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TIGC Covid-19 March 28 Round-Up

Random Questions and Observations on Covid-19:

  • Why are the results from Georgia and North Carolina so different?  The two states have nearly identical populations — 10.6 million for Georgia versus 10.5 million for North Carolina, according to 2019 Census Bureau estimates — but have very different Covid-19 results so far.  I’ve been watching this for a while, thinking the numbers might even out.  They haven’t.  As of today’s morning report from both states’ public health agencies, North Carolina has tested more than 50 percent more people than Georgia but found less than half as many “positives” as Georgia, and with only a fraction of the hospitalizations and deaths, as this table shows. Ga NC ComparisonThe question is why.  There are obviously a lot of variables at play, but the two states probably have a lot more in common than not.  Based on news reports I’ve read, North Carolina Governor Roy Cooper, a Democrat, seems to have acted earlier than his Georgia counterpart, Republican Brian Kemp — but not that much earlier.  We’re probably going to have to wait for the smoke to clear to develop a truly useful analysis of this, but these differences are worth keeping an eye on.  It’d be interesting to see the AJC and the Charlotte Observer collaborate on a tick-tock plotting state and maybe major local actions across a timeline.
  • Like Sherman, Covid-19 continues its march through Georgia.  As of today’s late-morning report from the Georgia Department of Public Health, Covid-19 has now been found in 108 of Georgia’s 159 counties.  March 28 Counties with Confirmed CasesAs this map shows, the areas that have so far avoided reporting positive tests are almost entirely rural, including a swath of sparsely populated counties through east-central and southeast Georgia and another group of counties in west Georgia that somehow haven’t yet been pulled into the orbit of the Albany hot spot.  While the bulk of the positive tests and deaths are in Metro Atlanta, the rate of infection and Covid-19 deaths is much higher in South Georgia, as the table below shows.  Indeed, if you focus on Dougherty County and its six contiguous counties, the Covid-19 infection rate and death rate are, respectively, 6.7 times and 20 times that of TIGC’s Metro Atlanta region.MARCH 28 Regional Summary
  • Using smartphones to gauge mobility in the face of Covid-19.  One of the more interesting chunks of data to surface in the Covid-19 pandemic has come from a company called Unacast.  Unacast uses location tracking data from smartphones to track how far users travel each day and aggregates that data to see what that can tell us about whether people are following the guidance of their state and local leaders and limiting their local travel.  The data seems to lag by a few days and it’s still early, but it’s still interesting.  As of today’s report, four of the five best-behaving counties in Georgia are in Metro Atlanta — Forsyth, Dawson, Cherokee and Fulton counties.  The other top-five county was Clay County, which ranked second and had slashed its “average distance traveled” by 39 percent.  Clay County sits hard on the Alabama line in southwest Georgia, on the edge of the Albany blast zone, but has yet to report a single positive Covid-19 test.  You have to wonder if everybody in that county hasn’t gone inside and shut all their doors and windows.  You can find the Unacast data here.  Scroll down until you find the U.S. map, then click on Georgia and, when that map comes up, click on your county and wait for the data to come up.  It’s a little slow and clunky, but still useful.
  • Covid-19’s economic toll may be tougher on rural Georgia than Metro Atlanta.  The great recession hit Metro Atlantans harder than their rural Georgians.  That reality showed up first in IRS data and in total personal income data from the Bureau of Economic Analysis (BEA) and later in a BEA report on county-level gross domestic product.  As Covid-19 began to spread, I found myself thinking it might be tougher on rural Georgia.  If the Great Recession hit Metro Atlanta harder, it recovered faster and has since widened its economic, education and health gap with rural Georgia; rural Georgia has generally lost population and seen its local economies contract, which, I figured, left it in a weakened position to deal with a global pandemic.  Now comes Politico with a really good piece fleshing out my general concerns.  It’s worth reading.
  • While the Covid-19 data is still developing day-by-day, there are already some interesting oddities and riddles that are worth noting and wondering about:
    • College towns.  One early theory was that college towns would be hard hit, given their population of young people who still think they’re invincible.  Well, yes and no.  Clarke County, home of the University of Georgia, had 35 cases as of mid-day Saturday, just over half of the 67 reported by Carroll County, home of West Georgia College.  Those weren’t all that surprising.  The bigger riddles were Baldwin County (home of Georgia College and State University) with only two cases and Bulloch County (Georgia Southern) with a big goose-egg so far.  I haven’t found data on the number of tests given in each county, but both Baldwin and Bulloch are large enough that you’d have to think they’d conducted a fair number of tests.
    • Bartow County.  Other than Albany, Bartow County, just of I-75 north of Metro Atlanta, has emerged as the state’s second-hottest hot spot.  As of Saturday, Bartow County, with a population of about 106,000, had reported 116 cases, the sixth-most in the state, and its infection rate was second only to Dougherty County.  I am, I believe, reliably informed that the outbreak traced back to a large community gathering in Cartersville, but I haven’t found any published reporting on it and am going to hold off on the details for the time being.
    • Taliaferro County.  This tiny, impoverished county of about 1,700 people might be considered a prime target for Covid-19.  It’s located about a hundred miles east of Atlanta on I-20 and a far piece from any major healthcare facilities.  As it’s worked out so far, it’s one of only two counties on that route between Atlanta and Augusta that still hasn’t reported a positive Covid-19 test (neighboring Warren County is the other).  One likely reason is a paucity of testing in the county, but another could be an early decision by the local school superintendent, Allen Fort.  As Jim Galloway reported nearly two weeks ago, Fort didn’t wait for guidance from Governor Kemp or anybody else; he acted on his own and sent all his students home.  In doing so, he may have flattened the curve in his little county.

Stay tuned.  I’ll follow up with more early next week.

New Kaiser Health News data adds political dimension to Covid-19 crisis

[Note: The third data column in the table about a half-dozen paragraphs down was mislabeled when this was initially posted.  It has now been corrected thanks to a good catch by alert reader Denese Whitney.  Trouble in God’s Country appreciates the help.]

Last Thursday I posted a piece suggesting that Covid-19 might constitute a perfect storm for rural Georgia — that old age and poor health status could combine with a frail healthcare delivery system to put rural areas in particular jeopardy.  Since then a couple of reports have come out that support that view and bring certain healthcare and political realities into sharp focus.

First was a report Saturday from Kaiser Health News (KHN) that documented the number of ICU beds available in virtually every U.S. county and compared those numbers with the population of people 60 and older in each of those counties.  That’s not a perfect measure of an older person’s access to critical care, of course; just because there’s not an ICU bed in your home county doesn’t mean there’s not one in the next county or a nearby city.  But it’s not a bad measure of the magnitude of the healthcare challenge taking shape.

The second was a report in today’s Washington Post.  Philip Bump, one of the nation’s top data journalists, took the KHN data and laid it over county-level data from the 2016 presidential election between Donald Trump and Hillary Clinton.

“Comparing the county-level data from Kaiser Health News to 2016 presidential election data,” Bump wrote,  “we discovered a remarkable bit of data: About 8.3 million people who voted for Trump in 2016 live in counties where there are no ICU beds or no hospitals. That amounts to about 13 percent of the total votes Trump earned in that election, or one out of every eight votes.

“Those counties are also home to about 3.8 million people who voted for Hillary Clinton, a figure which makes up only about 5 percent of her total. Most of the counties voted for Trump by wide margins; he won them by an average of 41 points. He won 10 times as many counties with no ICU beds as did Clinton.”

This afternoon I’ve pulled Kaiser’s Georgia data and combined it with data from Georgia’s 2018 gubernatorial election and today’s Georgia Department of Public Health report on the number of people in the state who have tested positive for Covid-19.  (As of mid-day today, that number was up to 600 people from 59 counties; 38 of the “positives” were from “unknown” counties.)

Overall, the situation here in Georgia is a microcosm of the national picture Bump found — and if the primary goal in this situation is to try to meet the healthcare needs of the entire state, state politics, as always, hovers not very far in the background and imposes a set of difficult strictures on the process.

