Rural Voters and the Rural Vote in 2016
David L. Brown
Much has been written about the impact of rural voters, and the rural vote, on the 2016 presidential election. According to The Pew Research Center, Trump beat Clinton in rural areas by 62% to 34%, and by 50% to 45% in the suburbs. In contrast, Clinton bested Trump by 59% to 35% in urban areas. Moreover, Trump’s share of votes grew in direct relation with the degree of urbanization from about 40% in metropolitan areas with 1 million or more people to 58% in smaller metropolitan areas to fully 70% in totally rural counties (Kurtzleben 2016). Research shows that the share of rural votes cast for Democratic presidential candidates since 2000 was highest in 2008, and declined in both 2012 and especially in 2016. Moreover, this pattern characterizes nonmetropolitan counties with and without small and medium sized cities, and to a lesser degree both core and suburban parts of smaller metropolitan areas (See Figure 12). Only large metropolitan core areas avoided the Democratic drop off (Scala and Johnson 2017f).
As is true of most areas of social and political life, basic data on political preferences and voting behavior mask important differences that are revealed when one digs below the surface. Accordingly, our overall contention is that the rural vote definitely contributed to Trump’s unexpected victory in 2016, but perhaps not as much as would be suggested by these overall differences or by media reports of the “rural revolt”. In this essay we identify predictable voting outcomes based on a long tradition of rural demographic research3, and on research examining rural attitudes and preferences toward social and economic issues. We also focus on political outcomes that were much less predictable, and that reflect the culmination of long-term processes of adverse economic transformation, perceived disrespect and arrogance by cultural and political elites, and rural alienation.
What Was Predictable in 2016
Republicans tend to win rural areas, and that was no different in the 2016 election. Only 7.4% of nonmetropolitan counties were Trump swing counties (went for Obama in 2012 and Trump in 2016), compared to 21% of metropolitan counties. The rural preference for Republicans, including Trump, is consistent with data on rural-urban attitudes toward major economic and social issues debated during the election. Analysis by the Pew Research Center indicates that rural persons feel less economically secure and more threatened than their urban and suburban counterparts when it comes to the availability of jobs in one’s own area, the perceived adverse impacts of immigration on economic opportunities, and children’s lifetime economic prospects (Morin 2016). In addition, research shows that rural persons are more conservative on a set of social issues including abortion and same sex relations (Dillon & Savage 2006), although this research showed that rural residence per se is not the major determinant of social conservatism. Rather it is the high concentration of evangelical Christians living in such areas. More recent research by Scala and Johnson (2017f) shows a regular gradient of social conservatism as one moves from the cores of large metropolitan areas to the most rural counties. In addition to attitudes toward abortion and same sex relations, this study also includes data on attitudes toward affirmative action, legal status for immigrants, gun control, and belief in climate change. In all instances, the regular urban-rural gradient of conservatism described above is apparent.
Research shows that there are distinct regional differences in rural conservatism with the South being substantially more conservative on social issues. Since over four in ten rural persons live in the South, the concentration of evangelical conservatives in this region had a strong impact on Republican victories in 2016. In addition, race and region interacted in an interesting way to influence Trump’s advantage. Fifty five percent of the nation’s Black population lives in the South, and virtually all of the nation’s rural Blacks live in this region. In five deep Southern states: Alabama, Georgia, Mississippi, South Carolina and Louisiana, Blacks comprise over a quarter of the population, and a higher share of rural populations. Hence, one would expect the strong Democratic preference among Southern Black voters, including those living in rural areas, to pull voting results toward the Democratic Party in these states. In fact, research by Scala and Johnson (2017f) showed that high percent African American is strongly associated with voting for the Democratic presidential candidate in both 2012 and 2016. They (2017f: 20) characterize these high minority percentage populations as “enclaves of Democratic support in the largely Republican South….” In other words, while high minority percentage may translate into Democratic victories in particular local elections in the South, the concentration of Black voters in particular districts as a result of gerrymandering has systematically diminished Black political power at the state level. Voter identification laws and other constraints on voting further dilute Black political power. Hence, in 2016, the “solid South” was solid for Trump. Of course, the solid Republican South has not always been a given. In fact, the South was solidly Democratic for 100 years between the Civil War and the passage of the Voting Rights Act of 1965. Outside of the South, rural America is predominately white. Accordingly, Black rural voters had little impact on the 2016 election in these regions.
What Was Not As Predictable in 2016 (but should have been?)
When examining the overall impact of the rural vote on national elections, it is important to note that rural people account for only 14% of the nation’s total population and a similar share of votes cast in 2016 (Leip 2017). Hence, while Trump’s 62% – 34% rural advantage contributed to his victory, it was certainly not sufficient to swing the election. Because US presidential elections are based on the results of the Electoral College, both the rural and small city advantage in three key states (Michigan, Pennsylvania, and Wisconsin) enabled Trump to garner a set of electors that made the critical difference (only 107,000 votes out of more than 13 million cast in those states). In Michigan, for example, Trump’s rural and small city advantage was sufficient to counterbalance Clinton’s over 200,000 vote lead in major metropolitan areas. Trump won Michigan by just over 10,000 votes; he received over 48,000 more votes than Clinton in Macomb County alone – a county that Obama solidly won in both 2008 and 2012. It is not a stretch to say that Macomb County won Michigan for Trump. Macomb County is not rural. In fact, it is the third most populous county in the state and is part of the Detroit-Warren-Dearborn metropolitan area. Similar scenarios unfolded in Wisconsin and Pennsylvania. In these states, it was not only rural, but also small/medium sized city voters who nullified significant Clinton victories in the largest cities. Trump won Pennsylvania by just over 44,000 votes out of 6.1 million votes cast; a margin of only 0.7%. Nearly 60% (over 26,000 votes) of Trump’s victory margin in Pennsylvania came from metropolitan Luzerne County – home to Wilkes-Barre. This margin is remarkable because Luzerne County is a traditionally democratic county that Obama carried twice, and has not gone for a Republican candidate since 1988. Luzerne County is a strong example of the “type” of county where Trump performed much more strongly than expected. The number of manufacturing jobs there declined from over 42,000 in 1980 to fewer than 19,000 today, and the types of manufacturing jobs there pay less than jobs that were available 40 years ago. In Luzerne County, the poverty rate has been increasing; median household income has basically remained stagnant; over a quarter of prime-age (25-59) residents are either unemployed or not in the labor force at all; drug-overdose rates have tripled over the past 15 years; and suicides have more than doubled. Similar stories unfold across the Industrial Midwest, Appalachia, and even in parts of New England (Monnat 2017).
In many of the rural areas and small cities where Trump performed better than expected (or Clinton performed worse than expected), economic distress has been building and social conditions have been breaking down for decades. These are not necessarily the poorest places in America, but they are places that are generally worse off today than they were a generation or two ago. In these places, there are now far fewer of the manufacturing and natural resource industry jobs that once provided solid livable wages and benefits to those without a college degree. It is important to acknowledge that deindustrialization is not a new rural phenomenon. In fact, manufacturing began to decline as a share of all rural employment in the 1970s (Fuguitt, Brown and Beale 1989). In Pennsylvania, for example, the number of workers employed in manufacturing has declined since 1980 in all but one county. Since 1990, the working-age adult (age 18-64) poverty rate has increased in 96% of Michigan counties, 90% of Pennsylvania counties, and 86% of Wisconsin counties.
It is important to understand that, in the US, work has historically been about more than a paycheck. Work is a symbol of status in this country. American identities are wrapped up in our jobs. But the U.S. working-class (people without a college degree, people who work in blue-collar jobs) regularly receive the message that their work is not important and that they are irrelevant and disposable. That message is delivered through stagnant wages, declining health and retirement benefits, government safety-net programs for which they do not qualify but for which they pay taxes, and the seemingly ubiquitous message (mostly from Democrats) that success means graduating from college. One interpretation is that Trump capitalized on and exploited these frustrations and anxieties in Pennsylvania, Michigan, Wisconsin and elsewhere in the rust belt. His anti-free trade message for example, likely resonated with some voters who saw manufacturing plants shut down and saw low-wage service jobs replace the better paying jobs previously available to their parents and grandparents. Growing racial and ethnic diversity in these same places contributed to the perception (however inaccurate) that immigration was at least partly to blame for their woes. Another interpretation is that apathy or ambivalence about Hillary Clinton in these places, paired with Democrats’ arrogance in the impermeable “big blue wall”, may have led some potential voters who would have supported another Democratic candidate who emphasized working-class issues to not vote at all. When you’re driving by old-shuttered factories with boarded up windows, watching nightly news reports about drug overdoses, and seeing more of your neighbors sign up for disability instead of working, the message that America is great already simply does not jive with your own reality.
So what makes these downwardly-mobile places any more likely to swing to Trump than persistently poor and disadvantaged places? After all, persistently disadvantaged and poor communities also suffer from frustration, distress, and anxiety, and a sense of being abandoned by political elites. For one thing, racial composition certainly played a role. Thanks to both his overtly and implicitly racist messages, Trump was unlikely to garner any significant support from Hispanic or Black voters regardless of long-term economic distress in many majority-Black and majority-Hispanic areas. Reference group theory, developed 60 years ago offers an additional explanation. In 2016, Andrew Cherlin wrote an op-ed for the New York Times, applying “reference group theory” to rising mortality rates among non-Hispanic whites. Perhaps the key to understanding rising white mortality rates in the context of declining mortality for other groups, and likewise the key to understanding the swing to Trump in many downwardly-mobile places, is considering the standards by which a group (or place) compares itself. It should come as no surprise that Trump performed best in counties with the highest drug, alcohol, and suicide mortality rates, especially in Appalachia, the Industrial Midwest, and New England (Monnat 2017). The tie that binds high rates of drug, alcohol, and suicide mortality and Trump performance is collective (place-level) downward mobility. Cherlin makes the case that working-class whites are comparing themselves to a prior generation that had more opportunities, whereas Blacks and Hispanics are comparing themselves to a generation that had fewer opportunities. Likewise, working-class residents of once robust manufacturing and natural resource extraction towns see fewer good employment opportunities for people without a college degree and out-migration of their best and brightest. Thanks to 40+ years of deindustrialization, automation, globalization, and neoliberal policy regimes (Brown and Swanson 2003; Smith and Tickamyer 2011), the residents of many former strong manufacturing towns and the rural areas surrounding them feel like they’re doing much worse than previous generations in the same places. The Great Recession and its spatially uneven recovery only exacerbated these long-term stressors (Bailey et al. 2014). As a result, voters in these places were primed to embrace messages of change versus the status quo, and to switch their political allegiance from the Democratic Party to Donald Trump.
