Rural Despair and Decline: How Trump Won Michigan in 2016
Introduction When Donald Trump won the Electoral College vote in 2016, he shocked the entire world. In part, people believed he could never win because he would never crack the Democrats’ famous “Blue Wall”: the combination of Michigan, Wisconsin, and Pennsylvania. But he did, winning counties that John McCain and Mitt Romney could not. Political pundits asked themselves: How did this entitled, brash, inexperienced New York millionaire appeal to rural voters? What seems like thousands of think-pieces have been written on the issue, each suggesting that Trump won the election because of Russian interference, deeply rooted misogyny, racial backlash to Obama’s presidency, the rise of social media, and a myriad of other factors. However, some scholars have suggested that Trump’s unexpected triumph could be traced to another factor: pain and discontentment across rural America. Over the past several decades, America’s working class has seen its way of life disappear. With a loss of jobs due to innovative technology, outsourcing of manufacturing jobs, and mass migration out of Rust Belt states, residents of Illinois, Indiana, Michigan, Ohio, Pennsylvania, West Virginia and Wisconsin—areas that used to be vibrant parts of America’s heartland—feel left behind (1). Some believe that this downward trajectory helped spark the rise of Trump. While this might seem counterintuitive at first because Trump is viewed by the liberal media as an uncaring, East Coast elite, scholars have strived to understand the appeal of Trump among working-class, white voters, particularly in the Midwest. This line of research is particularly important for the future of electoral politics. The movement Trump’s election sparked, and even Trump himself, are not going away anytime soon. Understanding Trump’s appeal can explain his continued support and how other candidates can seize upon the movement he built. This research paper will explore the connection between despair and the rise of Donald Trump. It will use data on unemployment, education levels, and levels of drug- and alcohol-related deaths and suicide taken from the swing state of Michigan, which narrowly helped Trump win the 2016 election. The data section will show that Trump performed better in places where residents seemed more likely to feel economically and socially left behind. However, we must first classify and understand what scholars mean when they discuss despair in rural, working-class America.
Understanding Rural Support for Trump Political scientist Katherine Cramer, in her book Politics of Resentment, argues that rural politics can be understood as stemming from the creation of a rural community consciousness, rooted in resentment toward “elites” and urbanites (2). Through interviews with a group of locals in rural Wisconsin, Cramer discovered what rural consciousness looks like and defines it as three sentiments: caring about perspectives of power, primarily that urban areas have all of it and rural areas have none; respect for the “rural way of life”; and the perspective that too few resources are allocated to rural areas (3). All in all, her definition of rural community consciousness paints a picture of rural Americans feeling that urban Americans, and by extension government officials who are overly influenced by urban values, have no respect for their way of life, and are draining rural resources and livelihoods through welfare programs and other legislative efforts that advantage urban areas. This perceived allocation of resources toward exclusively urban areas is not actually the case, and many government funds and programs target rural areas. However, governmental bodies do not always prioritize marketing their budget allocations and as a result many rural communities are uninformed about the inner workings of the political system. In addition, because rural Americans are disillusioned, they have low desire to learn about government activities, leading these causal beliefs to go unchallenged and unresearched (4). This breakdown in communication and understanding has long-reaching effects. The Republican Party has seized upon these sentiments to further its agenda. Many rural Americans are wary of governmental employees and programs that they view as elite and guilty of stealing rural money for personal or political gain (5). Journalist Thomas Frank understands the power of rural resentment but argues that it is not necessarily about a lack of understanding of budgetary inner workings or community anger but is rather about character assessment. He asserts that while resentment and hostility are important in understanding the rural vote, the most crucial factor is actually authenticity.6 Republicans, he argues, have successfully rebranded Democrats as out-of-touch city elites worthy of scorn. Even as Republican political figures push legislation that hurts working-class Americans, they successfully market themselves as relatable, the politicians that a voter would want to have a beer with (7). This authenticity wins them voters despite their lack of concrete political achievements for lower-income, working-class Americans. When putting these scholarly findings in conversation with the campaign message on which Trump ran, it is easy to see how in 2016 Trump played upon the resentment and despair of rural areas by framing the Democrats, and by extension the urban liberal elite, as the cause for all problems. Throughout the campaign, Trump had a habit of saying exactly what was on his mind, perhaps giving him an air of over-a-beer-authenticity and relatability—he