In the 2018 gubernatorial election, Brian Kemp, the Republican nominee who ultimately won and is now governor, largely swept rural Georgia, carrying 130 counties.  Of those, 83 don’t have a single ICU bed (indeed, most don’t even have hospitals).  Combined, those counties have a population of 1.7 million, more than 380,000 of whom (22 percent) are over 60.  So far, only 47 of the state’s 600 confirmed Covid-19 cases hail from those counties, but it seems likely those numbers will rise as testing becomes more available in rural areas.

In contrast, the Democratic nominee, Stacey Abrams, dominated the state’s urban areas, which has a much younger population and much more robust healthcare delivery systems.  Twelve of the 29 counties she carried were indeed rural (including a half-dozen southwest Georgia counties that are now in the orbit of the Covid-19 hotspot erupting in and around Albany) and also boast no ICU beds of their own.  But the overwhelming majority of her support came from urban and suburban areas that are home to large healthcare systems with a good number of ICU beds.

This table illustrates the contrast.

Kemp Abrams ICU Bed Table

The 130 counties Kemp carried are home to right at 52 percent of the state’s 60-plus population but have fewer than a third of the state’s ICU beds.

To use Philip Bump’s Washington Post framework, more than a half-million Georgians who voted for Kemp — about 25 percent of his total — reside in counties without a single ICU bed.  That’s true of only about 10 percent of Abrams’s voters.

Again, rural Georgians who fall victim to Covid-19 may well be able to get access to an ICU bed in Metro Atlanta or another major city if they need it, but the current pandemic does seem to put a sharp new focus on a problem that has bedeviled the state’s Republicans since they took power at the turn of the century — how to provide healthcare to rural areas that constitute their political base.

For a decade now, the state’s GOP leaders have steadfastly refused to take advantage of billions of dollars in Medicaid Expansion funds and presided over a steady stream of rural hospital failures.

I’ll try to do more with this over the next couple of days.  For now, here’s a complete list of Georgia counties with all the data discussed in this post.

County # !CU Beds # Covid-19 Cases Confirmed by DPH 3/22/20 Brian Kemp’s % of 2018 General Election Vote Stacey Abrams’s % of the 2018 General Election Vote Population Aged 60+ Percent Population Aged 60+
Appling 3 0 79.7% 19.9% 4208 22.8%
Atkinson 0 0 74.4% 25.2% 1451 17.5%
Bacon 4 0 86.7% 12.7% 2329 20.6%
Baker 0 0 58.2% 41.4% 919 28.3%
Baldwin 12 2 49.5% 49.8% 9287 20.4%
Banks 0 0 89.8% 9.4% 4207 22.9%
Barrow 6 1 73.6% 25.2% 12454 16.6%
Bartow 21 57 76.1% 22.8% 18389 17.9%
Ben Hill 5 0 63.9% 35.6% 3982 23.1%
Berrien 0 0 85.0% 14.4% 4266 22.4%
Bibb 117 1 38.4% 61.0% 31993 20.8%
Bleckley 0 0 78.6% 20.5% 2860 22.4%
Brantley 0 0 91.3% 8.1% 3792 20.6%
Brooks 0 0 61.5% 38.1% 4099 26.2%
Bryan 0 0 70.1% 28.8% 4985 14.3%
Bulloch 24 0 62.8% 36.3% 11463 15.5%
Burke 0 0 50.6% 48.9% 4540 20.0%
Butts 0 1 71.7% 27.7% 4813 20.4%
Calhoun 0 0 42.7% 57.1% 1362 20.9%
Camden 5 0 65.3% 33.6% 8503 16.3%
Candler 6 0 72.4% 27.2% 2476 22.7%
Carroll 18 14 69.8% 29.1% 20423 17.8%
Catoosa 0 0 79.5% 19.3% 14752 22.4%
Charlton 0 1 75.1% 24.4% 2473 19.1%
Chatham 78 4 40.0% 58.9% 56014 19.6%
Chattahoochee 0 0 54.5% 44.7% 645 5.8%
Chattooga 0 1 79.9% 19.4% 5705 22.9%
Cherokee 15 18 72.1% 26.3% 42210 17.9%
Clarke 104 9 28.6% 70.3% 17345 14.0%
Clay 0 0 45.3% 54.0% 937 31.0%
Clayton 34 13 11.8% 87.8% 36959 13.5%
Clinch 0 0 76.1% 23.6% 1466 21.6%
Cobb 119 61 44.6% 54.0% 120689 16.3%
Coffee 10 0 70.7% 28.8% 7860 18.3%
Colquitt 10 0 75.9% 23.5% 8918 19.4%
Columbia 0 3 66.5% 32.5% 26092 18.2%
Cook 0 0 70.9% 28.7% 3527 20.5%
Coweta 19 8 69.7% 29.1% 25269 18.3%
Crawford 0 0 72.9% 26.4% 2798 22.6%
Crisp 16 0 63.0% 36.6% 5331 23.2%
Dade 0 0 82.5% 16.2% 4079 25.1%
Dawson 0 1 85.9% 13.1% 6208 26.5%
Decatur 10 0 60.2% 39.4% 5831 21.6%
DeKalb 168 45 15.7% 83.4% 121505 16.5%
Dodge 6 0 73.9% 25.7% 4726 22.4%
Dooly 0 0 52.7% 47.0% 3522 25.1%
Dougherty 50 48 29.8% 69.8% 18997 20.8%
Douglas 8 4 39.4% 59.8% 22028 15.7%
Early 0 2 55.2% 44.5% 2739 26.3%
Echols 0 0 88.2% 11.0% 681 17.0%
Effingham 0 2 76.9% 22.0% 9061 15.9%
Elbert 4 0 70.0% 29.5% 5092 26.4%
Emanuel 8 0 70.0% 29.5% 4782 21.3%
Evans 0 0 69.4% 30.2% 2260 21.0%
Fannin 5 0 83.0% 16.1% 8800 35.9%
Fayette 37 9 56.0% 42.7% 25277 22.9%
Floyd 65 9 71.1% 27.8% 20941 21.7%
Forsyth 24 4 70.6% 28.0% 33215 15.7%
Franklin 8 0 86.5% 12.7% 5691 25.5%
Fulton 538 108 26.8% 72.2% 159840 15.8%
Gilmer 0 0 83.7% 15.3% 9166 31.0%
Glascock 0 0 91.4% 8.2% 713 23.6%
Glynn 24 3 63.6% 35.6% 20679 24.8%
Gordon 8 4 81.9% 17.1% 10941 19.4%
Grady 4 0 67.3% 32.3% 5593 22.3%
Greene 0 0 65.1% 34.4% 6034 36.1%
Gwinnett 82 27 42.3% 56.5% 122430 13.8%
Habersham 4 0 83.5% 15.6% 10665 24.3%
Hall 85 9 73.4% 25.5% 36793 19.1%
Hancock 0 0 24.7% 75.0% 2435 28.1%
Haralson 0 0 87.7% 11.5% 6357 22.1%
Harris 0 0 74.0% 25.2% 7948 23.9%
Hart 0 0 76.6% 22.6% 7015 27.5%
Heard 0 1 83.2% 16.1% 2692 23.2%
Henry 40 7 42.0% 57.3% 34127 15.7%
Houston 36 1 57.9% 41.1% 25522 17.0%
Irwin 0 0 75.8% 23.9% 2170 23.4%
Jackson 0 0 81.6% 17.4% 12724 19.9%
Jasper 0 0 74.5% 24.9% 3116 22.7%
Jeff Davis 4 0 82.6% 16.9% 3219 21.5%
Jefferson 0 0 47.0% 52.6% 3828 24.0%
Jenkins 0 0 64.7% 35.0% 2049 22.9%
Johnson 0 0 72.5% 27.2% 2334 23.8%
Jones 0 0 67.8% 31.6% 6333 22.2%
Lamar 0 3 69.4% 29.9% 3955 21.6%
Lanier 0 0 71.3% 28.4% 1972 19.0%
Laurens 16 2 65.9% 33.6% 10610 22.4%
Lee 0 16 74.7% 24.8% 4964 17.0%
Liberty 0 0 36.2% 63.1% 7447 12.0%
Lincoln 0 1 69.4% 29.9% 2318 29.8%
Long 0 0 64.7% 34.4% 2398 13.4%
Lowndes 48 8 57.7% 41.6% 17921 15.7%
Lumpkin 0 1 79.2% 19.3% 7478 23.7%
Macon 0 0 36.9% 62.9% 2925 21.4%
Madison 0 0 78.5% 20.7% 6721 23.5%
Marion 0 0 63.9% 35.3% 2190 25.6%
McDuffie 0 0 60.5% 39.0% 4855 22.6%
McIntosh 0 0 59.5% 40.0% 4475 31.8%
Meriwether 0 0 58.9% 40.4% 5261 24.9%
Miller 0 1 77.9% 21.6% 1561 26.5%
Mitchell 0 0 56.2% 43.5% 4919 21.8%
Monroe 0 1 71.9% 27.2% 6594 24.6%
Montgomery 0 0 76.1% 23.3% 1891 21.1%
Morgan 0 0 71.2% 28.0% 4678 26.0%
Murray 0 0 85.8% 13.4% 7520 19.1%
Muscogee 61 2 38.6% 60.7% 34483 17.4%
Newton 10 4 45.1% 54.3% 18455 17.6%
Oconee 0 1 69.8% 29.0% 7602 21.1%
Oglethorpe 0 0 70.5% 28.4% 3441 23.5%
Paulding 8 4 66.6% 32.5% 21365 14.0%
Peach 0 3 52.2% 47.3% 5201 19.2%
Pickens 6 2 84.8% 14.2% 8464 27.9%
Pierce 0 0 88.9% 10.7% 4228 22.1%
Pike 0 0 85.7% 13.6% 3658 20.4%
Polk 0 4 79.1% 20.1% 8705 21.0%
Pulaski 6 0 69.8% 29.8% 2806 24.6%
Putnam 0 0 71.9% 27.5% 6275 29.3%
Quitman 0 0 55.5% 43.6% 913 42.7%
Rabun 0 0 80.0% 18.8% 5564 34.0%
Randolph 0 1 45.1% 54.4% 2258 31.3%
Richmond 264 10 31.5% 67.7% 38152 18.9%
Rockdale 16 2 32.0% 67.4% 17124 19.4%
Schley 0 0 81.0% 18.3% 1100 21.3%
Screven 0 0 60.4% 39.4% 3450 24.6%
Seminole 0 0 66.7% 32.8% 2541 29.7%
Spalding 22 2 61.2% 37.9% 14777 23.0%
Stephens 6 0 80.7% 18.6% 6327 24.7%
Stewart 0 0 41.8% 58.0% 1209 20.7%
Sumter 10 2 48.8% 50.7% 6539 21.3%
Talbot 0 0 39.5% 59.8% 2045 31.8%
Taliaferro 0 0 38.1% 61.6% 511 27.7%
Tattnall 0 0 76.3% 23.1% 4769 18.8%
Taylor 0 0 62.9% 36.5% 2020 24.4%
Telfair 0 0 66.8% 32.8% 3802 23.3%
Terrell 0 2 45.7% 53.9% 2148 23.9%
Thomas 35 0 61.2% 38.3% 10319 23.0%
Tift 20 2 69.7% 29.7% 8091 20.0%
Toombs 8 0 74.8% 24.8% 5815 21.4%
Towns 0 0 81.7% 17.4% 4566 40.9%
Treutlen 0 0 68.9% 30.8% 1666 24.7%
Troup 20 4 60.9% 38.4% 13380 19.3%
Turner 0 1 63.0% 36.6% 2004 24.9%
Twiggs 0 1 52.7% 46.8% 2395 28.8%
Union 5 0 83.4% 15.6% 8856 39.8%
Upson 28 0 66.8% 32.6% 6294 24.0%
Walker 0 0 81.0% 17.9% 16583 24.2%
Walton 7 0 76.9% 22.4% 17821 20.1%
Ware 22 0 71.7% 27.8% 7974 22.3%
Warren 0 0 46.6% 53.1% 1586 29.3%
Washington 0 0 50.6% 49.1% 4483 21.9%
Wayne 12 0 80.2% 19.1% 6149 20.6%
Webster 0 0 59.9% 40.0% 671 25.5%
Wheeler 0 0 71.1% 28.7% 1550 19.5%
White 0 0 84.5% 14.4% 7553 26.5%
Whitfield 34 2 72.3% 26.8% 18625 17.9%
Wilcox 0 0 73.3% 26.5% 1960 22.0%
Wilkes 0 0 59.0% 40.4% 2863 28.9%
Wilkinson 0 0 55.6% 44.0% 2285 25.0%
Worth 0 2 75.4% 24.1% 5131 24.7%
Totals 2508 562     1,863,154 18.3%

