Dividing the above veteran counts by population for each county, we can see that Fall River, Meade, and Sanborn counties have the highest proportion of veterans in their populations:
The veteran proportion is lower than average in six of our ten most populous counties. The exceptions are Pennington, which is home to Ellsworth Air Force Base; Meade, which is home to one of our major VA hospitals; Lawrence, which is right next door to Meade and Pennington; and Yankton, which I can’t explain easily—Yankton doesn’t even have a VA clinic.
With the exception of our Black Hills sites, counties with college campuses, have lower than normal veteran populations, as do our West River reservation counties. That pattern suggests that younger populations will have lower proportions of veterans.
South Dakota lies right on a continental transition point where the benefits of warmer weather—better crop production, fewer people dying from cold—outweigh the costs. Head south and down from the mountains, and climate change is more likely a net negative to farming, outdoor labor productivity, and energy costs.
The counties poised to make out best in a warmer world are Lawrence in the northern Black Hills (2.4% better GDP) and McPherson on the north central prairie (2.3% better GDP). Ziebach in West River could take the worst hit (3.4% worse GDP). Douglas (down 2.4%) and other southeast counties also drop more than others in South Dakota.
According to this climate change analysis, most of West River would see increases in average crop yields, while most of East River would see decreases. Energy expenditures would increase on both sides of the river. Mortality rates would decrease in nearly every county (the exceptions I find are Tripp, Charles Mix, Hutchinson, Douglas, and Hanson), but crime increases everywhere as the number of cold days decreases.
South Dakota is happily removed from any foreseeable coastal damage. The study does not assume mass migration from the coastal and southern areas where climate change would do the greatest economic harm, so it is possible that the study underestimates the possible economic benefits of folks flooded and steamed out of their homes coming to South Dakota to live and work… and we all know how good immigration is for South Dakota’s economy!
The 16% of American counties that voted for Clinton generated 64% of American economic activity in 2015. Trump’s third of the map includes far more tiny boxes; I can slide 49 counties over from Clinton’s side and completely cover all 2,650 of Trump’s counties.
I wrote on first viewing that this split between high-output and low-output counties appears to align with the split between new-economy counties and old-economy counties. Donald Trump doesn’t even understand that divide; his business is wheeling-dealing for land and buildings, not providing goods or services. He doesn’t even know what “the cyber” is, let alone how it has fundamentally reshaped economic activity. He doesn’t grasp the global economy; he wants to throttle it and have us retreat back into some fantasy shell of isolationism that will tank the entire U.S. economy and leave other nations free to trade and innovate and grow, leaving us to become the next Portugal. Trump isn’t promising anything to put his lower-GDP counties in a better situation; he’s just promising to drag the bigger-GDP counties down.
Sharing my concern, the Brookings Institution’s Mark Muro and Sifan Liu write that a Presidential focus on boosting the GDP of those low-output counties is fine, if the Administration can look forward and accept change:
Given the election map we revealed, the Trump administration will likely feel pressure to respond most to the desires and frustrations of the nation’s struggling hinterland, and discount the priorities and needs of the nation’s high-output economic base.
On one hand, more attention to the economic and health challenges of rural and small-city Rustbelt America could be welcome, especially if it focuses on the right things: realism about current economic trends, adjustment to change, improving rural education and skills training, and enhancing linkages to nearby metropolitan centers. However, Trump’s promises to “bring back” the coal economy and “bring back” millions of manufacturing jobs (that now don’t exist thanks to automation) don’t speak wisely to real-world trends in low-output America. They look backwards and speak instead to local frustrations [Mark Muro and Sifan Liu, “Another Clinton-Trump Divide: High-Output America vs Low-Output America,” Brookings Institution, 2016.11.29].
Muro and Liu worry that focusing on economic development in lagging rural areas could leave high-output urban areas on their own in sustaining and building the education, research, and infrastructure that makes them successful. I’ve advocated a similar focus in South Dakota economic development: let our big, rich towns take care of themselves while focusing government intervention in places where the free market isn’t meeting economic needs, like the reservations.
But in a way, we already do that. Last March, WalletHub measured state dependency on the federal government, based on federal funding as a percentage of state revenue, the ratio of federal funding to IRS collections in each state, and the share of federal jobs in each state.
Of the ten states most dependent on federal funding and jobs, only New Mexico went for Clinton, by a margin of eight percentage points. The other nine most Uncle Sam-dependent states all went for Trump by double digits (19 points in Missouri to 42 points in West Virginia). Of the ten states least dependent on federal funding and jobs, nine went for Clinton (though it was close in New Hampshire, Minnesota, and Nevada). Only one of those more self-sufficient states, Kansas, went for Trump, by 21 points.
Real Republicans and Libertarians could read these maps and conclude is not that we need to understand the emotions and fears of the Trump voters but that we need to get them off their couches and off the dole and get them contributing to the new economy instead of dreaming that the Trumpist government will give them the things they used to work for.
Related Reading: Loren Collingwood of the Washington Post tests county data and finds a distinct correlation between Trump votes and loss of manufacturing jobs from 2000 to 2014. However, Collingwood finds a slightly stronger correlation between Trump’s vote share and change in Hispanic population over the same period:
I also found evidence consistent with the “racial threat” hypothesis. As shown by the orange dotted line in the graph, Trump’s vote was higher in counties where the number of Latinos has increased significantly since 2000. This suggests that some voters may have supported Trump as a way of expressing white identity in an increasingly diverse nation [Loren Collingwood, “The County-by-County Data on Trump Voters Shows Why He Won,” Washington Post, 2016.11.19].
Collingwood finds even stronger negative predictors of Trump turnout in percentage of blacks, percentage of Hispanics, and, the strongest, percentage of residents with college degrees.
Trump may have played to economic anxiety, but he played more to uneducated white fright.
Would you look at that? If you could believe everything you read on the Internet, you’d have to conclude from Rep. Mickelson’s address that he lives good Democratic District 15 and couldn’t be eligible to run for office in District 13. Attentive Reader might have to go grab a petition to run to fill that vacancy.
Statewide, the average number of crashes per 100 people is 2.033. In the map above, green shows counties below that average—i.e., with fewer crashes than their population would predict if the statewide average held everywhere. Red shows counties above that average. The darker the color, the farther from the average. Click on each county to show its crash numbers and population for 2014.
If the ratio of crashes to population indicates relative safety on the highways, then some of the safest counties in South Dakota are in Indian Country:
crashes per 100 population
Meanwhile, the ten seemingly most dangerous counties are the rural counties along the I-90, SD 34, and US 212 corridors:
crashes per 100 population
Those numbers don’t mean that folks in Faulk and Jones counties are uniquely bad drivers; it’s more likely that those counties simply see a lot more through traffic compared to their relatively small populations, and that traffic brings those counties more than their otherwise fair share of accidents. But those numbers also suggest that first responders in those areas may have to work harder and see more roadside nastiness than there counterparts in other, less-traveled or more policed counties.
Five of our counties with populations over 10,000, including Minnehaha and Pennington, are above the average crash-to-population ratio; the other thirteen counties with five-figure populations, including burgeoning Lincoln, are below the statewide crash-pop ratio.