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The Urban–Rural Gulf in American Political Behavior

Political Behavior volume 42, pages 1343–1368 (2020)Cite this article

Abstract

Urban–rural differences in partisan political loyalty are as familiar in the United States as they are in other countries. In this paper, we examine Gallup survey data from the early-2000s through 2018 to understand the urban–rural fissure that has been so noticeable in recent elections. We consider the potential mechanisms of an urban–rural political divide. We suggest that urban and rural dwellers oppose each other because they reside in far apart locations without much interaction and support different political parties because population size structures opinion quite differently in small towns compared with large cities. In particular, we consider the extent to which the compositional characteristics (i.e., race, income, education, etc.) of the individuals living in these locales drives the divide. We find that sizable urban–rural differences persist even after accounting for an array of individual-level characteristics that typically distinguish them.

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Data Availability

All data and information necessary to replicate the results in this article are available in the Harvard Dataverse at Gimpel et al. (2020) at https://doi.org/10.7910/DVN/IYBIUP.

Notes

  1. While non-metropolitan locales may cast a far lower percentage of total statewide votes than found in metropolitan areas, these votes are often sufficiently one-sided in political preference to be decisive. The precise numbers depend on geographic definitions of “rural” or “non-metro.” The nation has become more metropolitan, but there remains a substantial population (19%) in rural areas the U.S. Census defines as “places” containing less than 2500 people. If one includes populations in towns of less than 25,000 but greater than 2500, there is about 26% of the total population in rural areas. (https://www.census.gov/prod/cen2010/cph-2-1.pdf, see Table 6.)

  2. The proportion of voters living in electoral landslide counties (i.e., a margin equal to or greater than 20% or the two-party vote in presidential elections) has steadily increased. In 2016, 60% of the electorate resided in a landslide county, up from 38% in 1992. The increase in landslide counties came primarily from Republicans in rural areas and small towns (Aisch et al. 2016).

  3. Buttel and Flinn (1975, p. 135) comments that, “Agriculture was not viewed merely as the source of wealth, but as a fount of those human virtues and traits most congenial to self-government—a sociological rather than economic value.”

  4. This is known as allopatric speciation.

  5. Though Ripley et al. (2019) finds urban residents are less politically tolerant than rural ones.

  6. See Gallup (n.d.). GPSS surveys started asking for ZIP Codes in 2003.

  7. We choose to use ZIP Codes instead of counties or media markets because they are nearly always smaller in size and therefore a more precise measure of residence.

  8. Our analysis includes 124,381 geocoded respondents, which may vary across analysis because of other missingness.

  9. This is based on residential classification categories drawn from the U.S. Department of Agriculture’s urban–rural continuum codes (US Department of Agriculture 2016).

  10. Respondents were first asked, “In politics, as of today, do you consider yourself a Republican, a Democrat, or an independent?” If they identified as independents, they were then asked, “As of today, do you lean more to the Democratic Party or the Republican Party?”

  11. Population density is highly correlated with average employer size in the respondent’s ZIP Code. This indicates that our measure captures and tracks the size of local intermediary institutions as one moves from rural to urban environments.

  12. Our geographic measures are at the ZIP Code level. When referencing a locale, we are referencing a ZIP Code within that location. We include the ZIP Code to which we refer in parentheses.

  13. It is a credit to Gallup that respondents are sampled from such faraway places as the Aleutian Islands.

  14. The correlation between the logged variables is -.72.

  15. For the distance variable, we add one to values of zero in order to avoid undefined values. When we present the exponentiated scale in the figures, the lowest value is 1 since log(1)=0 and exp(0)=1.

  16. Based on comparisons using t-tests, each of these values are significantly different from each other.

  17. The 95% confidence interval for the estimate of strong Democrat is .35 to .37 for the minimum value and .23 to .25 for the maximum value. The 95% confidence interval for the estimate of strong Republican is .21 to .22 for the minimum value and is .31 to .33 for the maximum value.

  18. We explore whether the relationship between partisanship and density and distance change over time, but find no systematic evidence of this in our sample.

  19. Though we did not develop hypotheses based on the strength of partisanship, we note that the relationships appear strongest for strong identifiers. We speculate that this could be related to strong partisans being more likely to sort as a function of characteristics related to geography (Bishop 2008).

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Authors and Affiliations

  1. Department of Government and Politics, University of Maryland, 3140 Tydings Hall, 7343 Preinkert Drive, College Park, MD, 20742, USA

    James G. Gimpel & Nathan Lovin

  2. Department of Political Science, Washington University in St. Louis, Campus Box 1063, One Brookings Drive, St. Louis, MO, 63130, USA

    Bryant Moy & Andrew Reeves

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  1. James G. Gimpel
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Gimpel, J.G., Lovin, N., Moy, B. et al. The Urban–Rural Gulf in American Political Behavior. Polit Behav 42, 1343–1368 (2020). https://doi.org/10.1007/s11109-020-09601-w

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