 

 

 

 

March 20 Covid-19 Update: 420 positive cases in 50 counties

In its Friday update, the Georgia Department of Public Health (DPH) reported that the total number of confirmed Covid-19 cases in the state had jumped to 420 from 297 on Thursday and that the number of counties involved had risen to 50 from 34.

As the map below shows, the virus has continued to spread outward from Metro Atlanta and, just as significantly, in southwest Georgia, where Albany and Dougherty County have emerged as a hot spot.  March 20 Covid-19 Update

On Thursday, the only southwest Georgia counties with confirmed cases, in addition to Dougherty, were Lee, directly to the north, and Early, to the west on the Alabama line.  On Friday, DPH added Miller, Randolph, Terrell, Turner and Worth to that list.  Those eight counties are now home to 54 known Covid-19 victims.

Perhaps oddly, the only area of the state still largely unscathed is a massive stretch of sparsely populated and for the most part poverty-stricken rural counties that run from east central Georgia — in the area roughly between Metro Atlanta and Augusta — down through southeast Georgia.

Laurens County, whose county seat of Dublin is the largest city in that general region, reported its first case on Thursday.  Outside of Dublin and Vidalia, in Toombs County, these counties do not have major healthcare facilities.

Throughout the state, local officials continued to struggle to come to grips with the spread of the virus.  In Albany, Dougherty County Commission Chairman Chris Cohilas asked the county’s residents to shelter-in-place and indicated the county government was moving to enacting an order requiring it.  Across the state, Southeast Georgia Health System suspended most visitation rights at its Brunswick and Camden hospitals.

Covid-19 may stir a perfect storm for rural Georgia

When I began work on this project some years back and came up with the title “Trouble in God’s Country,” I was thinking about things like the urban-rural divide in economics, education, healthcare, and politics.  It never crossed my mind that a new plague might come along that would stir up what might be a perfect storm for rural Georgia.

To be sure, Metro Atlanta and the state’s other urban areas will obviously take huge beatings in this as well, but rural communities may prove even more vulnerable, and not even in the long run.  Without even bothering to run through my usual tons of data, we can take judicial note of certain indisputable realities.

The most basic is that Georgia’s rural communities are older and less healthy.  That alone puts a Covid-19 target on their backs.  Add to that a healthcare delivery system that might charitably be described as frail and you’ve already got the makings of a heaping helping of trouble in God’s country.

But there’s more.  You can stir politics, religion, and crime into the mix.  As I’ve documented before, rural Georgia is overwhelmingly Republican and pro-Trump, and there’s already national polling by Pew Research suggesting that Republicans are taking Covid-19 less seriously than Democrats (or at least were until President Trump and FOX News began shifting tone earlier this week).

From the Pew report on its polling: “ … a vast majority of Republicans (76%) say the news media have exaggerated the risks associated with the virus – 53% greatly and 24% slightly – while far fewer (17%) say the media have gotten it about right. Democrats, on the other hand, are much more likely than Republicans to think the news media have gotten the level of risk about right (41%).”

As for religion and crime, it’s not for nothing that the region of Georgia from the gnat line south is known as both the Bible Belt and the Prison Belt.  Both churches and prisons hold the potential to serve as lethal vectors for the bug.  I started canvassing rural Georgia contacts earlier this week and one common theme in the feedback revolved around a reluctance to cancel church services; as things have worked out, it sounds like the cancellations are indeed taking place, maybe a week later than they might have.  In several communities, churches are reportedly planning to live-stream their services via Facebook and other technologies.