certainly appeared honest due to his unfiltered dialogue. Moreover, his lack of political experience likely worked in his favor in areas where government officials are seen as untrustworthy. By contrast, his opponent, Secretary of State Hillary Clinton, had held governmental office at various levels for many years, which played into Trump’s painting of her in the eyes of many rural communities as an urban elite living off the people’s hard earned money. Further complicating the relationship between rural and urban areas is the perception that urban areas are more liberal or have different ways of life. Sociologist Jennifer M. Silva argues that some rural voting patterns can be explained by the considerable amount of fear that exists in many of these places, both around shrinking economic opportunities and the general future of communities (8). This fear often manifests itself as a feeling that America must return to “disciplined values” such as hard work, or worries that immigrants are stealing all the well-paying jobs. In his campaign, Trump certainly identified these fears (9). This could be seen in his harsh anti-immigrant rhetoric, seemingly placing the blame on them for the lack of decent-paying jobs. Trump also emphasized his skills as a businessman, arguing that they would help him run the country and increase the job market. Many authors believe that Trump’s strong performance in rural counties can be explained by the “landscapes of despair” theory, arguing that all of the areas in which Trump over-performed or Clinton underperformed have experienced immense social, economic, and health declines over the past several decades. These authors believe that Trump appeals to voters who are not necessarily the poorest in America but whose lives are worse off than they were several decades ago (10). Trump spoke to that pain and offered these Americans a message that appealed to their despair. Goetz, Partridge, and Stephens find that economic conditions have changed over time throughout rural communities, with urban centers becoming more prominent and fewer agricultural jobs available. However, they find that not all rural areas are doing uniformly poorly economically across America. Instead, there has been “profound structural change” in most of these areas in terms of the types of employment available (11). This structural change could contribute to the feeling among many rural Americans of having been left behind and could also explain some of the draw to Trump’s nationalism, as trade and increased globalization, along with new technology, have contributed to this extreme change (12). Goodwin, Kuo, and Brown agree with this theory and find a correlation be- tween higher rates of opioid addiction in a county and the percent of the county that voted for Trump (13). They found that opioid addiction is one way to measure the sociocultural and economic factors that often created support for Trump and noted that simple unemployment measurements fail to capture this same trend. These two pieces of data imply that the voting patterns of Trump’s supporters do not correspond with being worse off economically than the rest of America, but rather are related to whether people personally feel like they and their communities are backsliding, with opioid addiction as an indicator of this attitude. Gollust and Miller argue that the opioid crisis triggered support for Trump, not necessarily because it is a measurement of sociocultural factors within communities, but because it triggered a comparison in the minds of people living in communities where the crisis was rampant (14). Through experimentation, Gollust and Miller found that Republicans and Trump supporters were more likely than Democrats to view whites as the political losers in the country (15). It is easy to see how Trump’s aggressive rhetoric appealed to people who felt like they were losing and that they needed a fighter to advocate for them. Journalists and Political Research Associates Berlet and Sunshine believe that Trump’s rise can be attributed to changing ways of life and Trump’s connection to right-wing populism (16). They argue that there was a rise in the notion that the white-Christian-heterosexual-American way of life is “under threat” in the years preceding the election. They believe that Trump’s brash candidness, his willing- ness to invoke Islamophobia, homophobia, and xenophobia, and his appeals to Christianity and the patriarchy tapped into a deep-simmering rage that had been growing among rural people (17). In this way, white racial antagonism contributed to Trump’s success. Rural Americans redirected their despair into rage toward those individuals and collectives that they perceived as a threat to their way of life. This argument is heavily focused on the effects of bigotry and anger on people’s voting choices, whereas several other authors, such as Cramer and Frank, believe that rural support for Trump was much less rage-based and much more about a lack of trust in government and the feeling of being neglected for years. We believe that the theories of despair and feelings of backsliding can explain some of the trend toward rural support for Trump in 2016. We believe that data will show that the most important despair factors depict not how badly off a community was in 2016, but rather the comparative: how much worse off it was in 2016 compared to several decades earlier. Finally, we agree with Cramer’s theory of rural consciousness and feel that it may have played a role in general distrust for Clinton as a candidate, but found it impossible to test those attitudes given the data available.
