Introduction

Natural disasters can uproot peoples’ entire lives in a matter of days. It goes without saying that such untimely acts of God can create a multitude of challenges for election administrators in affected areas. Do these events affect voter turnout? Are certain areas less equipped to handle these disasters? What can be done to mitigate the adverse effects? In this paper, we will explore these questions and others as we examine the effects of Hurricane Matthew and how it potentially affected voter turnout in the 2016 general election specifically in Georgia.

Methods

For the sake of this analysis, I decided to examine the state of Georgia by its counties. All the source data used in my observations also split Georgia by county, so this serves as the best, most compact way to study the location of voters. The goal is to determine which areas were most affected and then analyze turnout, demographics, and other information that could reveal interesting findings. To determine such areas, I found a map of Georgia, also split by counties, published by the Federal Emergency Management Agency (FEMA)11 Georgia Hurricane Matthew (DR-4284) https://www.fema.gov/disaster/4284.

This map indicates which counties required different levels of assistance. Clearly, the southeastern coastal areas were designated to receive both individual and public assistance. As such, I hypothesized that these areas are more likely to have faced lower turnout than the rather high state average of 76.53%22 Georgia Statewide General Election 2016 Results http://results.enr.clarityelections.com/GA/63991/184321/en/summary.html.

Furthermore, I hypothesized that these counties likely had higher rates of early voting than the state did overall. It is well-documented that states experiencing hurricanes often see spikes in early voting since voters might be displaced and early voting does not restrict voters by precinct.

In my observations, I used a Georgia Daily Voter File from November 2016, as provided by Professor Michael McDonald, along with a publicly available copy of the Georgia Voter History file found online33 Georgia Voter History http://elections.sos.ga.gov/Elections/voterhistory.do.

Findings

Challenges

I faced a variety of challenges while compiling this data into a presentable final product. As an R novice, there was a rather steep learning curve to understanding the intricacies of coding in the proper formats, especially with regards to reading in data. I encountered a variety of errors and warning messages that interfered with my progress throughout this semester, and sometimes it took quite a while to figure out what exactly needed to be done to proceed.

I would also like to note a choice I made that might have affected the results of my research. Before conducting any analysis, I filtered out inactive voters from the Georgia voter file. This lowered my statewide turnout rate denominator by about 600,000 individuals, causing my aggregate voter turnout figure to be 80.86%. This number is well higher than the official rate of 76.53% posted by the Secretary of State77 Georgia Election Results http://results.enr.clarityelections.com/GA/63991/184321/en/summary.html, but it was still the most accurate way to handle the data.

I find it quite confusing how there potentially exist a couple hundred thousand individuals in Georgia that were able to cast their ballots in the 2016 general election despite being marked as inactive in the voter file at the time. Perhaps there was a voter registration surge during the week-long extension of the registration deadline specifically in Chatham County (which includes Savannah, GA)88 Georgia County’s Voter Registration Deadline Extended https://www.newsweek.com/georgia-lawsuit-seeks-voter-registration-deadline-extension-after-hurricane-510270. Since I removed all individuals from the voter file who were registered after the original October 11th deadline, this could very well be a major factor in my lower denominator.

Lastly, I wanted to investigate turnout using other demographics, like party breakdown, but since there is no party registration in Georgia, the only available data in the voter file is “party last voted”. Because such a low percentage of the registered voter population participates in party primaries, most of the cells in this column are blank. This makes any meaningful analysis of this demographic category impossible.

Conclusion

After conducting a great deal of analysis on voter turnout in the 2016 general election across the state of Georgia, most of my findings were a bit surprising, to say the least.

Both of my hypotheses were shown to most likely be incorrect. While Liberty County and Long County both faced lower turnout rates than did the rest of the state, it is difficult to confidently attribute this to the aftermath of the storm since most of the other counties in the subset experienced turnout rates closer to the state average. Also, because I strictly utilized observational data, I obviously cannot make any conclusions about causation regardless. As such, there might be a multitude of confounding variables that contributed to lower turnout in these counties compared to the remainder of the state.

Furthermore, early voting rates in this subsetted region, aside from Liberty County, seem to have been much lower than the state average. This also suggests that there are likely outside factors that attributed to Liberty County and Long County having lower turnout since early voting typically surges in areas recently affected by natural disasters. Further study into whether this supposed phenomenon actually occurs would be helpful to clear up this conundrum.

That being said, I am not going to just shrug my shoulders and give up just yet. Deeper investigation into some available data could help shed light on some interesting factoids that could guide future research into the matter.

Reverting back to the Census data utilized previously, I notice that Liberty County is relatively poorer than the rest of the state is. Median household income between 2012 and 2016 in Liberty County was $42,484, which seems significantly lower than the state’s median household income of $51,037 during that time. Liberty County’s poverty rate is also a bit high at 15.3%, compared to Georgia’s rate of 14.9%.

Long County is a bit closer to the state average in terms of median household income at $50,848, but its poverty rate is substantially higher than the rest of the state at 18.6%.

If the hurricane did in fact affect voter turnout in these two counties, it is worth noting these numbers since it is reasonable to infer that their respective Supervisors of Elections might not have had sufficient resources to tackle the various obstacles that potentially came with Hurricane Matthew. Thus, perhaps early voting rates were lower in these counties due to a lack of adequate infrastructure to accommodate demand for it.

Lastly, if other areas affected by different natural disasters have experienced upticks in early voting, it might be wise for Supervisors in the counties most affected by Hurricane Matthew to invest more into early voting for future elections as a precautionary measure.

I hope to continue studying the effects of natural disasters on voter turnout in the future. As Hurricane Michael recently swept through the Florida Panhandle, it would be wise to now examine the potential effects that storm had on turnout in that specific area for the 2018 general election. It is possible that might be a better case study to analyze, but nonetheless such research would certainly be a valuable addition to the current body of literature.