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Annual Power Ratings stable at the top, volatile at the bottom

Now that we’ve published the Partner Up! for Public Health campaign’s 2013 Community Health & Economic Vitality Power Ratings, we’ve begun to dig into the barrels full of data that underlie those rankings.  What we’re looking for is interesting or useful nuggets of information that help inform the discussion about the relationship between health and the economy at the local level.

Sometimes you spot patterns or trend lines that seem interesting, but it’s not always easy to interpret the data or explain what – if anything – it might mean.  That’s the case with today’s topic.

One of the things we’ve observed, somewhat casually, in producing our Power Ratings – based on health outcomes data compiled by the University of Wisconsin and economic rankings calculated by the Georgia Department of Community Affairs – is that the top of the list hasn’t changed much year-to-year while the bottom of the list has been pretty volatile.  So we decided to take a deeper look to see if we were correct.

While we’ve only produced formal Partner Up! Power Ratings for 2012 and 2013, the fact is that we’ve got data going back to 2010, so we decided to do a four-year look-back to see if our stable-at-the-top/volatile-at-the-bottom pattern would hold up over that period of time.  It does.

To conduct this analysis, we decided to identify the counties that were in the top 10 percent and the bottom 10 percent of Georgia counties for each of the four years from 2010 through 2013.  Since Georgia has 159 counties, we looked at the top and bottom 16 counties for each year.

The first thing we looked at was the number of repeat performers on each list.  As it turns out, these 14 counties made the top 16 in all four years:

County 2010 Power Ratings 2011 Power Ratings 2012 Power Ratings 2013 Power Ratings
Oconee Co. 1.5 1.5 2 2
Forsyth Co. 3.5 4 3 2.5
Fayette Co. 3 2.5 2 3.5
Columbia Co. 4.5 5 4.5 4.5
Cherokee Co. 6 6.5 5.5 6
Cobb Co. 6 6.5 7 7.5
Gwinnett Co. 7.5 9 7.5 9
Coweta Co. 13 15.5 16 12
Lee Co. 17 17 16.5 12.5
Bryan Co. 18 14.5 9 14.5
Hall Co. 14 15.5 16.5 15
Morgan Co. 17.5 19 18 15
Harris Co. 8 8.5 13 17
Houston Co. 14.5 13.5 15.5 17.5

Henry and Effingham counties made the Top 10% in 2010 and 2011 but fell out in 2012 and were replaced by two North Georgia counties, Towns and Dawson, for that year and 2013.  All in all, again, a very stable list.

Not so at the bottom.  Over the four-year period, a total of 26 counties made the Bottom 10% at least once and only six made it all four years.  The unfortunate half-dozen counties that made the Bottom 10% all four years are: Burke, Turner, Jenkins, Randolph, and Crisp and Wilcox (adjoining South Georgia counties that tied for dead last in this year’s Power Ratings).

The remaining 20 counties that made the Bottom 10% (and the number of years they were there) are: Laurens (1); Wilkinson (1); Johnson (1); Decatur (1); Clinch (1); Jeff Davis (2); Ben Hill (3); Macon (3); Stewart (1); Talbot (1); Jefferson (3); Cook (1); Quitman (3); Terrell (3); Warren (3); Taylor (3); Telfair (3); Hancock (2); Calhoun (3) and Taliaferro (1).

The most obvious observations to be made from this analysis have to do with geography and population.

All 26 of the counties that have made the Bottom 10% are located in Middle or South Georgia.  Of the 18 that made the Top 10% at least once, only two are from South Georgia: Lee County, an affluent suburban county just north of Albany and Dougherty County, and Bryan County, which adjoins Savannah and Chatham County near the coast.  The other 16 are in North Georgia or the Metro Atlanta area.

The 26 are also exceedingly small.  Most have populations of less than 10,000 people, and many are shrinking.  Only three – Burke, Crisp and Decatur – have populations of more than 20,000.

The Top 10% counties, meanwhile, include Metro Atlanta counties that are home to the bulk of the state’s population and smaller bedroom counties that are attached to larger urban communities throughout the state.

In future blogs, we’ll examine the stability/volatility issue in the middle groupings of counties – and take a stab at figuring out what if anything useful the data tells us.

 

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