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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.


  • 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



Albany and Macon make Business Insider list of America’s 50 most miserable cities

Business Insider went up yesterday with a story about the 50 most miserable cities in America, a list that included Albany at 18th and Macon at 47th.  The wonder is that there weren’t more, and I’d wager that Augusta and Columbus, perhaps among others, didn’t miss the list by much.

Business Insider built its list using Census data that “(took) into consideration population change (because if people are leaving it’s usually for a good reason), the percentage of people working, median household incomes, the percentage of people without healthcare, median commute times, and the number of people living in poverty.”

To a significant degree, the Business Insider list echoes findings by the Economic Innovation Group (EIG), which for the past two years has used its Distressed Communities Index (DCI) to rank all U.S. counties as well as all cities with populations of at least 50,000.  EIG’s DCI includes several of the same metrics as Business Insider’s misery index, but one significant difference is that Business Insider looks at population trends.

I think that’s an important metric and one that’s largely overlooked in evaluating the health of Georgia’s cities and counties.  By my count, 68 of Georgia’s 159 counties have (according to Census Bureau estimates) lost population over the past five years.  The vast majority of those are rural Middle and South Georgia counties.  (I’ve long thought the Georgia Department of Community Affairs needs to retool its Job Tax Credit rankings to reflect population trends, but that’s a subject for another post.)

Another interesting aspect of the Business Insider list has to do with what’s missing.  While Florida had a half-dozen entrants on BI’s bottom 50, Georgia’s other contiguous Old South neighbors managed to stay off the list entirely: Alabama, Tennessee, North Carolina, and South Carolina didn’t have a single bottom 50 city amongst them.  Even Mississippi and Louisiana had only one bottom 50 city apiece — Jackson at 32nd and Shreveport at 45th.  How so?  Is there anything to be learned by comparing Georgia’s cities to those in our neighboring states?

Finally, the BI list reinforces (at least in my mind) the notion that any serious rural revitalization effort has to include — and probably start with — the regional hub cities.  As I’ve written before, many of Georgia’s major regional cities are suffering their own types of problems, and if they’re allowed to slide past some hard-to-discern tipping point, it would probably doom the rural counties around them for decades to come.

Couple of reference notes:

  • I’ve written about the Economic Innovation Group’s Distressed Community Index a couple of times, here and here.
  • You can find the details of EIG’s Distressed Communities Index on page 3 of this report.
  • And finally, a tip of the hat to Doug Hall, TIGC’s man in Mexico, for flagging the Business Insider list on Facebook.  I probably would have missed it otherwise.

Rural Georgia: Doing its part to send Metro Atlanta kids to college

One recurring theme in my Trouble in God’s Country research is that Metro Atlanta is paying the lion’s share of taxes in Georgia while consuming a much smaller portion of social services, such as Medicaid and food stamp benefits.  Rural Georgia, generally speaking, doesn’t cover its costs for those services.

In at least one regard, however, rural Georgians seem to be doing their best to balance the books.  They’re spending millions of dollars on Georgia lottery tickets that help send tens of thousands of Metro Atlanta kids to college.

Of course, a fair number of rural Georgians get advanced education through lottery-funded HOPE scholarship grants – at either University System of Georgia (USG) institutions or one of the state’s technical colleges – but Metro Atlanta is clearly getting the better end of this particular deal.

I’m not sure this qualifies as real news.  It probably won’t come as a surprise to political leaders and policymakers who work in these areas.  Also, I should stipulate that the Georgia lottery and HOPE scholarship data I’ve been studying comes with a handful of significant caveats.  Available data from the University System of Georgia (USG) and the U.S. Census Bureau make it possible to paint pretty precise county-level and regional pictures of educational attainment patterns and college enrollment trends throughout the state.

The lottery and HOPE data are a little fuzzier and the resulting pictures are therefore a bit blurrier.  After studying the data for a bit, I’ve decided the best way to tell this story is to present two views – a big-picture, macro view, and then a more isolated micro snapshot.

First, the big picture, and here the caveats are especially important.  Lottery sales are reported on a county-specific basis, but that doesn’t necessarily mean that a lottery ticket sold in a particular county is purchased by a resident of that same county – or even a Georgia resident.  Inter-county or interstate sales aren’t tracked, although it’s pretty obvious that many of the Georgia counties on the Alabama border are pulling in millions of dollars from that state[i].

HOPE scholarships, meanwhile, are awarded to students in their county of residence, not their county of origin.  Odds are that the initial awards do go to students in their county of origin, but it’s also obvious that many students effectively move to their college towns and establish residence there while they’re still receiving HOPE awards.  Even a cursory review of data for college communities makes that clear.

Still, the big picture is a useful starting point.  For that I organized lottery sales and HOPE scholarship data by my five Trouble in God’s Country regions – 12 Metro Atlanta counties, 41 North Georgia counties, 43 Middle Georgia counties, 56 South Georgia counties, and seven Coastal Georgia counties.  Here’s how those numbers shake out:

Regional Lottery and Hope Analysis

The obvious takeaway from this is that Metro Atlanta and North Georgia were the only two regions that got larger shares of the HOPE scholarship grants than they ponied up for lottery tickets.  The largely rural areas of Middle, South and Coastal Georgia didn’t do nearly as well.

For the micro view, I organized a cluster of 16 largely rural counties in interior Middle and South Georgia; I’m calling it the South Central Georgia Cluster[ii]Cherokee S. Georgia MapAll these counties are far enough away from a state line that they shouldn’t get a lot of interstate lottery dollars, and most (with a couple of exceptions) are well off the beaten path of the major interstate highways.  In other words, it’s a fair presumption that their lottery sales are largely local.

As a point of comparison, I chose Cherokee County, an exurban county on the northern edge of Metro Atlanta (that’s the green county in the northern part of the map).  In 1994, the 16-county cluster of rural counties was home to a little more than twice as many people as Cherokee County – 231,402 to 107,569, according to Census estimates for that year.  But from the git-go, the rural counties were more enthusiastic lottery players.  In 1994 (the first full year of the lottery), lottery sales in those 16 counties were 3.6 times as much as in Cherokee County.

Today, the populations are roughly equivalent: 254,149 for Cherokee County versus 271,182 for the 16 rural counties, based on 2018 Census Bureau estimates (the latest available).

But lottery sales in those 16 counties are still more than double Cherokee County’s: $151.9 million to $69.3 million.  If Cherokee County and the 16 South Central Georgia counties constituted a state of sorts, here’s what their total respective shares of the lottery sales and HOPE grants would look like over the life of the programs:

South Central Cherokee Comparison

Perhaps more interesting than the total shares of lottery sales and HOPE grants is the way the trend lines evolved over time.  From 1994 through 2011, the 16-county South Central Georgia Cluster received more in HOPE scholarships than Cherokee County.  But in 2012, both areas took major hits in HOPE funding (as did the state as a whole).  The South Central counties suffered a 43.2 percent hit in HOPE scholarship grants and still haven’t gotten back to their 2011 level; Cherokee County dropped 26.5 percent but recovered more quickly and had gotten nearly all the way back to its 2011 high by 2017.  The result has been that Cherokee County passed the 16 rural counties in HOPE grants in 2012 and has been widening the gap ever since.

South Central Cluster Cherokee Trendlines

This matches a pattern I’ve seen in other education-related data.  As I noted in my last post, up through 2010, Metro Atlanta had trailed the other 147 counties in the state in fall freshman enrollment at USG colleges.  But those lines crossed in 2011 and the gap has been widening ever since.  The same pattern shows up in a comparison of Gwinnett County and all 56 counties of interior South Georgia from late 2016.

I’ve got more work to do on all this.  I need to take a deep dive into enrollment patterns at the state’s technical colleges, and I’m expecting to get a breakout on HOPE scholarship grants by type of institution – USG, technical college, or private college – fairly soon.  I’ll try to update all this within a couple of weeks.

Even with that work still to be done – and with all the caveats stipulated above – it seems fair to suggest that a lot of poor folks in rural Georgia are sending a lot of Metro Atlanta kids to college.


