July 19, 2026
By Stephen Stofka
In last week’s post, I ended with a promise to explore our political polarization. We are divided not only by values and ideologies, but by population density. Some of us prefer less density and less regulation. Those who live in a small town may have a limited choice of dining, shopping, health care and entertainment. There may be only one movie theater with one screen. In rural areas, the land doesn’t sit there as a space for human homes, businesses and institutions. The land works. It provides food for human or animal consumption. It provides trees, minerals and other resources. Its mountain peaks reach into the skies to collect water from the clouds, channeling water down the slopes to the valleys below. In these areas, the human community is a part of the land. People live with the land.
The U.S. Census Bureau has long distinguished rural and urban areas by population density. Recently, they have included outlying areas with low population density whose economies are tied to a nearby metro area. The distinguishing characteristic is that 25% of residents commute to a metro area for work (Source).
In dense urban areas, people live on the land. The human community dominates the land. Grass and trees serve as a spiritual refuge for humans, and a habitat for birds and small mammals. Land is space for human designs of concrete, asphalt and lumber. People who live there appreciate the availability of choices in most areas of their lives. They tolerate the regulatory environment needed to manage the frictions between neighbors. They are accustomed to a diversity of cultures and religious traditions.
In the small east Texas town where my mother grew up, there were churches of a variety of Protestant sects. In less than a mile on the main street of the town, there was a Methodist, Baptist, Christ the King and Presbyterian Church. That’s all the variety that people needed in that community. Those who attended an Anglican or Catholic church could drive almost an hour to Dallas on Sunday morning. My mother could not remember meeting a Jewish or Muslim person until she moved to New York City after World War 2.
Different Outlooks
The residents of rural and urban areas have different sensibilities and outlooks on life. They may share values and ideologies, but they place a different emphasis on those values when they vote. In rural towns, change is slow and the residents are suspicious of new developments. In many rural towns, there aren’t enough job or educational opportunities for young people. Many move to urban areas, where they must become accustomed to a faster pace of life. They try on new ideas, music, and other diversions. They become part of social groups that may seem alien to their parents. They question the choices and morals of the political leaders their parents voted for. A recent study by Pew Research shows that younger voters under 30 vote more heavily for Democratic Presidential candidates. Those over 60 favor Republican candidates (Source).
Density Signpost
Several studies in the past twenty years have found that population density is a significant factor predicting party affiliation. It’s not a direct cause of course, but it represents important differences in economic and social circumstances between urban and rural environments. After reading several papers on this topic, I’ll rely on a paper by Trevor Brown and Suzanne Mettler (2024) (Source). The paper is openly available and the authors reference a lot of other papers on the topic. Brown and Mettler note that, until 2000, there was not such a clear partisan divide between rural and urban voters. Here’s a chart from their paper to show the wide divergence in voting patterns in the past two decades.
The chart shows the percentage of voters, urban and rural, who voted for the Republican Presidential candidate. In the year 1996, there was only a 3% difference between urban and rural voters. In the 2020 Presidential election, that difference had grown to 21%. What happened?
Multiple Causes
There are several causes that account for the behavior of an airplane in flight, and human beings are a lot more complicated than airplanes. Different authors have highlighted a number of causes. Those in areas of higher population density are more educated. Not smarter. Just more educated. They work at jobs that require more education, or a degree becomes a filtering mechanism in a competitive job market. Job growth in rural areas has been stagnant. Those in urban areas have higher incomes but have higher housing and living costs. As already discussed, there is a greater variety of religious institutions, and less social pressure to embrace any religion.
Brown and Mettler pointed to some studies that stressed rural “consciousness, identity, or values,” but that didn’t explain the urban-rural divergence in political sentiment of the past three decades. Some authors cited the resentment that rural voters might have for urban voters who impose their priorities on everyone. However, this is not a recent development. In the 1960s, voters in rural upstate New York voted Republican. Voters in the urban area that included New York City voted Democratic. For decades, Colorado voters in the Front Range east of the Continental Divide have voted Democrat, while those in the western part of the state and on the eastern plains voted Republican. Perhaps readers will have similar experiences of longstanding urban-rural divides in other states.
A Shift in Outlook
Many election analysts have struggled to explain the change in voter sentiments before and after an election. Just prior to the 2016 election, Gallup asked voters for their assessment of the economy. 81% of Republican voters thought the economy was getting worse. Soon after Trump won the election, only 44% of Republican voters felt the same way. Among Democrats there was a similar shift, although not as extreme (Source, McCarthy & Jones, 2016). In the weekly Gallup surveys over the following weeks, Republican confidence improved while Democrat’s outlook declined. A similar shift happened before and after the 2024 election. Some analysts have termed the phenomenon partisan perceptual bias. A better term might be partisan prediction bias.
We Predict
We are creatures who must survive on our ability to predict. Unlike a cat, we do not have fast reflexes. To navigate our world, we must predict the motion of others. We get more comfortable driving a car as we learn to make many predictions in a short period of time. Secondly, we are social animals. To survive, we must learn to read the feelings and intentions of others to anticipate how they will act.
Vote Forecasting
Writing in the Public Opinion Quarterly, Andreas Graefe (2014) noted that asking people to predict an election winner was very accurate. She analyzed vote forecasting surveys from 1932 to 2012 and found that the majority of respondents correctly predicted the outcome 89% of the time (Source).
Notice that these forecasting surveys are different from voter intention surveys that ask people who they intend to vote for. These kinds of polls are probably the most reported on. For these kinds of polls, analysts must apply several statistical techniques to align the characteristics of their polling samples more closely with the characteristics of all voters in an electoral region. Sometimes they simply do not make the correct adjustments. Surveys prior to the 2016 election accurately predicted that Hillary Clinton would win the national popular vote. However, the national popular vote does not determine the winner of the Electoral College vote. Analysts misweighted the survey samples in several swing states, which delivered the winning electoral votes to Donald Trump (Source).
Gut Prediction
Asking voters how each of them will vote takes a lot of statistical tweaks to achieve an adequate level of accuracy. Asking voters for their gut predictions of the winner turns out to be quite accurate without those tweaks. When researchers ask people for their opinion on the economy, I think what many people respond with a prediction. Those predictions are based on our perception of our personal circumstances, local and national conditions. We filter those perceptions by ideology, values, and our previous experience. What pops out is a binary prediction of a likely winner from each of the two dominant political parties, not an assessment of the broad economy.
Why do people make such different predictions? Next week I want to look more closely at the components of our prediction process. And with that, I hope to see you next week.
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Photo by Brad Switzer on Unsplash
Notes:
Brown, T. E., & Mettler, S. (2024). Sequential Polarization: The Development of the Rural-Urban Political Divide, 1976–2020. Perspectives on Politics, 22(3), 630–658. https://doi:10.1017/S1537592723002918
Graefe, A. (2014). Accuracy of vote expectation surveys in forecasting elections. Public Opinion Quarterly, 78(S1), 204–232. https://doi.org/10.1093/poq/nfu008
McCarthy, J., & Jones, J. M. (2016, November 15). U.S. economic confidence surges after election. Gallup. https://news.gallup.com/poll/197474/economic-confidence-surges-election.aspx
