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Ecologies of Unease: Geographic Context and National Economic Evaluations

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Abstract

Assessment of the nation’s economic performance has been repeatedly linked to voters’ decision-making in U.S. presidential elections. Here we inquire as to where those economic evaluations originate. One possibility in the politicized environment of a major campaign is that they are partisan determinations and do not reflect actual economic circumstances. Another possibility is that these judgments arise from close attention to news media, which is presumably highlighting national economic conditions as a facet of campaign coverage. Still a third explanation is that voters derive their national economic evaluations from living out their lives in particular localities which may or may not be experiencing the conditions that affect the nation as a whole. Drawing upon data from the 2008 presidential election, we find that varying local conditions do shape the economic evaluations of political independents. Moreover, unemployment is not the only salient factor, as fuel prices and foreclosures also figured prominently. Local economic factors, what we call geotropic considerations, shape national economic evaluations especially for those who aren’t making these judgments on simple partisan grounds.

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Notes

  1. Mondak et al. (1996) finds that when voters do have information, they are more likely to hold the president responsible for local, neighborhood conditions in their evaluation of his handling of the economy.

  2. Niemi et al. (1999) find that voters distinguish between the state and national economies based on objective economic indicators. While we do not directly test this theory, we do find evidence that objective state (and other definitions of locality) influence perceptions of the national economy.

  3. The principal components factor analysis for defining the similarity of political regions included the percentage Democratic of the presidential votes of 2004, 2000, 1996 and 1992, all loading on a single factor. The principal components factor analysis for defining the similarity of economic regions included U.S. Census sourced variables from 2006 estimates capturing median income, and components of the income distribution, all loading on a single factor. Each score was then subdivided into thirds, and regions were constructed based on the adjacency of counties lying within a common 33% of the distribution.

  4. Using a large administrative boundary such as county as the basic building block is not optimal, as space is continuous rather than fragmented into irregular and coarsely-sized pieces, but it proved to be the best possible option. Data at a smaller scale on home foreclosures, unemployment and fuel prices were either prohibitively costly or nonexistent.

  5. In related research, political scientists have found that women are more likely to engage in sociotropic economic voting than men (Welch and Hibbing 1992). The research on the extent to which other subgroups vote in sociotropic fashion is mixed, but our concern is less with the vote in this paper, and more the nature of economic evaluation itself.

  6. For statistical estimation, independence is coded: 0 = strong partisans, 1 = weak partisans, 2 = leaning partisans and, 3 = independents.

  7. Monthly home mortgage foreclosure data are available from the company RealtyTrac, at www.realtytrac.com. The monthly gasoline price data originate from www.GasBuddy.com. The monthly unemployment rate is available at the U.S. Department of Labor, Bureau of Labor Statistics, and reported at the county level 2 months after the data are collected at: www.bls.gov/data/home.htm.

  8. Unemployment rates for October 2008 at the county level were not reported until December, after the election.

  9. In other specifications of the model, we include the change in gas prices but present only the peak price in July here. This is because those locations showing low values of change from the July peak would have residents complaining about the sustained high cost of fuel. But at the other end of the distribution, residents from locations experiencing high values of change might exhibit concern because their peak prices were usually the highest—often shockingly high. The pungent memories of the extraordinary summer spike proved lasting for many voters, in spite of a significant fall in prices by October and November.

  10. In the Supporting Information, we present a basic analysis of the effect of geotropic factors on vote choice. We ran a straightforward one-level logistic regression model with presidential vote as the dependent variable, and placing national economic evaluation, July gas prices, the change in unemployment and accumulated foreclosures on the right-hand side as explanatory variables. We also controlled for a number of the usual predictors of vote choice, including party identification, income, race/ethnicity and whether the respondent claimed to be a born again Christian. These estimates show is that foreclosures drive up support for Obama, as does the peak in July gas prices, independently of national economic evaluations. Change in unemployment has no statistically significant impact on vote choice.

  11. The interaction of low news interest and July gas price is positive in all specifications of the model and statistically significant in all but one specification of local region.

  12. The dependant variable is a five-point measure anchored at 1 through 5. Therefore a movement of 0.025 on the scale is 6.25% (.025/4).

  13. This effect is calculated as the standard deviation of the change in foreclosure rate (13.23) times the coefficient for interaction between new resident and the foreclosure rate (0.0024).

  14. These effects are calculated as the highest value for independents (3) times the highest and lowest observed peak gas prices ($4.20 and $3.61 respectively) times the coefficient for the interactions between independent and July gas price (.062).

  15. This effect is calculated as the change in unemployment for Arizona (0.73) times the value indicating the lowest level of interest in the news (3) times the coefficient for the interaction between news interest and unemployment (−0.02).

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Authors and Affiliations

  1. Department of Political Science, Boston University, 232 Bay State Rd, Boston, MA, 02215, USA

    Andrew Reeves

  2. Department of Government and Politics, University of Maryland, College Park, MD, 20742, USA

    James G. Gimpel

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  1. Andrew Reeves
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Correspondence to Andrew Reeves.

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Reeves, A., Gimpel, J.G. Ecologies of Unease: Geographic Context and National Economic Evaluations. Polit Behav 34, 507–534 (2012). https://doi.org/10.1007/s11109-011-9167-8

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