Methodology To test the effect of rural pain and despair in connection to GOP voting share in Michigan, we used data at the county level, primarily from 2016, which came from the United States Census Bureau and the Institute for Health Metrics and Evaluation (18). We focused specifically on the 2016 election because of the connection between Donald Trump’s share of the vote and struggling rural voters, which was higher than previous GOP candidates Mitt Romney and John McCain (19), as well as Trump’s reputation as an outsider (20). We chose to look at data from all Michigan counties, regardless of which candidate the county voted for or whether the county flipped parties between 2012 and 2016. We chose not to look exclusively at flipped counties because Trump flipped only twelve counties in Michigan. In order to obtain a statistically significant and unbiased result about the effects despair factors had on county result data, more than twelve data points were needed. We instead measured the effect of despair factors on the vote share that Trump received in each county in 2016. We defined six despair factors to represent the challenges and pains each county faced at the time of, or leading up to, the 2016 election. The first three of these factors are defined as the percent change in age-standardized mortality rates be- tween 1980 and 2014 for the following: alcohol use disorders, drug use disorders, and self-harm injuries (alcoholchange, drugchange, and selfchange, respectively). The source from which we obtained information on drug use disorder–related fatalities did not provide a breakdown by substance so we are unable to determine how much of this factor can be attributed to the ongoing opioid crisis. However, due to the sweeping nature of the crisis, particularly in rural working-class communities, we believe there is some relationship between the drug-use-disorder mortality rate and the opioid crisis (21). Factors that measure changes in living conditions over time, such as changes in fatal overdoses, alcohol deaths, and suicides, will test whether despair is truly about voters’ communities becoming worse than they were before. The fourth despair factor (undereducated) represents the education level of each county, using the percent of adults over 18 whose highest educational attainment in 2014 was a high school degree or less. This is an important factor to examine while exploring despair because lower levels of education limit career and income options and are often correlated with greater instances of feeling trapped or stuck in a community (22). The final two factors in the exploration are unemployment and the percentage of the county population that died of any cause in 2016. This last factor is important to consider because higher death rates often show that a county has an aging population and can accordingly suggest that younger people are choosing to leave. If the theories described above are true, unemployment should not matter as much because the “landscapes of despair” theory focuses on decline in communities and in economic opportunities, meaning many voters could be employed but working longer hours, harder jobs, getting paid less, or feeling like they have fewer opportunities than they once did. To test this we ran the same statistical analysis for unemployment but specifically looked at whether the variable was statistically significant in predicting the Trump vote. If it was not, that would prove Goodwin, Kuo, and Brown’s theory that unemployment is not the best measure of Trump’s support in 2016. We used two statistical methods of analysis. First, we used histograms to compare a single despair factor, such as percent change in alcohol use disorder–related deaths, against the way that the county voted in 2016 to see if certain factors of despair disproportionately affected one party’s vote share. Second, we used the regression equation below to test our hypothesis that the six aforementioned despair factors led to Trump’s higher vote share in Michigan. Finally, we analyzed the despair factors individually to show their discrete effects on the GOP vote share in Michigan’s 2016 election.
TRUMPSHAREi = β0 + β1DRUGCHANGEi + β2ALCOHOLCHANGEi + β3SELFCHANGEi + β4UNDEREDUCATEDi +β5UNEMPLOYMENTi +β6PRCNTDEATHSi +εi
We fit the model using the county-level data we gathered to examine whether the test statistic led us to reject or fail to reject the null hypothesis that there is no relationship between these six despair factors and the percent of the vote share that Trump received in Michigan in 2016. We also used the R-squared from this regression to determine how strong the linear relationship of the regression equation was.
