In trying to generate models to predict/fit this year's election cycle, I wanted to eliminate cultural factors to focus on economic factors. Doing so meant that I had find a legitimate way to control for regional patterns of cultural difference--for example, differences between the South, Midwest, Northeast, and West, presuming such cultural differences exist. Various demographic maps lend visual credibility to the existence of regional differences, although rigorously disentangling economic from cultural factors is challenging. Prior literature indicates various factors associated with "Southern Culture," including several attempts to create a "Southern Culture Index."
Below are seven maps that show regional differences based on some of the more common factors that are mentioned in the academic literature that are associated with differences between the South and other parts of the country. Total, I found 20 variables that I included in an exploratory factor analysis, ranging from voting patterns (Republican to the South, Democratic to the North), occupational differences (manufacturing to the South, science and finance to the North), and income differences (higher to the North, and along the coasts, lower to the South). However, these variables tended to produce low statistical results as factors, so I did not create maps for them. The maps below represent the variables that produced the strongest results in terms of creating a "Southern Culture" Index. In addition, I produce four more maps of the best index models that were generated using various combinations of these 20 variables.
To summarize the data, the South has a number of challenges compared to the North, Midwest, and West. For example, in addition to lower income mentioned above (although the cost of living is lower, helping to compensate for income disparities), there are lower rates of college graduation and union membership. The South has significantly higher rates of firearm deaths, teen births, and death rates from various causes. Some researchers have identified a relationship between rates of violence in a region and large rates of Scotch-Irish ancestry in that region, both of which are found in the South. Similarly, the South has a long history of human rights abuses in terms of slavery, a history which continues to shape the South. These factors all contributed to the strongest indices. However, the final models did not use Scotch-Irish ancestry, income, or cost of living. The best models used combinations of the following six variables: death rates all causes (CDC, 2010-2015), firearm death rates (CDC, 2010-2014), union membership (BLS, 2015), teen birth rates 15-19 years (CDC, 2015), white Evangelicals (PRRI, 2015), and slave ownership as a percent of the population (1860).
I generated the following maps using the opensource software QGIS, and I used the opensource software R for the factor analysis to generate the indices. The package psych has several nice factor analysis features. The first seven maps show the individual factors, while the final four maps show the best index models (combinations of factors), along with technical information about the strength of the models. As can be seen, all four models produced results that are very similar. Red states are the most "South-like," blue states are the least "South-like," and purple states have mid-range "South-like" characteristics, according to each of the four models.