Turner Robbins

Jackson Oliva

Wages and Wealth Versus Political Orientation

Hypothesis: When looking at the median income level for counties in the United States, we

became curious if the general level of wealth/income mattered when it came towards election

results. If a county turned out to be mostly high or low income, would that county tend to lean

towards a particular candidate or political party? By looking at the 2012 Presidential election

results and median income levels at the time, we predict that there will be a certain level of

correlation between these two statistics. We also predict that if a county has a low median

income, then they will lean more towards the democratic party. If a county has a high median

income, they will lean more towards the republican party. From this prediction, we also

stemmed that higher unemployment rates would correlate positively with democratic orientation

and that lower unemployment rates would correlate positively with republican orientation. WHY

IS THIS IMPORTANT?

Methodology: Using information gathered from the United States Census, we calculated the

correlation coefficient between median income in a county, and democrat vs. republican votes in

a county. We also calculated the correlation between percent of unemployment and democrat vs.

republican votes. For this experiment we observed the toss-up states of the 2012 election, so that

region-bias was less of an influence on the results. The toss-up states were: Wisconsin, Virginia,

Ohio, New Hampshire, Iowa, Florida, and Colorado.

1. Gather median income data in 2012 for each county of a state (this data was unavailable

so we used the median income for 2013).

2. Gather % unemployment (# unemployed / # workforce) in 2012 for each county of a

state.

3. Gather % democrat and republican voters for the 2012 election per county (# votes for

party/ # votes total).

4. Calculate correlation statistics of (Democrat vs. Income), (Republican vs. Income),

(Democrat vs. % Unemployment), and (Republican vs. % Unemployment). This can

easily be calculated using correlation formulas in Excel.

5. Repeat this process for each toss-up state in the 2012 election.

6. Average out the correlation statistics and analyze the data gathered.

Assumptions: In the data we observed for the median income, we are assuming that this data

only includes people who are registered workers, and does not count unemployed in the total

median count. In addition we assume that the median income is granularly representative of the

mean income. This experiment also assumes that the unemployed will not have a strong enough

influence to change the general correlation of the data gathered. Although we hypothesize that

there will be a strong correlation between the unemployment rate, income levels, and voting

behaviors this does not mean that there is a causation between them. There may be other

influences or effects at play when relating wages and employment to voting behavior and it

would be implausible to try and include them all.

Data: Although our hypothesis estimated that there would be a strong positive correlation (r

> .5) between income and the number of democrats in an area, this did not turn out to be entirely

true. As the table below shows, the average correlation coefficient did follow our hypothesis,

with people tending to me more conservative the wealthier they are, but it did not follow as

strongly as we assumed. A few outliers, namely Virginia and Colorado, had individual results

that were opposed to our hypothesis. While we cannot definitively say why these two states were

the opposite of what we expected we can make an educated guess that because they have voted

liberally in the past two elections (2012 and 2008) their citizens are generally more liberal

regardless of their income or employment status, thus skewing the correlation coefficient for

those states. In addition New Hampshire and Iowa showed the strongest correlation coefficients

for unemployment, with NH having more votes for Romney as unemployment increased, and IA

trending the exact opposite way. In addition, New Hampshire had the least amount of counties of

all the states we looked at, so it is entirely possible that the results were skewed because of a

deficiency in data quantity.

Conclusion: This experiment’s results are important as they show that, while far from being

100% consistent, that more information can be obtained about a person from simple publically

obtained statistics. In this case, one can make a more well-informed guess on someone’s

political orientation by knowing their income level or whether or not they are employed. In

another experiment, MIT students browsed through multiple people’s friend lists on social media

to determine the people’s sexual orientations based off the sexual orientation of their friends.

While this experiment seemingly covers a completely different topic, some of the principles are

the same. By looking at one set of information (the friends list), these MIT students were able to

more easily infer a completely different piece of information (a person’s sexual preference). In

the same way, we were able to take one set of information to infer something seemingly

unrelated. While our correlations weren’t perfect as a few outlying cases lacked the numbers to

show a consistent correlation, there was a decent amount of consistency in the data. While this

data might not be enough to prove our hypothesis, it does show that there may indeed be a

correlation which further investigation will more strongly prove.

Afterthoughts: By using our data, someone could make a claim stating that, because the results

of the correlation statistics were not entirely consistent across the states and because the

correlation results were not strong as a whole, there is no relationship between

income/unemployment levels and political affiliation. However, the results and data by itself is

consistent, and anybody who desired to repeat the experiment for themselves would get the same

results, and the data was gathered using the public data from the United States Census. The use

of more granular data, for example finding the voting results and median/mean income for a

neighborhood, may yield stronger results.

Reference Databases: http://www.census.gov/

http://www.theguardian.com/news/datablog/2012/nov/07/us-2012-election-county-results-

download

Other References: http://www.270towin.com/historical-presidential-elections/

http://elections.nytimes.com/2012/results/president/big-board

Appendix: In this section, we posted a number of the detailed graphs that detail the relationships

between median income/%unemployment and political affinity.

Wisconsin:

Virginia:

Ohio:

New Hampshire:

Iowa:

Florida:

Colorado: