We investigate the 2020 Presidential Election by using the county as the unit of analysis (n= 3,112) and examine the variance in the county percentage of votes cast for Biden and Trump as dependent variables. Our seven independent variables are conceptually related to racial diversity, educational attainment, wellbeing (includes economic, health, and quality-of-life indicators), and rural/urban classification. Using OLS regression, the model explains about 73% (R squared) of the variance in county vote outcome for both the Biden and Trump models. The regression results show that the counties that Trump won tended to be populated with people who are more white, less educated, score lower on a composite wellbeing measure and are located in a more rural setting compared to the counties that Biden won. However, the urban/rural effect is so small that we question whether there is a real effect after controlling for the effects of other variables.