[i] I figured this out when I was trying to get a handle on lottery sales patterns in different parts of the state.  One approach I took was to calculate lottery sales per capita – in other words, to divide total lottery sales by population.  The state average for 2018 was $437.08 per capita.  Far and away the top producer was Quitman County at more than $4,800 per capita.  One of the smallest and poorest counties in the state, Quitman County sits forlornly on a stretch of the Chattahoochee River that is known as Walter F. George Lake in Georgia and Lake Eufaula in Alabama; Quitman County’s top economic asset in this regard is no doubt the Ernest Vandiver Causeway, which spans the lake and connects it to the State of Alabama, which is one of about a half-dozen U.S. states that still doesn’t have a lottery of its own.  While Quitman County’s per capita sales dwarf those of all other counties, seven of the top 10 per capita sales counties border Alabama, and two others are just east of Quitman County.

[ii] The counties included in the South Central Georgia Cluster are: Atkinson, Bacon, Ben Hill, Bleckley, Coffee, Dodge, Irwin, Jeff Davis, Laurens, Montgomery, Pulaski, Telfair, Toombs, Treutlen, Wheeler, and Wilcox.

Kemp’s “rural strike team” should be a step in the right direction. We’ll see.

First, props where they’re deserved.  Georgia Governor Brian Kemp actually took a step in the right direction Monday when he told the AJC he’s creating a “rural strike team” to try to stimulate economic development in the state’s dying hinterlands.  He reportedly plans to unveil the details in Swainsboro on Thursday.

Only time will tell whether this is anything more than eyewash and window dressing, but there were a couple of promising hints in the AJC’s story.  One was that he’s bringing together “a half-dozen state departments and higher education agencies” to drive the effort.  That implies a more strategic approach and focus than I’ve seen so far, and one that’s long overdue.  Still, the strike force will have its work cut out for it.

The state of Georgia is quite literally in the process of tearing itself apart along rural and urban lines, and especially between Metro Atlanta and just about everything else from Macon south.  These are not slow-moving trends.  No matter how you come at it – economically, educationally, health-wise, politically – you can pretty much watch the division in real-time.

Take education as an example.  In 1970, according to Census Bureau data, there were fewer than a quarter of a million college graduates in the entire state of Georgia, and slightly more than half of them lived outside Trouble in God’s Country’s 12-county Metro Atlanta region.  Today, the state is home to nearly two million college graduates and 63 percent of them live in Metro Atlanta.

That disparity is only going to grow.  Up until 2011, the 147 counties outside Metro Atlanta sent more freshmen to University System of Georgia (USG) institutions than the 12 Metro Atlanta counties, which is probably what you’d expect.  But in 2011 Metro Atlanta overtook the rest of the state and ever since then it’s been sending significantly more freshmen to USG colleges and universities than the rest of the state combined (see graph at right).

At the state’s two flagship universities, the University of Georgia and Georgia Tech, the gulf is bigger yet.  At Georgia Tech, 75.5 percent of the Fall 2018 in-state freshmen came from Metro Atlanta; at UGA, just over 63 percent of the in-state freshmen came from Metro Atlanta.  By my count, 71 counties, all rural, didn’t send a single high school graduate to Tech in the fall of 2018; at UGA, the same was true of 22 counties.

These differences matter.  Not only do they fuel Metro Atlanta’s outsized economic growth, they drive widening disparities in taxes paid and social services consumed.  With just under half the state’s population, Metro Atlanta in 2016 coughed up nearly two-thirds of the state’s federal taxes while consuming, as examples, about 37 percent of the state’s Medicaid services and about 41 percent of its food stamp benefits.

You can do the math on the other side of that equation.  Oh, okay, I’ll help: the 99 counties that constitute Trouble in God’s Country’s Middle and South Georgia regions don’t even come close to covering their own Medicaid and food stamp costs, let alone anything else.  After a while these kinds of numbers get to be politically untenable.