Results and Discussion The results we found conclusively show that we can reject the null hypothesis that there is no relationship between the six despair factors and Trump’s success in a county. The first statistical method we used was comparing histograms of each factor broken down by party identification. We found graphical evidence to suggest that death rates, suicide, undereducation, and unemployment were disproportionately higher in Republican counties. Changes in alcohol and drug deaths (alcoholchange and drugchange) did not seem to be strongly correlated with one specific party, though counties that voted very strongly for Republicans did seem to have the highest values (highest percent changes from 1980 to 2014) for both of these variables.
In some respects, the fact that many of these despair factors were higher in counties that voted Republican makes sense. By 2016, Barack Obama had been president for eight years, and often people who are unhappy with how the economy has been faring or who are unemployed vote for the candidate from the opposite party of the sitting president. However, large values of other factors, such as percent change in deaths from self-harm, are more alarming, as these first three variables measure changes dating back to the 1980s. We were surprised that alcohol and drug deaths seemed to be more evenly spread out between the parties than self-mortality, which was particularly unexpected due to the amount of literature on the correlation between those affected by the opioid epidemic and votes for Trump (23). Perhaps Goodwin, Kuo, and Brown’s theory that increased opioid usage is a good instrumental variable for Trump support still holds because this data only looks at drug mortality, not drug use. It is entirely possible that Trump counties have higher drug use, but we could not make a conclusion based on the data (24). However, due to the large percentage of drug overdoses that can be attributed to the opioid crisis, it is surprising that more of Gollust and Miller’s and Goodwin, Kuo, and Brown’s theories did not seem to be supported in this data set (25). The 3-D graphs in the appendix look at the relationships between the vote share that Trump received and percent changes in alcohol, drug, and self-harm mortality rates (26). The regression planes on these 3-D graphs show that percent change in self-harm mortality is the only variable with a clearly positive relationship to Trump votes. The other two changes in mortality variables have weaker linear relationships with Trump votes in part due to several county outliers. Exploring those outlier counties more and investigating why specifically they might not follow the common trend would be an interesting topic for ethnographic research. When we ran the regression analysis the first time, we included all six of the variables we categorized as measures of despair. We also ran the regression analysis with different combinations of these variables to see if we could increase the adjusted R-squared variable, which shows the accuracy of adding another variable to the model. We found that the model was most accurate when we excluded the unemployment value, and because its t-test statistic was not statistically significant, we made the decision to exclude it from the final regression we ran in order to have a more accurate model. At first, we were surprised that unemployment was not significant in the model; however, this seems to support the theory that many “despair voters” do have jobs—they are just low paying and highly stressful (27). This supports Goodwin, Kuo, and Brown’s analysis that the unemployment level is not a good measurement alone of whether a county voted heavily for Trump. More- over, the histogram shows that high levels of unemployment are not necessarily correlated with high percentages of the vote going to Trump. Clearly, there are other factors at play that this statistic fails to capture, and unemployment could be an incomplete benchmark for despair because it does not measure satisfaction in jobs nor whether a job pays a living wage. Overall, we found that a model with the five factors of despair besides unemployment gave an R-squared of .552, meaning that 55% of the variance among the percentage of votes Trump won in a certain county could be attributed to these factors alone. This is remarkably high considering that neither policies nor previous voting records were added into this regression. However, the only variables that were found to be statistically significant on their own were percent changes in self-harm deaths and percent of undereducated voters. We were surprised that percent changes in alcohol and fatal drug overdoses were not more significant than changes in self-harm deaths, but again, that could be partially attributed to the fact that the data only measures overdoses rather than frequency of use. While one would assume that there would be a positive representative relationship between the two, it is hard to know for sure. However, we can say that, on average, increases in despair in certain aspects