Prisons are a different story.  According to the Georgia Department of Corrections’ website, its 34 state prisons house 52,000 felony offenders, and it’s difficult to imagine a more active breeding ground for Covid-19.  The vast majority of those prisons are in rural Georgia, most of them in Middle and South Georgia (as the red dots on GDC’s facilities map, here, illustrate). DOC Facilities Map

Those prison populations are constantly ebbing and flowing, as newly sentenced felons begin serving their time and those who have completed their sentences are freed.  Meanwhile, thousands of rural Georgians who work at those prisons come and go to fill the three daily shifts at each of them.

As it turns out, one of those employees has already tested positive for Covid-19, as both GDC and the AJC reported yesterday, although GDC hasn’t said where the employee worked.  So far, the department hasn’t reported any inmate infections, but it’s obviously a significant concern: GDC’s home page currently features a “COVID-19 UPDATES” banner and a “Covid19 Response” statement detailing the department’s response to the bug.  Among other things, all visitation has been suspended and prisoner movement is being limited to “required medical transfers.”

As for positive tests around the state, those now appear to be seeping rapidly into rural Georgia.  Any early expectations that the virus might do most of its damage in urban areas and not find its way to the hinterlands is being undercut by daily reports from the Georgia Department of Public Health.   The number of positive tests went from 197 in 28 counties on Tuesday to 287 in 34 counties on Wednesday (with the home counties of a half-dozen victims “unknown”).

If, as these maps suggest, it turned up first in Metro Atlanta and North Georgia, it is now making its way below the gnat line.

And there’s some evidence that the reality on the ground in some localities is outpacing the information being reported on a daily basis by DPH.  Albany and Dougherty County have emerged as a South Georgia hot spot, and DPH today put the number of confirmed cases there at 20.  But at about the same time DPH was posting its Wednesday numbers, Phoebe Putney Memorial Hospital in Albany was holding a press conference where, according to the Albany Herald, it reported two more deaths (for a total of four), a total of 40 patients who had tested positive (and were either in the hospital or at home), and nearly 500 more area residents who were still awaiting test results.  Six Phoebe Putney employees have tested positive, the Herald reported.

And Thursday evening, the Tallahassee (Fla.) Memorial Hospital reported that an Early County, Ga., woman had died there of the virus.

_________

If rural Georgians were a little slower than their city cousins to react to the Coronavirus threat, they may now be taking it more seriously.  I canvassed a half-dozen contacts early in the week and then again yesterday and today.  All reported that the pace seemed to be picking up.  One South Georgia contact who reported earlier this week that people were mocking the “panic” and not changing their behavior indicated earlier today that was changing; more and more businesses cutting hours and more people were self-isolating.

Another contact, Jason Dunn, executive director of the Fitzgerald and Ben Hill County Development Authority, emailed me around mid-afternoon that it was “safe to say that we are seeing changes in day-to-day behavior.  Foot traffic in our office is down and the citizens that continuously support our locally owned eateries are more than likely to get their meal to-go rather than dining in.  The best that I can put it is that social distancing is tough in a tight-knit community, yet a majority of our citizens are taking precautions.”

And this from a northeast Georgia weekly newspaper editor: “Day-to-day has changed drastically. A lot of parents are getting a crash course in home-schooling and remote, electronic and digital learning. It also shows in dramatic fashion how a lack of adequate broadband can be a real disadvantage to a rural county without that kind of infrastructure!

“The question about the seriousness of the situation being slow to take hold was a good one,” he added.  “On a Tuesday, the track coach was planning on winning the state championship. On Friday he was bemoaning the fact that his team would probably not even get a shot at that achievement.”

Kemp, House Republicans headed for showdown over rural spending?

I’ve long thought Georgia was headed toward a rural reckoning that would boil down to money (as everything ultimately does), but I figured it might keep until the next reapportionment.  That’s when legislative power will almost certainly consolidate solidly and irrevocably in Metro Atlanta.  I may have been wrong.

Now comes James Salzer with the lead story in today’s Atlanta Journal-Constitution and a detailed rundown on Governor Brian Kemp’s proposed cuts to important rural programs, including some developed by the House Rural Development Council.

This is a little odd, of course, because Kemp, Georgia’s third Republican governor, rode into office on a tsunami of rural votes, and the operating presumption has been that Job One for Kemp & Co. would be to take care of rural Georgia.

The fact that we’ve already got this public a split on rural spending between the second and third floors of the Capitol is at least mildly surprising.  The House Appropriations Committee, which was the source of most of the grousing quoted in Salzer’s story, is made up largely of rural and small-town legislators from outside the Metro Atlanta area.

By my rough count, only about 30 of the committee’s 80 members come from Metro Atlanta, and most of those are from the suburbs.  Only a handful come from inside the perimeter.  In contrast, the committee’s leaders hail from places like Auburn, Ashburn, Musella, Nashville, Thomasville, and Moultrie.

To some degree, this may be little more than the annual kabuki theater the General Assembly performs — some might say stages — around the annual budget.  But it feels like more than that.

Watch this space.

Introducing TIGC’s economic and population growth indexes (yawn)

As a kid, about the only sport I was halfway decent at was baseball (fair glove, no stick).  But one summer I had a health problem that kept me from playing and the coach (also my dad) stuck me in the press box (another formative moment) and made me keep the box score.  As part of that experience I wound up cobbling together crude statistical and performance reports on the team players (Jolly went two for three and fielded three grounders at second for outs at first; Studdard struck out five batters and walked one, drove in two runs … ).

During that same period, I was a rabid St. Louis Cardinals fan and went over all their performance stats in both the daily paper and the old Sporting News; I could tell you that the Cardinals would win 9 of 10 games if Bob Gibson struck out XX hitters and Musial and Boyer got YY hits between them.  Now, I tell you all that to make it abundantly clear (if it hasn’t been already) that you’re dealing here with a frustrated sports statistician.  I’m still a little annoyed that Bill James beat me to Sabermetrics.

I also tell you all this to serve notice that this post is going to be even more data-intensive, tedious and boring than most of my stuff.  Only serious geeks should sally forth.

Our subject matter today is county rankings.  I did an initial post back in October summarizing available economic, health and education rankings, but said at the time I wasn’t satisfied with that first pass and would loop back for a second take after wallowing around in the data for a bit.  Among the main things I’m looking for are how to gauge growth, directionality (are things getting better or worse?), and the magnitude of the quantitative and qualitative differences among Georgia’s 159 counties (don’t say I didn’t warn you).

You can think of this as TIGC’s crude answer for pro football’s quarterback rating system (QBR) or ESPN’s football power index (FPI).  It’s an effort to synthesize a lot of different data points into a single number, or maybe a couple of numbers.  It’s also a work in progress.

(Indeed, let me pause here to invite any actual statisticians or data geeks who have stumbled onto this page to make suggestions as to how I can improve the approach you’ll find below.)

Why, you might ask, would I or anybody else care about that kind of data?  Good question.  Part of the answer is that I may have too much time on my hands.  But the more serious answer is that I think it could be helpful in deciding how to attack different types of development problems throughout the state.

I have long thought the state should be triaging its rural counties and sorting them into different groups depending on what kind of shape they’re in.  Some may be too far gone for traditional community and economic development initiatives to work.  Others may still have a pulse and might be revived with the right kind of support and assistance.

To aid in that effort, I am hereby introducing the Trouble in God’s Country Population Growth Index (TIGC PGI) and the Trouble in God’s Country Economic Growth Index (TIGC EGI).  To concoct these indexes, I spent more time than I ought to admit thinking about which time frames to use and finally settled on a combination of 10-year, five-year and one-year growth rates.  I figured the longer-term growth rate was the most important and assigned the 10-year rate 60 percent of the total value of my indexes.  The five-year growth rates were weighted at 30 percent and the one-year rates got the final 10 percent.

I warned you.  If your eyes are glazing over, feel free to skip the next part and go straight to the growth indexes and rankings below.

If you’re still with me, the basic formula looks like this:

TIGC Index = ((10yr Growth Rate x 6)+(5yr Growth Rate x 3)+(1yr Growth Rate x 1))

The bigger the number, the better.

Still missing from my equation is a recognition of the size differences – of both local populations and economies.   I’m still noodling with different approaches to that and (as indicated above) would welcome suggestions from data geeks who are more statistically literate than I am.

