistance concerning all possible adjustments in turnout/persuasion across groups.

[Adam Griffin]

I built in a elasticity to the non-White vote into my model, operating under the assumption that non-Whites in more Republican areas will be more inclined to support the GOP.

Do we have any empirical evidence for this? It seems reasonable to make this assumption for Asians and Hispanics, but my guess would be that Blacks vote pretty uniformly everywhere in the country.

This is where I'm having some questions in regard to the model.

First, I'm not entirely convinced that (non-Cuban) Latino voting behavior is phenomenally different from place to place based on the two-party vote of a given area. With the exception of parts of Texas, New Mexico and areas where extensive histories of Mexican settlement alongside Anglos have literally melded the communities together and diffused identities, I'm not of the belief that Latinos adopt native voting behavior in short order.

One element that makes this so difficult is that many heavily-GOP areas with large Latino populations tend to be relatively new phenomena and have very high rates of undocumented individuals; when combined with widely-observed Latino voter apathy among even citizen adults and a disproportionate share of citizens being under 40, it means that you need a very substantial Latino population to even constitute a double-digit share of the electorate (a 30% Latino area that has roots in the area for around a generation may only constitute 10% of voters). Outside of Texas, most of your heavily-GOP, heavily-Latino areas are relatively newfound occurrences. Small shares of the population = more liable to larger discrepancies/margins of error in projections.

Based on a variety of data-points available regarding the 2012 election that allowed me to compare my work in various areas to others' findings and actual voter demographics, I came to the conclusion that my model tended to under-represent Obama's white support by a couple of points more often than it over-represented it. This is a reality when building any model that tries to fit 3,100+ entities into a single set of criteria, even with customizations; unless you have the time or energy to grind away and calculate specific formulas for each individual county, you're going to have some outliers. Usually, these outliers are the result of one or more broader assumptions we make that either has more variance than we thought or that under/over-estimates what we consider to be reasonable/accurate.

I do believe reagente's model is being affected by the same dynamic, albeit in a different area. It's grueling work to eliminate all of these discrepancies and arguably impossible. While not necessarily true, I would assume that he constructed his model beginning with one of two basic/broader assumptions (as in, either he made more assumptions about the white vote in order to fill in the blanks on how non-whites voted, or vice-versa). Knowing which (if either) broader assumption made would further assist in seeing if what I'm thinking might be happening is correct.

In short, I think his model is more likely to over-represent non-white Trump support. I think this becomes more evident in Appalachia in particular. In fact, I believe this occurrence in his model is the same dynamic I faced in the Rio Grande Valley: you have one race that is damn near 100% of the population in many areas, so even slight discrepancies or shifts in both turnout and persuasion among that group (which for said group is minuscule and almost meaningless statistically) can cause very large and wild shifts among the minority group's numbers there. In some cases, the actual minority voting bloc could be viewed as a rounding error in and of itself. If there is a consistency to the assumption that results in a over- or under-representation, then it becomes very visible across a larger area. It reminds me of how I could shift Latino support in some of those Rio Grande counties by a few points...and generate a 50 point swing for Obama's white support. It drove me crazy trying to work it all out.

I might write an explanation with regard to my county specifically (which is in Appalachia and has a large Latino population) and how the numbers don't necessarily comport with the shifts in voter turnout by race and the margins between 2012-2016 I've observed in the county's voter file and data, but I'll do that later since this is already so long.

[Adam Griffin]

Any chance you can make the original sheet(s) you used available for access, where each county is available by row? I tried to download the set from the maps themselves but it formats the data into something that is not usable. I'm not sure if it's a sharing setting that has it hidden via the link or something else. Sometimes you can just link to it directly by using the link available when editing it (as opposed to the sharing link for embeds, etc). Usually the sheets in my projects have something like "Row 1", "Card 1", and (by default) "map of geometry". That first one is the one to which I'm referring. 

Is this what you are looking for?

Yes, I believe so - thank you!

[Adam Griffin]

Data from the Congressional Cooperative Election Study makes me somewhat more confident in my model's assessment of the non-White Vote. For example, there was a clear rural-urban divide among the Hispanic vote:

The data coming out from this report has very good insight into a lot of things.

By any chance, do they specify how they determine the four categories of urban/rural, and do they provide crosstabs or percentages for the percentage of Latinos that live in each?

I do still want to come back and talk about my county specifically in a bit relative to this model when I get some time this evening, but one thing I'd point out about it now. I'm pretty sure that by any definition, it would fall into either the "Less Rural" or "More Rural" categories, and here's the kicker: 70% of Latino voters in my county were 18-44 years of age.  

