Who are the 47% and Who Did they Really Vote For?

I know a lot of people who voted for President Obama (and about as many and maybe more who voted for Mitt Romney).  None of the people who voted for the President fit the famous “47%” profile of individuals dependent on government for support.  In fact, very much the opposite was the case.  Nevertheless, the notion that a dependent population was largely responsible for the President’s re-election seems popular in some circles.  My small circle of acquaintances is not a  valid sample from which to accept or reject the dependency theory of  the election so here is one small step toward empirical verification or rejection.

I chose ten states from various regions of the country (NH,MA,NY,IN,KS,GA,FL,TX,AZ,OR), half of whom were won by President Obama and half by Mitt Romney.   I compiled a county-level dataset that includes the percentage of votes won by each candidate, the percentage of the population age 25 and older in the county that has a bachelor’s degree or higher, and the percentage of the population in the county that is white and non-Hispanic.   For my dependency measure I used the percentage of total personal income in the county that comes from government transfer payments.  The largest government transfer payments are for Social Security, Medicare and Medicaid (see chart below).  Of those, only Medicaid is for low-income individuals (and thus more closely fitting the profile of dependency) and income support payments like disability, supplemental income, food stamps and other (see chart below).

transfer payments

The ten states are not random and perhaps not a valid sample and there are many more demographic variables I could have included but this is all I could accommodate in the span of a Boston Celtics game and a couple of glasses of wine.  The ten states represent 814 counties, or about 26% of all counties in the U.S.  Using a simple regression model that analyzes the impact of the educational, race, and dependency variables on the percentage of the vote in each county received by the President, results were significant but still only explain about 25% of the variation in the percentage of the vote received by the President.  A larger percentage of income in a county  from government transfer payments is, in fact,  positively related to higher percentage of the vote for the President (although the simple correlation is small), and a higher percentage of the population that is white is negatively related to the vote received by the President (no surprise that we are a long ways from being color blind).  Its no great epiphany that users and supporters of government assistance  would be more likely to vote for a Democrat or that white voters might be less likely to vote for the President.  What is most interesting, however, is that the strongest relationship is a positive one between the percentage of persons age 25 and above in a county who have at least a bachelor’s degree, and the percentage of the vote received by the President.  Republicans may be right about not being able to win as many individuals who rely on government assistance as will Democrats but over the next few decades the percentage of the population that will be receiving the largest share of government benefits (Social Security and Medicare) is going to skyrocket and the percentage of the population that has a bachelor’s degree or higher is likely to increase as well.

I guess you can dismiss election results when they appear to be an aberration driven by the “great unwashed” who depend on government benefits, but what do you say if  the results were more influenced by the voting behavior of the most educated?

Anyone interested in the limited dataset I have, feel free to contact me.  I’d love to include all 50 states and many more demographic and economic variable but I doubt I will ever get to that.  For the truly nerdy who might want the stats from the regression models, you are welcome to those as well.

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Explore posts in the same categories: Demographics, Dependency, Election, Politics

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