Correlations between Voting Patterns and County Health Rankings

Overview

This exploratory study examines how county-level election outcomes are associated with social factors such as access to healthcare (e.g. insurance coverage and provider availability) and socioeconomic conditions like income and poverty levels. The analysis begins with data from the MEDSL Dataverse, specifically the "County Presidential Election Returns 2000-2020" dataset maintained by the MIT Election Data + Science Lab. Additional data is sourced from the County Health Rankings & Roadmaps directory, complemented by the U.S. Census Bureau's SAHIE (Small Area Health Insurance Estimates) dataset spanning 2008–2022. By integrating these datasets, the study aims to uncover new insights into past election outcomes by analyzing a range of metrics at the county level.

To answer our research question about the relationship between voting patterns and county health rankings, I calculated the correlation between all measures of interest: Percentage of Healthcare Providers, Uninsured Percentage, Median Household Income, and Political Affiliation. I concluded that most of the variables are not correlated to one another since most of the values are negative. However, there seems to be a slight positive correlation between Median Household Income and Percentage of Healthcare Providers as well as Median Household Income and Political Affiliation. Percentage of Healthcare Providers is also slightly correlated with Political Affiliation.