Ahead of the presidential election, publications like the New York Times, the Huffington Post and 538 gave Hillary Clinton somewhere between a 65%-99% chance of winning the election. You know what happened next. As November 8 ticked into November 9 and it became less and less likely that Clinton would win, people began wondering how forecasters got it so wrong. Now that a month has gone by and pollsters have had a chance to look into the data, some partial explanations have emerged: pre-election polls underrepresented Trump voters, two candidates with historically low approval ratings caused voters to stay home and white working class voters in the Rust Belt came out strong for Trump. As personal finance is SmartAsset's specialty, we decided to look at voter statistics through that lens.

Study Specifics

We examined data on homeownership rates, median home values, credit card debt, costs of living, the percentage of the population with health insurance and the percent change of median income from 2010-2014 in 2,127 counties. We compared that data to the percentage of people in each county who voted for Donald Trump, the winning candidate. By doing this we can see which personal finance metrics correlated with voting for Trump.

Even in cases where the charts show a strong correlation readers should not assume the relationship is causal. There are a variety of reasons people pick their preferred candidate, some economic, some cultural and so on, making it hard to isolate any one cause.

Key Findings

• Rural vs. city divide – Much has been made of the fact that rural voters helped clinch the White House for President-elect Donald Trump. Hillary Clinton on the other hand won most big cities. Our data largely backs that up. According to our data, Clinton voters tend to live in counties with high costs of living and lower homeownership rates: both factors which are associated with large metropolitan areas. Our data also shows that Trump supporters tend to live in areas with lower costs of living and lower home values – the hallmarks of many rural areas.
• Income change was a toss-up – One interesting finding that emerged from our data is that Trump won the vast majority of counties that saw big income changes in both directions. Of the 50 counties which saw the biggest income decreases from 2010 to 2014, Trump won a convincing 46 counties. And of the 50 counties which saw the biggest income gains from 2010 to 2014, the president-elect won 44 counties. However, perhaps the more interesting discovery to emerge from our study is that income change did not play as big a role in the election as some may expect. That is (as the flat trend line on the graph in the corresponding section below shows), there was not a particularly strong relationship between how counties voted and the median income changes in the area.

Data

In order to create these charts, we gathered data on six metrics and compared them to the election results in 2,127 counties. Election results data comes from the American Press Bureau. The data does not include Alaskan counties, nor does it include counties for which we did not have data.

• Homeownership rates by county. Data comes from the U.S. Census Bureau's 2014 5-Year American Community survey.
• Median home value by county. Data comes from the U.S. Census Bureau's 2014 5-Year American Community Survey.
• Cost of living by county. This is the estimated annual cost of living for one adult. Data comes from the MIT Living Wage database.
• Percentage of a county's population who have health insurance. Data comes from the U.S. Census Bureau's 2014 5-Year American Community Survey.
• Average credit card debt by county. Data comes from SmartAsset estimates using Federal Reserve data.
• Median income change from 2010-2014 by county. This is the difference between the median income in a county in 2010 and in 2014. Data comes from the U.S. Census Bureau's 2014 5-Year American Community Survey and the U.S. Census Bureau's 2010 5-Year American Community Survey.