Some years back, I presented a very early version of my Trouble in God’s Country research to a group made up primarily of legislators.  One of those was House Ways and Means Committee Chairman Mickey Channell, who has since passed away.  “What do you do about it?” he asked.  I didn’t know then and still don’t, at least not entirely.  But I’ve since given it a lot of thought and would offer the following as a running start at an answer:

  • First, the multi-agency approach suggested by Kemp is the right idea — and critical. The state of Georgia arguably has one of the strongest and most sophisticated economic development infrastructures in the nation, but my sense is that its work has been largely siloed and not well integrated with other departments and agencies of state government.  The state’s urban-rural divide and the deterioration in much of rural Georgia constitutes a truly strategic problem.  It’s not an exaggeration to call it an all-hands-on-deck crisis.  In addition to the departments of Economic Development and Community Affairs, the team will have to include high-level engagement from throughout the state’s education bureaucracies and, I’d argue, public health and human services.
  • Start with a realistic evaluation of the state’s various rural areas and recognize that some are more viable than others. Some politicians like to say they want to run government like a business.  In business, if you’re losing money year after year, sooner or later you call it quits.  Under that theory, I can take the governor to 50 or so counties where he ought to turn out the lights and call it a day.  We can’t do that, of course, but it ought to be possible to invest discretionary tax dollars and other public resources in areas that at least have a fighting chance of generating a return, in terms of new growth and economic prosperity.  In other words, resist the normal political temptation to attack the worst problems first; instead, identify the regions that still have a pulse and see if they can be saved.
  • Shore up the regional hub cities first. It’s not just Georgia’s purely rural areas that are in serious decline; a lot of the major regional cities – Macon, Columbus, Augusta, etc. – are suffering various types of distress, and they are vital to rural areas around them.  As a practical matter, it may be too late to do much good for Albany and the rural counties between it and the Alabama line; that entire region of the state is bleeding population and shrinking economically to a degree that may put it beyond near-term salvation.  Figuring out how to strengthen other major hubs in ways that will enable them to better support their rural neighbors should be pretty close to the top of the to-do list for Kemp’s strike force.
  • Challenge the rural areas to compete for the state’s attention and dollars. Hopefully, one of the initiatives that will come out of Kemp’s effort will be a process by which multi-county regions or areas of the state can apply to the state for funding and technical support.  It shouldn’t be entirely on Kemp’s strike force to show up in Enigma, Ga., and say, “We’re from state government and we’re here to help you.”  Rural areas should be required to come forward with a rational vision, demonstrate that they have the leadership capacity to drive a major effort, and put serious skin in the game.  There should be milestones in that process and a credible system for evaluating progress.
  • Bite the political bullet and implement Medicaid Expansion. I should have listed this first but figured Republicans would stop reading right then and there.  Refusing to take advantage of Medicaid Expansion was the major failure of Nathan Deal’s administration and Kemp shows little inclination to do any better.  His attempt at a “waiver” approach (an all but transparent effort to deny Barack Obama any credit for the program) apparently can’t even pass muster in Donald Trump’s Washington.  Meanwhile, rural hospitals continue to close and people continue to die, prematurely and unnecessarily.  Even if Deal, Kemp & Co. are blind to the health benefits of Medicaid Expansion, you’d think they’d see the economic benefits of pumping billions of dollars into rural Georgia.  Maybe all things Obamacare still constitute a third rail of politics for Georgia Republicans, but my hunch is that the radioactivity levels tied to Medicaid Expansion have diminished to a point that it could be a political winner for Kemp – a Nixon-to-China sort of moment.

Again, I don’t know whether Kemp’s ”rural strike force” will prove to be anything more than eyewash and window dressing, but it’s encouraging that he’s taking a stab at the problem.  Hope springs eternal.

(c) Trouble in God’s Country 2019


‘Hey, Baby, you wanna help save America from socialism?’

A young up-and-comer in the Georgia Republican Party made headlines recently when he proclaimed that Republicans have a “fertility advantage” and suggested that a core GOP strategy going forward would be, basically, to out-breed the Democrats.

Brant Frost V, who is the second vice chair of the state GOP, told a recent meeting of Oconee County Republicans:

“Christian and conservative women have a 35 percent fertility advantage over Democrat women.  And the more conservative a woman is, the more likely she is to be married and have lots of kids – three, four, five, six kids.  And the more liberal and leftist a woman is, the less likely she is to even be married and have any children at all …”

I’m not making this up.  You can watch the video here.  Frost begins his remarks about an hour and seven minutes into the meeting.

As political Hail Marys go, you’ve got to give Frost credit for audacity.  I’m not sure it’ll work politically, but it’s bound to have a major impact on the Christian conservative singles-bar scene.  (“Hey, Baby, you wanna help save America from socialism?”)

But here’s the thing: Frost has absolutely zeroed in on the Republicans’ political problem.

According to data recently posted by the Georgia Department of Public Health (DPH), 79 of Georgia’s 159 counties had more deaths than births in 2018.

That’s actually news: it’s a record high, and it extends an alarming trend that started about a decade ago.  DPH’s public databases of births and deaths go back to 1994, and for about the first 15 years the number of counties reporting more deaths than births floated between about 10 and 20.  But that changed starting in 2010, as this graph shows.

More Deaths than Births Column Graph

These numbers are politically relevant to Frost’s grand strategy for a couple of reasons.  First, 78 of the 79 counties are rural; the only one that’s not is Fayette County, long recognized as a popular redoubt for retirees well past child-bearing age.  It’s also dependably Republican.

Second, Georgia’s rural regions have voted overwhelmingly Republican in the recent past.  Georgia’s current Republican governor, Brian Kemp, owes his narrow election over Democrat Stacey Abrams last year to extraordinarily high turnout and huge margins in rural Georgia.

And the importance of Frost’s vision – for conservative women to have “three, four, five, six” babies each – becomes even clearer when you drill down into the data and break it down by race.  Whites voted three-to-one for Kemp while blacks went more than nine-to-one for Abrams, according to an election-season poll of Georgia voters.

The number of counties reporting more white deaths than births was 104.  Eighty-four of those counties voted for Kemp.

These maps should leave little doubt about the vital importance of Frost’s strategy.  The first one spotlights the counties that had more white deaths than births in red; the second one shows the counties that went Republican in the 2018 gubernatorial in red and the ones that voted Democratic in blue.  It’s obviously not a perfect match, but it’s enough of an overlap that it ought to give your average GOP strategist a little heartburn.


Further, 57 of Kemp’s counties lost population between 2012 and 2017, according to Census Bureau estimates, and most of the Kemp counties that grew did so at rates that lagged the state average and, critically, traditionally Democratic urban areas.

That’s not the end of Frost’s political math problems.  At this point, there’s a fair body of polling data to suggest that Millennials lean decidedly toward the Democratic Party.  Last year Pew Research put the percentage of Millennials who consider themselves “consistently” or “mostly” conservative at 12 percent versus 57 percent who put themselves in a liberal category; the remainder put themselves in a “mixed” category.

The picture may be a little better for conservatives among Millennials who are actually registered to vote: Pew put that split at 59-32 in favor of Team Blue.  But it also found a gender divide that may impact the Frost strategy.  Some 41 percent of Millennial males tilted Republican, while only 23 percent of Millennial females did so.

For the sake of what I know is a dubious illustration, let’s say that all the Millennial women in counties that went for Kemp are the type of good Christian conservative women Frost has in mind and that the Millennial women in the Abrams counties are all godless Commies.

As the actual math on this works out, the women in the Kemp counties already have a consistently higher birth rate than the ones in the Abrams county; in 2018, the Millennial birth rate in the Kemp counties was 79.8 births per 1,000 women versus 72.7 in the Abrams counties.

The problem is, the Kemp women are badly outnumbered.

In 2018, 647,492 Millennial women in the Kemp counties gave birth to 51,687 babies (who, in this scenario, will all grow up to be good Republican voters).  The 886,215 Millennial women in the Abrams counties delivered 64,453 baby Democrats.

To close that gap of nearly 13,000, Millennial women in the Kemp counties will have to up their game; just matching the Democratic output would require them to raise their annual birth rate to just under 100 births per 1,000 women.  This arithmetic is admittedly (shall we say) speculative, but it seems clear that the good Christian women in Frost’s political fantasies will have their work cut out for them.

Now, it turns out there may be a silver lining for Republicans in all this data.  The same Pew research that found that conservatives had a surplus of men also found that liberals had more women and might not have enough men to go around.  This creates an opportunity for those extra conservative males to try their luck with liberal women (friendly pro tip: leave the MAGA cap in the pick-up).

Of course, such a development might create an entirely new classification problem for the Department of Public Health.  DPH keeps track of all the state’s births and deaths and classifies them in different ways – including ethnicity and race (white, black, multiracial).  If a Republican cross-pollination initiative works, DPH might have to add a political classification – Republican, Democrat, or Multi-partisan.