I am betting that in a lot of counties where you have less than marginal Latino populations in heavily GOP areas - even in the more rural parts - the 45+ voting patterns aren't going to be nearly as relevant as one might think, meaning your Latino support would skew much more toward the younger preferences than the older ones. Such is the reality in communities where large majorities of your 45+ populations are non-citizens and nearly all of your under 25s are citizens; it's the predominant reason why Latino voter participation lags even Latino CVAP. They're not necessarily less engaged in a fundamental sense, but rather, a much greater share of their eligible voting bloc is under 40 and therefore behaves like any under sub-40 voting bloc with respect to turnout.

[Adam Griffin]

Also (quite a bit off-topic), I'm wondering if you used the same shapefile I sent you?

The reason I ask is because some time ago - seemingly randomly - my embed maps at the default zoom level suddenly began to have "holes" in it where counties should be, especially in and around Appalachia. If you zoom in, the counties are clearly visible but as you zoom out, there are missing chunks. I noticed yours is working perfectly fine, however. It may just be a glitch on my computer/browser's end that's causing the issues for me on mine, but otherwise, I'm wondering if you did/used something different.

[Adam Griffin]

Since I know there was a similar map made for the 2012 election, is there any chance we could get a swing map between the two? I imagine it would track pretty much with affluent and educated communities generally swinging to Clinton and poorer and less-educated communities swinging to Trump, but I'd be curious to know if there are any exceptions.

It's doable in a sense, but as reagente pointed out, we used different ways of generating our maps. Because 2012 was simple enough in terms of a two-way model, I just focused on calculating Obama's share of the white vote directly (due to the fact that in most places, Romney's share would be ([Obama's share] - 1pt). 2016 was more complex and the way reagente did his makes more sense. We also presumably used different data-sets/combinations of data to make our maps.

Nevertheless, I could pretty easily generate a map that shows the difference between Obama and Clinton's shares of the white vote. It should be pretty accurate in the vast majority of cases, but as I've already noticed, we were in disagreement in some select counties based on the margin difference in them.

So this is the closest thing we can get to that: a simple comparison of the white vote share for Obama and Clinton between the two models. Here's the interactive map. Several of the areas that seem out-of-place I know are the result of the models' differences (for instance, my model is probably the one that is off in Clayton, GA & Imperial, CA).

Red counties/negative numbers indicate Clinton improved over Obama among whites; blue counties/positive numbers indicate Clinton did more poorly than Obama among whites. Numbers are shown as percentage point differences (i.e. Obama got 46% of whites and Clinton got 32% = "14").

[Technocracy Timmy]

Unless I'm reading the map wrong, it looks like Hillary improved among whites who were either wealthier, lived in more ethnically/racially diverse communities, or both.

Trump had quite a few upper class whites nervous about how his administration would affect the economy and the stock market indexes. The campaign he ran probably alienated a lot of moderates who live in multicultural areas.

[DPKdebator]

Based off that map above, it almost seems like MA's swing is due to a growing nonwhite population. Only two counties swung D (Norfolk, where I live, and Middlesex) while the rest of the state (even Suffolk, which has Boston) swung R. According to census estimates, the black population in Plymouth County grew from around 7% of the population to 10% (though they are pretty much entirely in Brockton, the largest city), though it is possible the model is off since some lily-white areas swung pretty strongly to Clinton.

[100% pro-life no matter what]

Good work!  Some seem hard to believe, like the county in Alabama where Trump apparently won the non-white vote.  Obviously, assumptions have to be made, which can skew things and produce a couple weird results.  I'm surprised that you have Clinton winning the Davidson County (TN) white vote.  I figured Trump probably narrowly carried it, but it may be in your margin of error anyway.

[mieastwick]

How does reagente's model have Clinton winning the White vote in Connecticut? The demographics do not appear favorable to this. My model (admittedly, based on less plausible assumptions than reagente's) has Trump winning the non-Hispanic White vote in every congressional district in Connecticut.
https://uselectionatlas.org/FORUM/index.php?topic=262902.0

[mieastwick]

How does reagente's model have Clinton winning the White vote in Connecticut? The demographics do not appear favorable to this. My model (admittedly, based on less plausible assumptions than reagente's) has Trump winning the non-Hispanic White vote in every congressional district in Connecticut.
https://uselectionatlas.org/FORUM/index.php?topic=262902.0

What share of the electorate did you have non-Hispanic White voters at in Connecticut?

I just used the Voting-Age Population share in 2010, so 74.2% (this is why I said my model was "based on less plausible assumptions" than yours).

So, let's look at it. In 2010, the Black share of the VAP was 9%. Subtract 8% from Clinton, 1% from Trump. Now you have 46.5% Clinton, 40% Trump. The Hispanic share of the VAP was 11% (rounded down). Subtract three points from Trump, eight points from Clinton. Now you have Trump at 37%, Clinton at 38.5%. Everyone else was 5.3% of the VAP. That gets to Clinton losing the non-Hispanic White vote in CT by a little over a point using 2010 Voting-Age Population demographics.