To complicate matters further, I’ve combined two county-level economic metrics – gross domestic product (GDP) and total personal income (TPI).  This is part of what I spent way too much time fiddling with, but in the end I decided both were important.  As an example, Forsyth County, which is at or near the top in every economic, education and health category I’ve studied, ranked 19th for GDP growth but 1st for TPI growth.  As another example, tiny Randolph County, which is near the bottom in every category, ranked an unbelievable 1st in GDP growth but 154th in TPI growth.

So, to figure the overall TIGC Economic Growth Index I calculated the GDP and TPI indexes separately and then simply added them together, assigning each equal weight.  The TIGC Population Growth Index was calculated using the same split described above.

I’ll follow up shortly with a post with some thoughts on what all this means, or at least suggests.

For now, though, here’s a data dump with the economic and population indexes for each county.  The sort is by the Economic Growth Rank.

County TIGC GDP Growth Index TIGC TPI Growth Index TIGC Economic Growth Index TIGC Economic Growth Rank TIGC Population Growth Index TIGC Population Growth Rank
Forsyth 3.15 6.91 10.06 1 3.23 2
Oconee 3.80 6.25 10.05 2 1.92 5
Jackson 4.25 5.56 9.81 3 1.60 9
Bryan 4.03 4.91 8.94 4 2.33 3
Randolph 7.66 1.21 8.87 5 -0.81 151
Cherokee 3.29 5.26 8.54 6 1.75 7
Burke 5.71 2.54 8.25 7 -0.23 115
Dawson 3.32 4.87 8.18 8 1.20 15
Greene 2.04 5.99 8.02 9 0.99 24
Twiggs 5.26 2.74 8.00 10 -0.92 157
Fulton 2.83 5.07 7.90 11 1.31 14
Effingham 2.81 4.49 7.30 12 1.82 6
Barrow 2.56 4.60 7.16 13 1.63 8
Hall 2.34 4.77 7.11 14 1.11 18
Columbia 2.25 4.75 7.01 15 2.29 4
Long 1.64 5.38 7.01 15 3.42 1
Paulding 2.59 4.30 6.89 17 1.56 10
Walton 2.71 4.11 6.82 18 1.08 21
Hart 3.44 3.36 6.80 19 0.32 65
Clinch 2.35 4.40 6.76 20 -0.31 124
Pickens 2.55 3.99 6.54 21 0.77 34
Morgan 2.19 4.30 6.49 22 0.52 50
Fannin 2.11 4.38 6.48 23 0.95 26
Banks 2.91 3.48 6.39 24 0.49 53
Franklin 3.40 2.98 6.37 25 0.35 62
Coweta 1.92 4.33 6.26 26 1.47 11
Cobb 2.46 3.79 6.25 27 0.86 31
Gwinnett 2.45 3.66 6.11 28 1.40 13
Henry 2.46 3.62 6.08 29 1.40 12
Taliaferro 3.36 2.72 6.08 29 -0.85 156
Fayette 2.09 3.83 5.92 31 0.63 42
White 2.13 3.67 5.81 32 1.00 22
Lumpkin 1.52 4.27 5.79 33 0.99 23
Warren 4.00 1.63 5.63 34 -0.85 155
DeKalb 1.56 4.07 5.63 35 0.79 33
Peach 2.82 2.75 5.57 36 0.06 91
Lanier 2.26 3.30 5.56 37 0.46 55
Talbot 3.34 2.07 5.41 38 -0.67 144
Madison 2.20 3.20 5.40 39 0.56 47
Pike 1.83 3.52 5.35 40 0.51 51
Chatham 2.09 3.22 5.31 41 0.92 29
Telfair 3.77 1.54 5.31 41 -0.27 121
Dade 2.19 2.88 5.07 43 -0.20 112
Gordon 1.55 3.51 5.07 43 0.46 56
Brantley 2.40 2.63 5.03 45 0.56 48
Bulloch 1.56 3.44 5.00 46 1.16 16
Appling 1.87 3.08 4.95 47 0.15 81
Bacon 1.70 3.17 4.88 48 0.11 84
Clarke 1.16 3.65 4.81 49 0.76 35
Clayton 2.33 2.47 4.80 50 0.94 27
Heard 2.22 2.57 4.79 51 0.13 83
Newton 1.63 3.08 4.71 52 0.92 28
Jasper 1.00 3.69 4.69 53 0.23 72
Douglas 1.99 2.69 4.68 54 0.95 25
Oglethorpe 1.95 2.72 4.67 55 0.30 66
Glascock 1.89 2.71 4.60 56 -0.11 107
Whitfield 1.10 3.50 4.60 56 0.25 69
Webster 1.55 3.03 4.58 58 -0.28 122
Towns 0.37 4.18 4.55 59 1.11 19
Harris 1.04 3.42 4.46 60 0.91 30
Atkinson 1.76 2.68 4.44 61 -0.03 97
Butts 1.45 2.97 4.42 62 0.24 71
Carroll 1.20 3.22 4.42 63 0.59 44
Bartow 1.04 3.37 4.41 64 0.66 40
Troup 2.04 2.34 4.38 65 0.43 59
Union 0.80 3.56 4.36 66 1.15 17
Tift 1.28 2.88 4.16 67 0.15 82
Gilmer 0.49 3.64 4.13 68 0.79 32
Stewart 2.28 1.77 4.05 69 0.75 37
Wheeler 0.80 3.25 4.05 69 0.71 38
Lee 0.64 3.40 4.04 71 0.44 58
Polk 1.53 2.43 3.96 72 0.28 68
Lowndes 0.77 3.18 3.95 73 0.75 36
Pierce 0.65 3.27 3.92 74 0.46 57
Rockdale 1.54 2.38 3.92 74 0.66 41
Stephens 0.70 3.23 3.92 74 0.07 87
Camden 1.09 2.82 3.91 77 0.62 43
Haralson 0.90 2.99 3.90 78 0.32 64
Coffee 1.15 2.74 3.89 79 0.19 74
Wilkes 0.64 3.25 3.89 79 -0.44 135
Quitman 1.49 2.34 3.82 81 -0.77 149
Rabun 0.25 3.54 3.78 82 0.33 63
Grady 1.33 2.43 3.76 83 -0.04 98
Houston 0.53 3.21 3.74 84 1.09 20
Lamar 1.04 2.64 3.68 85 0.52 49
Thomas 0.71 2.86 3.57 86 -0.05 100
Crisp 0.75 2.80 3.55 87 -0.26 119
Habersham 0.84 2.66 3.49 88 0.57 46
Glynn 0.46 3.00 3.46 89 0.70 39
Laurens 1.33 2.11 3.45 90 -0.14 109
Jeff Davis 1.39 2.01 3.40 91 0.18 75
Murray 0.98 2.38 3.36 92 0.07 89
Richmond 0.84 2.52 3.36 92 0.10 85
Taylor 1.74 1.62 3.36 92 -0.74 146
Screven 0.22 3.13 3.35 95 -0.41 131
Walker 0.92 2.29 3.22 96 0.16 78
Catoosa 0.51 2.65 3.17 97 0.51 52
Emanuel 0.87 2.30 3.17 97 0.05 93
Early 1.56 1.60 3.16 99 -0.63 143
Muscogee 0.63 2.49 3.12 100 0.17 76
Lincoln 0.02 3.09 3.11 101 -0.10 106
Spalding 0.37 2.73 3.10 102 0.36 61
McIntosh 0.23 2.82 3.05 103 0.24 70
Monroe -0.37 3.34 2.97 104 0.47 54
Meriwether 0.83 2.08 2.91 105 -0.43 133
Hancock 1.03 1.83 2.86 106 -1.12 158
McDuffie 0.37 2.44 2.81 107 -0.07 102
Jones 0.39 2.33 2.73 108 0.02 96
Floyd 0.15 2.58 2.72 109 0.19 73
Macon 0.68 2.02 2.71 110 -0.75 147
Ware 0.38 2.31 2.69 111 -0.07 103
Bibb 0.74 1.92 2.66 112 -0.09 104
Cook 0.24 2.35 2.59 113 0.05 94
Candler 0.33 2.25 2.58 114 0.06 92
Chattooga 0.04 2.22 2.26 115 -0.34 126
Pulaski 0.67 1.53 2.20 116 -0.43 134
Upson 0.11 2.07 2.18 117 -0.27 120
Crawford 0.95 1.19 2.14 118 -0.35 128
Tattnall 0.58 1.52 2.09 119 0.37 60
Echols -0.92 2.98 2.07 120 0.09 86
Colquitt 0.00 2.00 2.01 121 0.06 90
Marion -0.62 2.52 1.90 122 -0.09 105
Miller 0.11 1.79 1.90 122 -0.59 142
Sumter 0.25 1.65 1.90 122 -0.75 148
Johnson -0.29 2.19 1.89 125 -0.31 125
Washington -0.21 2.02 1.82 126 -0.25 118
Wilkinson -0.30 2.12 1.82 126 -0.50 138
Dooly -0.55 2.35 1.80 128 -0.34 127
Evans -0.53 2.31 1.77 129 -0.19 111
Charlton -0.08 1.80 1.73 130 0.15 80
Ben Hill -0.19 1.88 1.69 131 -0.42 132
Jefferson -0.01 1.69 1.68 132 -0.69 145
Jenkins 0.09 1.57 1.66 133 0.07 88
Elbert -0.24 1.89 1.65 134 -0.45 136
Dougherty -0.01 1.47 1.46 135 -0.25 117
Baldwin -0.43 1.86 1.43 136 -0.47 137
Berrien -0.76 2.19 1.43 136 0.16 77
Toombs -0.49 1.92 1.43 136 -0.14 108
Liberty -0.56 1.97 1.41 139 -0.15 110
Wilcox -0.30 1.63 1.33 140 -0.21 113
Dodge -0.36 1.60 1.24 141 -0.30 123
Irwin -0.82 1.97 1.15 142 -0.24 116
Montgomery -0.83 1.95 1.12 143 0.15 79
Mitchell -0.90 1.96 1.06 144 -0.53 141
Seminole -1.01 2.04 1.03 145 -0.52 140
Decatur -0.46 1.47 1.02 146 -0.40 130
Schley -1.33 2.33 1.00 147 0.58 45
Treutlen -1.27 2.19 0.91 148 0.03 95
Brooks -0.70 1.54 0.84 149 -0.39 129
Clay -0.08 0.90 0.81 150 -0.79 150
Worth -0.78 1.52 0.74 151 -0.51 139
Turner -0.67 1.28 0.60 152 -0.85 154
Wayne -1.05 1.57 0.52 153 -0.06 101
Terrell -0.95 1.40 0.45 154 -0.84 153
Bleckley -1.13 1.29 0.16 155 -0.04 99
Putnam -2.98 3.13 0.16 155 0.29 67
Baker -1.16 0.60 -0.57 157 -1.28 159
Calhoun -1.65 0.76 -0.89 158 -0.21 114
Chattahoochee -2.39 -0.59 -2.98 159 -0.83 152

 

Three-fourths of Georgia’s GDP now produced north of the gnat line

About three months ago I stumbled onto a December 2018 report from the U.S. Bureau of Economic Analysis (BEA) that included four years of newly developed county-level gross domestic product (GDP) data.  BEA billed that new set of data as a prototype and announced it would be coming out with an expanded report in December 2019.

That happened today.  This morning, BEA put out 18 years of county-level GDP data for most of the counties in the nation, including all 159 in Georgia, along with an update of its long-standing Total Personal Income and Per Capita Income reports.  So far, in sifting through the data, I haven’t turned up any real blockbuster news, but it does contain a number of interesting nuggets that are worth reporting.

Including:

  • As of 2018, fully three-fourths of the state’s gross domestic product was being generated north of the gnat line. My Trouble in God’s Country 12-county Metro Atlanta region and 41-county North Georgia region accounted for $396.9 billion of the state’s $529.1 billion GDP – or 75.01 percent.  This isn’t a huge surprise, but it is a first, and it represents the high point so far in a steady trend that developed several years ago as the state was clawing its way out of the Great Recession.
  • That divide would probably be even bigger except for the fact that Metro Atlanta got hammered worse than the rest of the state by the Great Recession. I’ve seen that pattern in other economic data – including Internal Tax Revenue data – and this new GDP data simply confirms it.  In 2008, Georgia’s total GDP fell $9.69 billion; of that, $8.26 billion – just over 85 percent of the total loss – came out of Metro Atlanta’s hide.  In 2009, the state’s overall GDP contraction was even bigger – another $17 billion – but the damage was a little more evenly spread; Metro Atlanta’s $11.5 billion loss represented only 67.6 percent of the state’s overall contraction for that year.
  • What’s more, most of the rest of the state initially recovered more quickly from the Great Recession than did Metro Atlanta. In 2010, every region except South Georgia showed a little improvement over 2009 – and South Georgia was basically flat.  Indeed, by 2010 Coastal Georgia and Middle Georgia were back to their 2007 pre-Great Recession levels.  It took Metro Atlanta until 2013 to match its 2007 GDP level.  TIGC’s 41-county North Georgia region took another two years – until 2015 – to get all the way back to pre-recession levels.  South Georgia’s recovery has lagged the other regions.  While its initial hit was relatively modest – down to $34.9 billion in 2008 from $35.7 billion in 2007 – its GDP has bobbed up and down slightly for a full decade, and it didn’t top its 2007 GDP level until 2018.
  • While the Metro Atlanta and North Georgia post-recession recoveries were a little slow getting started, their growth has accelerated over the past five years and easily outpaced the rest of the state. From 2014 through 2018, Metro Atlanta’s GDP grew by 22 percent while North Georgia’s expanded by 15.3 percent.  Coastal Georgia’s GDP grew by a relatively healthy 12.2 percent, but both Middle Georgia and South Georgia were stuck in single-digits – 7.5 percent and 6.1 percent, respectively.  Over that five-year period, the state’s GDP grew by a total of $78.3 billion.  Of that, $68.4 billion – or 87.4 percent – was north of the gnat line.

This table summarizes GDP by TIGC region for selected years and shows both the dollar growth and the percent growth for the most recent five-year period.

Regional GDP Chart

One way of highlighting the widening divide between North and South (and between Metro Atlanta and the rest of the state) is to revisit my comparison from three years ago of all 56 counties in interior South Georgia to Gwinnett County alone (see map).  South Georgia vs Gwinnett County

When I wrote that piece, I found that Gwinnett County, with roughly three-fourths the population of South Georgia, was outperforming South Georgia on every metric I could find – taxes paid, educational achievement, population health, etc.  At the time, the county-level GDP data wasn’t available.

Now that it is, it offers a fascinating addendum to my original comparison – and the data suggest that the Gwinnett-South Georgia gap is getting wider yet.  Going all the way back to 2001, Gwinnett County and South Georgia had very comparable GDPs; South Georgia’s was actually a little bigger — $32.5 billion to $30.8 billion.  As the graph below indicates, South Georgia and Gwinnett County remained at rough parity for about a decade, straight through the Great Recession and its immediate aftermath.

South Ga vs Gwinnett County GDP

But, like Metro Atlanta overall, as Gwinnett County began to recover, it did so at an accelerating pace and has widened its gap with South Georgia.  As of 2018, Gwinnett County’s GDP was nearly $44.2 billion versus just under $36 billion for all of South Georgia.  In the last five years, Gwinnett County’s GDP growth was more than three times that of South Georgia’s.

Another picture to be teased out of the GDP data has to do with county-specific growth rates, and I plan to follow up shortly with a post about that.  But here’s a teaser: Thirty-nine counties had smaller GDPs in 2018 than they did in 2001.  Perhaps predictably, the vast majority were small rural counties, but two were significant regional hub counties: Bibb (Macon) and Floyd (Rome).

Watch this space.

Political common ground hard to find in Georgia. Literally.

A few days after Georgia’s 2018 elections, I did a quick analysis and wrote a piece positing that the state’s widening urban-rural divide went beyond economics and education and extended to politics.  Rural areas seemed to be going more and more Republican while urban and suburban areas were trending more Democratic.  Recently I’ve finally gotten around to taking a deeper dive into past election results and can report a couple of things.

The first thing I can report is that the Georgia Republican Party’s rural strategy is now pretty clear.  Basically, they’re trying to run off all the Democrats.

The second thing I can report is that they’re doing a damn fine job of it.

I am only about half-joking.  One 2018 factoid that I don’t think got nearly enough attention is that Governor Brian Kemp, then the Republican nominee, cracked 90 percent in two rural counties, Glascock (in east-central Georgia, gave him 91.4 percent) and Brantley (deep southeast Georgia, 91.3 percent).  That was a first, at least in modern political history.  Kemp topped 80 percent in 27 more counties.

Even Donald Trump didn’t do that well; in 2016, he piled up 80 percent vote totals in 24 counties but never got into that 90 percent stratosphere anywhere.  Altogether, Kemp won 76 counties with more than 70 percent of the vote; you have to wonder if he wasn’t disappointed with the 36 counties he won with a relatively meager 60 and 70 percent, not to mention the 18 laggard counties that couldn’t deliver more than 50-something percent.

This pattern isn’t exclusively Republican, of course.  Democratic nominee Stacey Abrams broke 80 percent in Clayton and DeKalb counties and got into the 70s in three more counties, and the fact that once reliably red suburban counties are now trending blue has been heavily reported and well documented.  Indeed, as I was finishing up this research, Jay Bookman went up at the Georgia Recorder with an excellent piece documenting the “velocity” with which heavily populated urban and suburban counties are flipping from red to blue.  It’s a good companion to this piece.

It’s worth taking a minute, though, to recognize how and why all this is a big deal.  Up until 1990, Democratic landslides were foregone conclusions and anything less than a 25-point win was a little embarrassing.  But Republicans were clawing their way to relevance and in the 28 years since then every gubernatorial election but one has been decided by 10 points or less; the only real blowout was Governor Sonny Perdue’s 19-point thrashing of Lt. Governor Mark Taylor in 2006.

But that rough statewide equilibrium has masked tectonic shifts taking place beneath the surface.   First of all, Georgia’s Democrats and Republicans have basically swapped geographic territory over the past three decades.  In 1990, the state’s popular Democratic lieutenant governor, Zell Miller, carried 141 counties and posted a relatively modest 8.3-point win over Johnny Isakson, at the time a suburban Republican state senator.  In 2018, Republican Kemp carried 130 counties in his squeaker over Democrat Abrams, the party’s first female and African-American nominee.  Here’s what the raw 1990 and 2018 maps looked like.

 

That’s only part of the story, however, and it is a bit deceptive.  The way those Democratic and Republican voting blocs are assembled has changed radically over the past three decades – and those changes bring the state’s political divide into even sharper relief.

In 1990, Miller beat Isakson 52.9%-to-44.5%, and that spread was generally reflective of what you found around the state.  Fifty-three of the state’s 159 counties were decided in that middling 55%-to-45% range.  Another 50 counties were carried with less than 60 percent of the vote.  In other words, the vast majority of the state’s counties were fairly competitive.

Map the 1990 results based on the extent to which each party carried a county and you get paler shades of blue and red (left).  1990 Shaded MapYou even get some nearly colorless counties; Miller led in six counties with pluralities in the high 40s (Coweta County tipped his way by two votes out of nearly 12,000 cast).  Isakson’s best performance was 61.8 percent, in his home Cobb County.  Miller’s best showing was 75.1 percent in Chattahoochee County, one of seven rural counties where he topped 70 percent.

Last year was very different.  The closest gubernatorial race at least in modern history – Kemp’s 1.4 percent win over Abrams – was forged on the most divided and hyper-partisan political terrain in the state’s history.  Only 15 counties were decided with less than 55 percent of the vote, and only 19 more were won with less than 60 percent.

Put another way, in 1990, 60 percent was the ceiling in 103 of the state’s 159 counties – the most either Miller or Isakson got in any of those counties.  In 2018, 60 percent was the floor in 105 counties – the least either Kemp or Abrams got.  2018 Shade MapThe pastels that were so prevalent in 1990 were in shorter supply last year, especially the reds (right).

As one illustration of the magnitude of the rural shift, Miller’s native Towns County gave him 73.5 percent of its vote in 1990; last year, it went 81.7 percent for Kemp.

The real question in all this is, of course, so what?  How do the shifts and balkanization of the state’s political geography affect policy-making and legislating, especially as it relates to the problems of rural Georgia?  I won’t pretend to know, but my hunch is we’re headed for a reckoning.

For now, both the Georgia House of Representatives and State Senate are still safely in Republican hands, and they can be expected to advance and defend rural interests (even at the expense of urban taxpayers).

But the 2020 Census and the subsequent reapportionment will inevitably change that.  All the mischief that is likely to occur both in counting the bodies and redrawing the lines won’t be able to completely defy the gravitational pull of Metro Atlanta and Georgia’s other urban communities.  Rural Georgia will lose seats, and that will have political and policy consequences.

Exactly what they will be remains to be seen.  The one certain thing is that political common ground is, literally, getting harder and harder to find.

 

The Economic Innovation Group’s case for place-based “Heartland Visas”

John Lattieri, the president of the Economic Innovation Group (EIG), is up at The Washington Post with a provocative op-ed pushing the concept of place-based visas — an idea that would empower struggling rural communities to sponsor immigrants with certain skills to relocate to their communities.

Given the political and cultural realities of the areas of rural Georgia that would benefit most from such a program, I’m skeptical it’ll get much support here — but it’s a fine idea and one the House Rural Development Council would do well to consider.

You can read the entire op-ed at the link above, but here’s a nut graf:

The idea of “place-based” — rather than employer-based — visas has been already implemented in countries such as Canada and Australia. Recently, the Economic Innovation Group released a paper calling for a specific place-based visa program — a “heartland visa” — aimed directly at helping struggling regions break the economic and demographic declines they are experiencing. Such a program would open a new door — without reducing the slots available through other programs — for skilled workers who could meet a range of local needs, from helping grow a local robotics hub, to filling small-town physician shortages. But instead of relying on employer sponsorship, heartland visas would be tied to communities — ones that qualify based on a stagnant or shrinking local workforce, or other economic criteria. The draw could be considerable. Many demographically stagnant U.S. communities offer an enormously attractive chance for a better life for would-be immigrants.

As background, the Economic Innovation Group is a Washington-based think tank whose work I’ve cited several times, including here and here.

 

 

 

 

County Rankings 101: A basic primer

Lately I’ve been updating several data sets and taking a fresh look at various county rankings.  Generally, I find the rankings systems a little frustrating because they tell you who’s best and who’s worst but usually don’t provide much help in the way of explaining the chasm between best and worst.

That said, the rankings systems are still sort of an unavoidable starting point in evaluating the relative standing of Georgia’s 159 counties.  In the process of updating all these rankings, I wound up mapping a bunch of the data and decided that all this might make for a fair primer on how Georgia’s counties stack up in terms of economic vitality, population health, and educational attainment.

A quick description of the rankings I’ll be using below in a table and in a map:

  • Economics – For local economics, I rely on the Georgia Department of Community Affairs. DCA manages the state’s Job Tax Credit program, which is designed to steer jobs to the state’s poorest counties.  As part of that program, it ranks all counties in the state using a formula based on local per capita income, poverty rates and unemployment rates.  Because the JTC program is focused on helping poor counties, DCA ranks the counties from worst-to-best; for my Trouble in God’s Country purposes, I turn the reverse the JTC rankings and list them best-to-worst.
  • Health – Here I used the 2019 county Health Outcomes Rankings produced by the Robert Wood Johnson Foundation’s excellent County Health Rankings & Roadmaps program. This program produces county-level rankings for each state using a formula that factors in premature death rates and a variety of quality-of-life metrics.  (This program also produces a county-level Health Factors ranking, but that ranking includes a number of economic rankings that bump up against the DCA rankings above and the education rankings explained immediately below.)
  • Education – I haven’t been able to find a comparable set of county-level rankings for education, so I’ve created one. Here I’ve taken the most recent educational achievement data I can find (for 2013 through 2017) and created a ranking by averaging the counties’ ranks for the largest percentage of college graduates and the smallest percentage of high school dropouts.

All that done, I’ve created what I call a “rank of ranks” by adding up each county’s economic, health and education ranking and then ranking them based on their totals (see the full list below).  I’ve also mapped that data in a way that slices the state into 16 tiers (creating 15 groups of 10 counties and one of nine counties).

