Life expectancy and political affiliation

The 2020 presidential election was marked by a number of significant trends and developments, including record voter turnout and a deeply divided electorate. As the votes continue to be counted and analyzed, one notable pattern that has emerged is the relationship between life expectancy and political party affiliation. According to data from the Wikipedia, states with higher life expectancy rates were more likely to vote for Democratic candidate Joe Biden, while those with lower life expectancy rates were more likely to vote for Republican candidate Donald Trump. Here’s a graph showing the distribution:

The state with the highest life expectancy (82), Hawaii went for Biden with 63% of the vote. In stark contract, in West Virginia only 29% of voters picked Biden and the life expectancy in that state was not even 75.

Correlation isn’t causation of course (nobody is saying that voting for Biden increases your life expectancy). Life expectancy is influenced by a number of factors including access to health care, income per head and income equality. In recent years states that tend to vote Democratic have on average higher scores on these measures. But it wasn’t always so. Here’s the same graph for the 1960 presidential election, showing a correlation between the vote for Nixon and life expectancy:

The pattern is maybe less pronounced, but it is there. Partly this is because the Deep South just flipped from Democrat to Republican. This was already under way in 1960 when a total of 15 electors, comprising eight from Mississippi, six from Alabama, and one from Oklahoma, declined to cast their votes for either Kennedy or Nixon in the 1960 presidential election. Instead, they opted to support Senator Harry F. Byrd of Virginia, a conservative Democrat, despite not being on the ticket.

But the relative life expectancy of states has also changed. New York and New Jersey are somewhere in the middle in the 1960 graph, but towards the top in 2020, while Nebraska, Kansas and Iowa fell in the ranking from the 1960s to the present.

This analysis is of course done in Neptyne, the programmable spreadsheet. You can play with the graphs, the data and the code right here:

https://neptyne.com/neptyne/bmb3q2y9mg

Let me know if you find some other interesting patterns!