of life are correlated with an increase in support for Trump in the 2016 election, supporting the original hypothesis of this paper that rural despair played into Trump’s win in Michigan in 2016. However, we fail to find definitive conclusions regarding some of the connections drawn in previous scholarly literature between opioid overdose and the Trump vote. Perhaps the most striking analysis is running the same regression but with Democratic vote share in the 2016 election and comparing the results with those from the Republican vote share. As seen in the table below, the coefficients for each variable nearly flip signs. A decrease in suicide-, alcohol-, and drug-related deaths, or other despair factors, can be expected on average to be associated with a positive increase among the percentage of the county “voting blue.” Counties that vote Democratic, at least on average, tend to have had some sort of positive change, on the individual or communal level, around certain measures of despair (28). This does not mean that Clinton voters were necessarily better off than all Trump voters across Michigan, but rather that Clinton voters had seen their lives improve, if only marginally, and Trump voters had not. Theories of despair regarding rural voters do not compare the lives of rural voters to those of voters in other areas of the state but rather investigate whether rural communities are worse off than they were several decades ago. Similarly, just because certain counties have seen an improvement in certain despair factors does not mean that their communities are not also grappling with alcohol, drug, and mental health issues. Additionally, better-educated counties tend to vote Democratic, with less-educated counties voting Republican. This is a reversal of certain historical trends (29). Again, at some level it is logical that voters who are doing better vote for the party that has been in power for the past several years. However, the data in these studies capture decades of crumbling communities. There is a downward trend in these communities in terms of levels of despair that shows that regardless of which party these counties vote for (whether they vote for the opposite party when they feel dissatisfied with the current one, or for the same party when things seem to be going well), neither party has been able to stop the 34-year trends of increases in suicide-, drug-, and alcohol-caused deaths. This validates theories of “rural consciousness” and “rural despair” by Cramer and Goetz, Partridge, and Stephens that rural communities clearly see and feel suffering in their communities and perceive a lack of attention and resources given to them (30). One could also argue that this supports Silva’s theory that many rural communities fear for their futures based on the downward spiral these communities have experienced for several years or decades (31). This fear could motivate voters to act more drastically or to believe that a massive change is necessary. In the voting booth, this could lead to their voting for a more unconventional candidate.
Trump’s main slogan was “Make America Great Again,” suggesting that, at some level, he understood and was trying to court those experiencing this sense of despair. For many voters, America is the best it has ever been: we have unprecedented levels of rights and acceptance for women, minorities, and members of the LGBTQ+ community. Going back seems like regression, not progress. But as shown by this data, many of the counties that voted for Trump in 2016 were better off by certain metrics in 1980 than they are now. It makes sense that residents of these counties could be worried about the continuing decline of their communities and could want to go back to a better time and quality of life. Not to mention, according to Cramer’s thesis of rural consciousness, voters in rural Michigan could be very distrustful of any type of governmental employee promising change. Trump’s brand as a businessman with no prior political experience could have especially appealed to those affected by rural-consciousness thinking. His role as an outsider was relatable. His phrase “drain the swamp” directly spoke to the prevailing belief in these communities that Washington, DC is full of people who take taxpayers’ money and waste time. His opponent had been in the public eye for years in various government positions and was by extension seen, and marketed by conservative news outlets, as the leader of the “liberal elite.” Particularly in contrast with her, Trump could have seemed particularly appealing to those rural voters. The data we found strongly supports Cramer’s thesis that rural despair and resentment led to the crumbling of the Blue Wall. In order for Democrats to rebuild their former strongholds in these states, the party must examine the real pain and anger that many rural voters experience. They need to understand the hopeless- ness people are feeling and recognize why Trump specifically appeals to them. Trump, and the Republican Party, have been strategic in tapping into the anger, fear, and pain that rural voters feel. Democrats contributed to the phenomenon of rural consciousness and the belief that Democrats are coastal elites who neither care about nor understand middle America (32). Clinton and other Democrats have made several public missteps, including making fun of these voters, that have further reinforced this idea. Trump has succeeded in directing rural voters’ anger and mistrust toward the government, specifically bureaucracy and governmental programs that could actually help rural areas. Overall, Democrats need to strengthen their relationship with white working-class voters, and understanding rural despair and consciousness might be the first step to doing so. They need to consider creating messages that specifically address and appeal to rural voters and find and support candidates who can connect with them. To win back rural voters, Democrats also need to focus on messaging in rural America. That includes creating programs that provide resources and relief to these struggling areas, but also, perhaps more importantly, it requires making sure that rural communities are aware of these resources. If rural communities still view government as ineffective and uninterested in their problems, these programs will not be sufficient. It will take significant effort and messaging on behalf of Democrats to convince enough voters that the Democrats’ party, not Trump’s, actually represents rural Americans’ best interests. While President Biden managed to do this in 2020, very narrowly, it remains to be seen whether other Democratic candidates will be able to or will even want to capitalize on this messaging. It also remains to be seen which candidates will seem authentic to rural voters—clearly this was a big factor in Trump’s victory and was maybe an even bigger factor contributing to Clinton’s loss. Going forward, the Democrats will need to support candidates who can reach rural voters effectively and authentically, which remains a tall order. While Trump, not establishment Republicans, created a new coalition that drew on rural pain and despair, it would be naive to assume that the Republican Par- ty will not continue to take advantage of rural despair to win elections. Since Trump’s defeat, the messaging of the Republican Party has remained largely the same as when Trump was in office. If Democrats do not devote resources to successfully addressing these voters, they will have to accept the possibility that their once reliable Blue Wall will fall again or will never be rebuilt, and they will need to find another sizable coalition of voters to target in order to win elections at every single level.
1 Pottie-Sherman, “Rust and Reinvention,” 2.
2 Cramer, The Politics of Resentment, 11.
3 Ibid., 54.
4 Cramer and Toff, “The Fact of Experience.”
5 Cramer, Politics of Resentment, 127.
6 Frank, What’s the Matter with Kansas?, 113.
7 Thomas, What’s the Matter with Kansas?, 119.
8 Silva, We’re Still Here, 45.
9 Inglehart and Norris, “Trump and the Populist Authoritarian Parties.”
10 Monnat and Brown, “More than a Rural Revolt.”
11 Goetz, Partridge, and Stephens, “The Economic Status of Rural America in the President Trump Era and Beyond,” 101. 12 Ibid, 117. 13 Goodwin et al., “Association of Chronic Opioid Use With Presidential Voting Patterns in US Counties in 2016,” e180450. 14 Gollust, and Miller, “Framing the Opioid Crisis: Do Racial Frames Shape Beliefs of Whites Losing Ground?” Journal of Health Politics, Policy and Law 45, no. 2 (April 2020): 241-276. 15 Gollust, and Miller, “Framing the Opioid Crisis: Do Racial Frames Shape Beliefs of Whites Losing Ground?”
16 Berlet and Sunshine, “Rural Rage,” 480–82.
17 Ibid, 490.
18 Foster-Molina and Warren, Partisan Voting, County Demographics, and Deaths of Despair Data.
19 Monnat, “Deaths of Despair and Support for Trump in the 2016 Presidential Election.”
20 Cramer, Politics of Resentment, 127-137.
21 Florian Sichart et al., “The Opioid Crisis and Republican Vote Share.”
22 Autor, Katz, and Kearney, “The Polarization of the U.S. Labor Market.”
23 Goodwin et al., “Association of Chronic Opioid Use With Presidential Voting Patterns in US Counties in 2016,” e180450. 24 Ibid.
25 Imtiaz et al., “Recent Changes in Trends of Opioid Overdose Deaths in North America.”
26 Created with the help of Ella Foster-Molina.
27 Torraco, “The Persistence of Working Poor Families in a Changing U.S. Job Market.”
28 We do not mean to suggest that Democratic voters do not face their own share of struggles, rather that this data on average suggests that counties that voted Democratic were less affected by these specific measures of despair in 2016. 29 Harris, “America Is Divided by Education.”
30 Goetz, Partridge, and Stevens, “The Economic Status of Rural America in the President Trump Era and Beyond.” Applied Economic Perspectives and Policy 40, no. 1 (February 16, 2018). 31 Kim Parker et al., “Similarities and Differences between Urban, Suburban and Rural Communities in America.”
32 Cramer, Politics of Resentment, 127-137.
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