Combined Econ Health & Education Rankings

The color-coding is simple: the darker the green, the better the overall ranking; the darker the red, the worse the ranking; the palest shades of green and red constitute the middling counties.  (You can access an interactive version of this map here.)

(You can find 16-tier interactive maps for the economic, education and health rankings here, here and here, respectively.)

The mapping is useful in a couple of ways, perhaps mostly by spotlighting regional patterns that are virtually impossible to see in a printed list.  For example, you can see, in dark red, five contiguous southwest Georgia that are all in the bottom 10 counties overall: Stewart, Quitman, Clay, Randolph and Calhoun.  At the same time, just south of those five counties, most of the counties strung along the Florida border have more middling rankings; they have at least some positive metrics.

So, one question, it seems to me, is this: If the state has finite resources to invest in economic development, does it invest them in areas that aren’t showing much signs of life?  Or in those that can demonstrate a pulse?  I’ll take a stab at an answer in future posts.

One of the obvious takeaways from the list below is that the economic, health and educational rankings tend to run fairly close to one another.  This is especially true at the top and bottom of the rankings; you’ll find some divergence in the middle ranks, and that’s where you’ll also find the most churn year in and year out.  Conversely, it’s difficult to dislodge counties that occupy the top ranks of these lists, and counties at the bottom have a tough time even getting a toe-hold to try to move up these ranking ladders.  These lists have not changed much in the time I’ve been watching them.

One frustration I have with these and other rankings is that they don’t answer what I call the “so what” question, and there are other metrics that I think would help create useful measures and rankings of the overall viability of Georgia’s counties.  I’m working on a few of those now and hope to be able to update this in the near future.

Here’s the list.   The sort is based on the overall rank, from highest to lowest.

County 2019 DCA Reverse JTC Rankings 2019 RWJ Health Outcomes Rankings 2013-17 Educational Achievement Rankings Total of Ranks Rank of Ranks
Forsyth County 2 1 1 4 1
Oconee County 1 2 1 4 1
Fayette County 4 4 1 9 3
Cherokee County 3 3 8 14 4
Cobb County 5 7 5 17 5
Columbia County 6 6 6 18 6
Harris County 7 8 14 29 7
Gwinnett County 15 5 14 34 8
Coweta County 14 10 13 37 9
Fulton County 23 11 4 38 10
Bryan County 11 20 9 40 11
Dawson County 10 15 21 46 12
Lee County 8 17 21 46 12
Paulding County 18 9 24 51 14
DeKalb County 34 16 11 61 15
Jones County 22 13 30 65 16
Henry County 30 25 12 67 17
Pickens County 9 19 40 68 18
Houston County 33 22 16 71 19
Morgan County 16 27 32 75 20
Camden County 40 18 18 76 21
Jackson County 12 12 53 77 22
Effingham County 13 29 38 80 23
Union County 29 24 28 81 24
Catoosa County 26 23 36 85 25
Chatham County 25 52 10 87 26
Walton County 20 26 42 88 27
Douglas County 41 31 21 93 28
Pike County 21 30 42 93 28
Hall County 17 14 70 101 30
White County 36 21 44 101 30
Lumpkin County 38 39 33 110 32
Glynn County 32 64 18 114 33
Bartow County 27 33 57 117 34
Barrow County 24 35 59 118 35
Monroe County 19 67 38 124 36
Rockdale County 55 45 27 127 37
Rabun County 48 48 37 133 38
Putnam County 53 46 35 134 39
Liberty County 51 57 29 137 40
Chattahoochee County 96 44 6 146 41
Towns County 69 62 20 151 42
Oglethorpe County 28 32 93 153 43
Fannin County 50 59 47 156 44
Long County 80 28 55 163 45
Floyd County 47 54 64 165 46
Habersham County 45 36 86 167 47
Newton County 74 51 45 170 48
Lowndes County 59 78 34 171 49
Carroll County 43 72 57 172 50
Greene County 46 82 46 174 51
Troup County 39 80 56 175 52
Dade County 37 43 96 176 53
Pierce County 49 47 88 184 54
Clarke County 114 56 16 186 55
Hart County 42 58 87 187 56
Crawford County 72 49 69 190 57
Gilmer County 64 63 68 195 58
Madison County 56 68 71 195 59
Thomas County 61 86 50 197 60
McDuffie County 113 34 66 213 61
Muscogee County 68 120 25 213 61
Stephens County 52 105 61 218 63
Walker County 62 87 72 221 64
Whitfield County 54 42 125 221 64
Banks County 31 65 127 223 66
Bulloch County 124 73 26 223 66
Tift County 70 92 65 227 68
Jasper County 35 53 142 230 69
Peach County 81 112 40 233 70
Glascock County 60 37 137 234 71
Gordon County 65 61 113 239 72
Brooks County 73 74 94 241 73
Bibb County 82 135 31 248 74
Haralson County 57 97 95 249 75
Lamar County 99 101 49 249 75
Clayton County 136 69 52 257 77
Lincoln County 85 90 84 259 78
Richmond County 88 125 48 261 79
Seminole County 71 127 63 261 80
Bacon County 58 113 91 262 81
Montgomery County 140 50 72 262 81
Heard County 75 70 118 263 83
Bleckley County 127 85 53 265 84
Schley County 111 71 83 265 84
McIntosh County 90 55 121 266 86
Evans County 67 108 100 275 87
Talbot County 92 77 109 278 88
Franklin County 63 100 117 280 89
Treutlen County 120 60 100 280 89
Lanier County 135 66 80 281 91
Ware County 89 114 79 282 92
Baker County 66 88 129 283 93
Charlton County 102 38 143 283 93
Cook County 93 98 92 283 93
Laurens County 104 118 62 284 96
Pulaski County 115 75 100 290 97
Upson County 86 106 100 292 98
Butts County 78 84 131 293 99
Wayne County 107 96 90 293 99
Washington County 110 81 104 295 101
Miller County 44 155 97 296 102
Worth County 76 95 126 297 103
Echols County 100 40 158 298 104
Screven County 131 94 74 299 105
Polk County 87 91 123 301 106
Grady County 97 102 106 305 107
Elbert County 79 103 124 306 108
Wilkinson County 77 124 108 309 109
Decatur County 95 136 80 311 110
Wilkes County 83 131 97 311 110
Spalding County 98 139 75 312 112
Dodge County 133 117 67 317 113
Coffee County 109 111 99 319 114
Early County 94 150 75 319 114
Baldwin County 143 121 60 324 116
Dougherty County 123 151 50 324 116
Macon County 139 110 78 327 118
Colquitt County 84 116 133 333 119
Chattooga County 103 83 148 334 120
Appling County 91 109 139 339 121
Toombs County 121 144 77 342 122
Wilcox County 134 93 116 343 123
Wheeler County 156 41 147 344 124
Murray County 118 76 151 345 125
Brantley County 129 104 121 354 126
Berrien County 126 123 112 361 127
Meriwether County 105 130 131 366 128
Johnson County 132 89 146 367 129
Webster County 138 119 110 367 129
Dooly County 119 99 150 368 131
Candler County 101 154 115 370 132
Irwin County 145 143 84 372 133
Tattnall County 125 115 134 374 134
Turner County 117 138 119 374 134
Sumter County 147 148 80 375 136
Crisp County 141 152 89 382 137
Atkinson County 108 122 157 387 138
Emanuel County 150 134 106 390 139
Taylor County 153 107 130 390 139
Burke County 137 140 114 391 141
Clinch County 106 149 136 391 141
Mitchell County 130 133 128 391 141
Telfair County 159 79 156 394 144
Terrell County 112 141 141 394 144
Jeff Davis County 116 146 139 401 146
Warren County 122 159 120 401 146
Ben Hill County 155 147 111 413 148
Jenkins County 152 128 135 415 149
Clay County 158 156 105 419 150
Stewart County 154 126 144 424 151
Randolph County 151 137 137 425 152
Calhoun County 149 132 148 429 153
Taliaferro County 144 129 159 432 154
Jefferson County 146 142 145 433 155
Marion County 142 145 151 438 156
Twiggs County 128 158 154 440 157
Quitman County 148 157 154 459 158
Hancock County 157 153 153 463 159