By Diana Elliott and Emma Kalish
In an earlier version, we transposed the national voting results for Trump and Clinton. The feature has been revised to correct the error.
The outcome of the 2016 presidential election caught many by surprise, considering the projections in the lead-up to November 8. In the wake of the election, media outlets and pundits have made assertions about which voters drove the election and what motivated their choices.
One of the primary narratives to emerge has been that financial insecurity drove the election results. Was this election about the frustrations of working-class white voters and their increasingly precarious economic status? Or is the explanation grounded in votersí demographic characteristics, including race and ethnicity, age, and educational attainment?
Data may shed some light on the answers. We looked at county-by-county election results and votersí financial and demographic characteristics and found that financial insecurity-as measured by credit scores-did not drive voting preferences. The perception of financial insecurity, however, may have been quite important. Before the election, the most favorable ratings of Trump were held by those with the most anxiety about their finances, regardless of income or local economic conditions.
Our analysis considered not only income, but also other components of financial security, including familiesí access to credit and their wealth-building potential. Financial security is also intertwined with familiesí economic context, like job opportunities and access to homeownership. All these factors paint a more complex picture of Americansí financial lives than has been portrayed in the postelection narrative.
Among the 55 counties (or county equivalents) with residents with the highest average credit scores (720 and above), Hillary Clinton won just four of them: Falls Church, Virginia (with an average credit score of 729); San Juan County, Washington (722); Cook County, Minnesota (721); and Washington County, Minnesota (720). High credit scores are associated with long, successful credit histories and bills paid on time and are implicit markers of financial security and stability over a lifetime.
High credit scores are also more often held by white consumers. The most diverse areas in this group are Falls Church, Virginia (73 percent of the population is white), and Morris County, New Jersey (72 percent). All but three (Douglas County, Colorado; Falls Church; and Morris County) have median incomes well under $100,000 a year.
Unemployment rates are under 5 percent in each of these areas, and homeownership rates range from 59 to 90 percent. Residents here are not necessarily high income, but they do work, and many are white, working-class people. They are financially secure homeowners who generally pay their bills on time and have access to reasonably priced credit.
Meanwhile, Clinton won all 11 counties that had the lowest average credit scores (below 600, in the subprime range). In 10 of those counties, Clinton won more than 60 percent of the votes. In Tensas Parish, Louisiana, which has an average credit score of 598, Clinton won with 52.3 percent of the votes. Tensas Parish is majority black, but also has the highest percentage of white residents (42 percent) in these 11 counties. The other counties with average subprime credit have high percentages of black or Native American residents, and the median household income is below $32,000 a year.
Unemployment rates in these 11 counties range from 7.4 to 14.9 percent, and homeownership rates range from 44 to 75 percent. These residents are more likely to be financially insecure with diminished access to credit and fewer job prospects.
Residents in counties with the best credit scores tend to be white, higher-income, middle-class homeowners and overwhelmingly Donald Trump voters. Meanwhile, residents in counties with the lowest credit scores tend to be black or Native American, have low median household income and unstable employment, are less likely to own their homes, and overwhelmingly are Clinton voters.
This seems to indicate that financially secure voters were more likely to cast their ballot for Trump. But letís take it a step further. We can focus on the effects of financial insecurity by holding countiesí other economic and demographic factors constant (median income, unemployment rate, percentage white residents, percentage with a bachelorís degree or higher, and homeownership rate). Doing that shows that financial security is no longer a significant predictor of whether Trump won a particular county.
Why? Because higher credit scores are located in counties with higher percentages of white residents, and race was an important predictor in this election: a 10 percentage point increase in a countyís white residents is associated with an 8 percentage point increase in voting for Trump. Education was also important. A 10 percentage point increase in residents with a bachelorís degree or higher is associated with a 20 percentage point increase in not voting for Trump.
These data suggest that, unlike the educational attainment and race of a countyís voters, financial insecurity was not a strong driving force in this election. Perhaps more important, the data suggest that underlying and persistent racial patterns of financial insecurity prevail in the United States-a fact that all politicians, regardless of party preferences, would be wise to address.
About the data
Voting results are by county and county equivalents. Credit scores are from VantageScoreģ. White and black voters are non-Hispanic. In the Kusilvak Census Area in Alaska, the average credit score is 579, and 70 percent of the population has a subprime credit score. But Alaska is the only state that does not report electoral data along county lines, so voting data for all Alaska counties are aggregated and reported for the whole state. The Kusilvak Census Area was excluded from this tally of low-credit-score counties because it is unclear for which candidate the electorate voted for president.
View the full methodology.
This interactive was funded in part by the Ford Foundation under the Urban Instituteís Opportunity and Ownership Initiative. We are grateful to them and to all our funders, who make it possible for Urban to advance its mission. The views expressed are those of the authors and should not be attributed to the Urban Institute, its trustees, or its funders. Funders do not determine research findings or the insights and recommendations of Urban experts.
Special thanks to Signe-Mary McKernan and Caroline Ratcliffe of the Urban Institute, Thom File of the US Census Bureau, John Sides of the George Washington University, and 5W Infographics. Laura Greenback, David Hinson, Serena Lei, Sara Rosenthal, and Jerry Ta of the Urban Institute also contributed to this project.
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