The fact that many rural and small city Americans voted for a Republican presidential candidate in 2016 is not surprising. In the South, the Plains, and parts of the Mountain West, they have done so for decades. However, the strong rural and small city Republican vote in Pennsylvania, Michigan, Wisconsin and elsewhere in the Industrial Midwest and Appalachia signaled an unexpected switch that had a profound effect on the election. To be sure, no single factor can explain the switch from Democratic to Republican majority voting in these areas in 2016, but declining community-level well-being (as defined by several health and economic indicators) in small cities and rural areas appears to have played a major role. Trump’s populist message was attractive to many long-term Democratic voters in these places who felt abandoned by a Democratic Party (and candidate) that failed to articulate a strong pro-working class message, and who evidently believed it did not have to work very hard to earn votes from behind the “big blue wall.”
While data reviewed here and elsewhere show that Trump’s election victory was not primarily due to a new “rural revolt,” the media’s current emphasis on the political impact of rural people and places offers an opportunity for sociologists, especially those with a spatial orientation, to consider the multiple important intersections and interdependencies between rural and urban areas. The rural-urban binary is an outdated concept that never really supported an accurate analysis of the spatial organization of American society. Hence, examining rural vs. urban voting patterns is not a particularly useful way to understand the nation’s changing politics. In the future, we recommend that such analysis focus on the urban-rural interface. Rather than a boundary separating rural from urban, the interface is a space of intense social, economic and environmental relationships between urban, suburban and rural communities (Lichter and Brown 2011; Brown and Shucksmith 2017f). An increasing share of the nation’s population and economic [and political] activity is located in the interface, hence, the interrelationships linking urban, suburban and rural communities will contribute to political outcomes.
For example, consider the future of Congressional representation. Will the results of the 2020 Census contribute to Congressional redistricting that reflects how Americans really live in our increasingly integrated society, or will they be used to continue to pack democratic voters into urban districts, while continuing to cede rural districts to the Republicans? This, of course, is a very high stakes game because business as usual imparts a rural bias on the Electoral College. The Electoral College’s rural bias was intentionally built into the nation’s political system by Jefferson and Madison who feared the concentration of power in urban areas, and preferred an agrarian development trajectory (Badger, 2016). Since over 90% of the US population lived in rural areas in the immediate post-revolutionary period, the rural bias might have been more legitimate at that time. Today, however, with 86% of the US population living in urban areas, the rural bias is questionable at best. As Emily Badger (2016:2) has observed, “The Electoral College is just one example of how an increasingly urban country has inherited the political structures of a rural past.” Since governors play a central role in post-census Congressional redistricting, future gubernatorial elections will have the knock-on effect of influencing the Electoral College, and hence, the 2020 and 2024 presidential elections.
In conclusion, where one lives matters to American politics, but rural, suburban or urban residence per se is not necessarily the causal factor. Rather, the concentration of various types of persons in different types of places, and the relationships binding these urban, rural and suburban places together into integrated social and economic units is a key to understanding election results. Future research should focus on the interaction of residence and various population attributes such as age, race, gender, class, immigration status, occupation and industry, etc. Moreover, a simple urban vs. suburban vs. rural comparative framework is increasingly unlikely to yield reliable explanations of changes in political processes. Rather, it is the interrelationships among places along the urban-rural continuum in combination with the characteristics of persons living in such places that will reveal emerging trends and changes in the political behavior in America’s diverse communities.
1International Professor of Development Sociology, emeritus, Cornell University and Assistant Professor of Rural Sociology, Demography, and Sociology, Penn State University, respectively.
2Unless cited otherwise, election outcomes described in this report are based on the authors’ analyses of county-level voting data from Dave Leip’s Atlas of U.S. Presidential Elections. http://uselectionatlas.org/.
3The USDA has supported rural demographic research for decades through its “multi-state research committees.” Both authors are active participants in multi-state project W-3001, “The Great Recession, Its Aftermath and Patterns of Rural and Small Town Demographic Change.”
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