The past century and a half of Appalachian history is deeply entwined with the coal industry. Over the course of the past century, coal has gone from being a burgeoning industry to a dying vestige of the past. How does this affect the people in Appalachia who were dependent on the coal industry as a way of life? Using data from the United States Geological Survey, each of the 52 counties in Appalachian Tennessee was assigned a value aspiring to correspond to levels of mining activity. The data was then tested for correlation with population growth rates using historical census data. The result of analysis showed that in the early half of the 20th century, counties with more mining activity tended to experience higher rates of population growth. This correlation lowers to non-statistically significant amounts in the second half of the century.
13 thoughts on “Mining Activity and Population Trends in Appalachian Tennessee”
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This project is inspired by the idea of abandoned coal towns. I wanted to get a deeper look at the ebb and flow of people in and out of these areas of high industry as the industry decays. I focused on Appalachian Tennessee in particular because there is not as much research as opposed to other “iconic” Appalachian areas like West Virginia and Kentucky. The correlation I found isn’t as strong as I hoped for, but it does show something about Appalachian TN pre- vs post-WWII.
Henry, this is a very interesting time-period project as it shows a variance of how the importance of coal-towns have shifted over the past decade-plus so significantly. After reading this, my next question would be: “so where did the populations who were previously centered around coal towns go?”. I would be interested to see if a similar weak positive correlation has developed between population growth and the newer energy sources in the area. For example, as the correlation between coal and population declines, does the correlation between solar energy operations and population increase?
Although I took on this project specifically to look at population loss, I actually didn’t find as much population loss as I expected. The phenomenon I observed was more often that after a growing population in the early half of the 20th century, the population just… stopped growing. Population was collected on a county scale, so this suggests that after a lot of these coal towns shut down, people didn’t move far (stayed within the county).
There is a movement for Appalachia to refocus on renewable energy (specifically solar), and in many areas it has shown positive results. I can’t really comment on the patterns restricted to Tennessee, but the region is unique in that Knoxville is one of the strongest economies in Appalachia. Considering the brainpower that Oakridge draws to the region, I think TN is set up better to adapt than a lot of other Appalachian regions.
At least where my family lives, I can confirm that this is true. Even while jobs are almost non-existent, people are not leaving.
Henry,
This is a really interesting study! My parents and several preceding generations hail from Campbell and Claiborne Counties, and my father and three of my siblings still live there. Two of my brothers-in-law have been employed in coal mines in Claiborne County, as this has become one of the primary forms of employment in the area. My grandfather was also a coal miner for the greater part of his adult life in this area. I can say from my family’s experience that the decline of the coal industry has taken a significant economic toll on the residents of these counties. Interestingly, while new families are not moving into this area, few families are leaving to look for work elsewhere. If you haven’t already, I would recommend looking into the work of Zada Komara at the University of Kentucky. She is a historical archaeologist who specializes in the archaeology and ethnography of abandoned Appalachian coal towns (though her work is primarily in KY). I have no doubt that she would be happy to discuss her work and yours! Great job!
This is a very interesting study. The logic is clear and the research design is legit. One of the questions is the selection of 10 km as buffers. What is the rationale for this 10km and why do we need to know the density within a buffer? I think it would be better to investigate more about the population migration after the coal economy collapsed. What is the potential reason for people to stay without working opportunities provided by the coal industry? Thanks!
Hi Bowen, thanks for giving my project a look! The choice of 10km buffers was to capture the influence of mining activity outside the county. I chose the 10 km buffer to capture a sort of “commuting” sphere of influence. While not many miners commuted, other jobs related to the industry may have and I wanted a way to see that.
My best guess as to why people would stay in the county after a collapse of a coal town is just because they couldn’t afford to move elsewhere. Most miners in coal towns were paid with company store credits, which meant they wouldn’t have much economic agency after the coal town collapsed.
Hello Henry,
This is a really unique project and I enjoy it a lot. This is a super unique project and it is ridiculously important when evaluating the economic and population decline of the rust belt and Appalachian states. I think your poster is put together well and visually appealing, and I particularly like Figure 3. In a previous comment, you mention the population movement after a coal town dies is likely in-county is also super interesting. The sudden loss of relatively high-paying jobs in low-population counties has to be very stressful to the local economy, especially when the people who lost their jobs stay local.
I do have some questions. As someone else mentioned, the rationale for the 10-kilometer buffer is not apparent. Also, would it be possible to insert a map demonstrating population changes over time on the county level? You address it in your well-written conclusion, and the association between mining towns and population stagnation/decrease seems logical, but currently, I have no way of seeing the correlation myself. Although I do like Figure 3, it is a unique way of demonstrating your point.
Hi Spencer,
The choice of 10km buffers was to capture the influence of mining activity outside the county. I chose the 10 km buffer to capture a sort of “commuting” sphere of influence. While not many miners commuted, other jobs related to the industry may have and I wanted a way to see that.
Yeah, I did actually create that graph and then didn’t have space to put it on the poster… I don’t know how to add it to the website now. but you can see it here: https://docs.google.com/document/d/1un6grHeg_glMekpMhoPvzqs1ANv8vW3CAVa1z1D32vA/edit?usp=sharing
You can see how in the earlier half of the century, Appalachian counties grew faster than Tennessee counties, presumably because of the coal industry, but when coal started losing it’s popularity the population trends of Appalachian counties started to be more in-step with the state as a whole. This chart doesn’t show the differential of mining activity in Appalachia, so I thought it was more illustrative of my point to show the correlations instead.
I wonder if the decrease in mining activity in this region will continue at a steady rate or become exponential based on the push towards renewable energy. Interesting project, had not thought about coal mining in terms of population.
Unfortunately, I could not find data sufficient to make inferences about the levels of local mining activity over time. This level of mining here is based on a map of all mines regardless of their time period of operation. To see how coal has compared to other sources of energy, I found this page (https://www.eia.gov/todayinenergy/detail.php?id=26912) really enlightening. Coal consumption has actually been increasing until within the last decade (and looking at the graph, who’s to say it will continue to decrease?) but it has steadily lost market share since mid-20th century.
Hi Bowen, thanks for giving my project a look! The choice of 10km buffers was to capture the influence of mining activity outside the county. I chose the 10 km buffer to capture a sort of “commuting” sphere of influence. While not many miners commuted, other jobs related to the industry may have and I wanted a way to see that.
My best guess as to why people would stay in the county after a collapse of a coal town is just because they couldn’t afford to move elsewhere. Most miners in coal towns were paid with company store credits, which meant they wouldn’t have much economic agency after the coal town collapsed.
You have a solid start to a longer term project here, Henry, good job. It’s a prime candidate for space-time analysis, perhaps emergent hotspot analysis via space-time cube or similar. Emergent hotspot analysis works just like regular hotspot analysis but with time added–i.e. all things are related, but near things–in space and in TIME–are more related than distant things. Thus, temporal proximity matters as well. Such an analysis might capture ebb-and-flow over time, rather than beginning-end state comparison. Other things to consider: demography has three fundamental variables: fertility, mortality, and migration. It seems like you are mostly focusing on migration here, but it is implicit. How might fertility and mortality be controlled for or otherwise tracked? This is an inherently interesting and timely topic, good going.