Perhaps you had Hispanics and Asians at a lower share of the electorate than their share of the VAP, which is reasonable.

In any case, 2015 had non-Hispanic Whites a lower share of the population than in 2010 in CT by three points, so Hispanics and Asians would have to be a much lower share of the electorate than their share of the VAP for a Hillary win of the non-Hispanic White vote to make sense.

[mieastwick]

Also, because I had some free time, I decided to calculate the results without margins by my model:

How'd you get your numbers for voting behavior in South Florida??? Those results look very interesting, surely.

[nclib]

It's ironic how Clinton won FL-25 whites and lost its non-whites whereas the opposite is true in the other Cuban districts FL-26 and FL-27.

Aside from Cubans, the only districts where Trump won non-whites are OK-2 and OK-3. I know Native Americans in rural Okla. are pretty assimilated and Republican--is there anything else here? Are there other CDs where the non-white vote is close?

[jimrtex]

So, I've finished version 2.0 that breaks things down by race.

One issue I've ran into is that since I don't always have county level information for Asian and Hispanic groups, I just apply a statewide weight (i.e. a boost for Trump if there are more Vietnamese in a state, a detraction if there are more Hmong, as an example). for the remaining counties. This sometimes leads to "choppy" borders (where you can tell the boundary of a state). Accordingly, there might need to be some revision at work, though it might also be justified for some states.

Here are all the maps I've made for this project. I apologize for the current colors (I post these maps on the other websites I'm on, where everyone else is accustomed to a Red=Republican scheme). I'll change them to Atlas Colors once I finalize this.

Any suggestions would be appreciated!

I would suggest that you screen counties for no data, based on some threshold (1% or 2%)  so that for example in rural areas of the Midwest and West you aren't trying to extract black or Asian voting behavior. There is not enough data in these areas to be statistically valid, or alternatively, enough persons of the group as to be relevant.

[100% pro-life no matter what]

Statewide using your numbers for the white vote:

Trump's best states with the white vote:
1. Mississippi: 89%
2. Louisiana: 84%
3. Alabama: 83%
4. South Carolina: 75%
5. Georgia: 75%
6. Texas: 74%
7. Oklahoma: 73%
8. Tennessee: 72%
9. Wyoming: 72%
10. Arkansas: 71%

Clinton's best states with the white vote:
(DC: 86%- Mississippi whites were actually more unified than DC whites)
T1. Massachusetts: 56%
T1. Vermont: 56%
3. Hawaii: 52%
4. Rhode Island: 50%
5. Washington: 48%
T6. Connecticut: 47%
T6. Oregon: 47%
8. Maine: 47%
T9. California: 47% (narrowly won)
T9. New York: 47% (narrowly lost)

Now, in some cases, these are different from the exit polls, but, usually, they aren't too far off.

Major cities where Trump won the white vote:

Raleigh, NC
Charlotte, NC
Columbia, SC
Charleston, SC
Jacksonville, FL
Tallahassee, FL
Tampa, FL
Orlando, FL
St. Petersburg, FL
Mobile, AL
Montgomery, AL
Birmingham, AL
Huntsville, AL
Nashville, TN
Memphis, TN
Louisville, KY
Lexington, KY
Cincinnati, OH
Detroit, MI
Indianapolis, IN
Kansas City, MO
Little Rock, AR
Baton Rouge, LA
Houston, TX
San Antonio, TX
Dallas, TX
Oklahoma City, OK
Wichita, KS
Omaha, NE
Lincoln, NE
Phoenix, AZ
San Diego, CA (this one might be surprising?)

[OneJ]

First of all, I just want to say you did a spectacular job on this project reagente!

The question I'm asking is what happened to the electorate of voters by state photo and summary? It's not viewable for me anymore.

[Pennsylvania Deplorable]

It's interesting that you show Trump losing white voters in NY by 0.8 while the CNN exit poll showed him winning it by 6. Exit polls have been unreliable before (like the 44% of Hispanics voted for Bush claim), but that's a big difference.

[TDAS04]

I didn't expect Clinton to lose the white vote in Connecticut.

[SaneDemocrat]

Portland, San Fran and DC have largest percentage of white liberals. Interesting.

[TDAS04]

I wouldn't be surprised if Hillary received a lower percentage of the white vote than any other major party nominee since McGovern.  Not sure about 1984, but even Mondale probably got more of the Southern white vote than Hillary.

Portland, San Fran and DC have largest percentage of white liberals. Interesting.

Seattle belongs on that list as a city.  (It just doesn't show because more people in King County live outside the city than in it.  Portland contains most Multnomah County residents, San Francisco is a county itself, and DC is like a county for these purposes).