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Place, Race, and the Geographic Politics of White Grievance

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Abstract

Rural resentment is a form of place-based grievance politics that scholars have used to explain the growing urban-rural divide in American politics. However, whereas extant theory assumes that rural resentment stems from rural identification, recently available data shows that beliefs about geographic inequity, which are central to rural resentment, are not held exclusively by those who embrace a rural identity. If geography is not the sole source of rural resentment, then what else explains this ostensibly place-based phenomenon? Among White Americans who do not identify as rural, we posit that belief in the existence of deliberate rural deprivation by government and media elites can be conceived as ‘place-based empathy’ toward rural Americans. Further, we argue that place empathy toward rural areas is partially an expression of White grievance politics stemming from the belief that the stereotypical rural resident is a White American suffering from relative deprivation at the hands of government officials who privilege non-white (and non-rural) constituents over them. Using the 2020 American National Election Time Series, as well as novel mTurk data, we show that White consciousness predicts beliefs about geographic inequity among non-rural identifiers but not rural identifiers. Instead, consistent with previous research, we show that racial prejudice is a better predictor of geographic attitudes for rural identifiers and White consciousness has little independent association. These findings provide a more complete and nuanced understanding of the ways race and place intersect to explain the grievance politics of White Americans in the Trump era.

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Data Availability

All data will be made publicly available upon publication of this paper.

Materials Availability

Replication files are available at https://doi.org/10.7910/DVN/A4B3RY.

Notes

  1. Quoted from Tucker Carlson Tonight, January 3, 2019.

  2. Statistic 1 in the replication files. Endorsement is operationalized as a score of twelve points or higher on the eighteen-point rural resentment scale.

  3. These four facets are perspective-taking, empathy concern, fantasy, and personal distress. For a review, see Davis (1983).

  4. Representative of non-institutionalized American citizens over the age of eighteen years old. Survey weights were used in all analyses.

  5. Our theory of rural empathy is predicated on attitudes that are uniquely felt by White individuals, therefore we restrict our analyses to White respondents. In Tables A1 and A2 we show that White non-rural respondents have higher empathy than non-White non-rural respondents and that feelings towards Whites are not equally predictive of rural empathy among White and non-White non-rural respondents.

  6. The original Munis (2020) index has four components. The ANES includes only adaptations of three of these components. Nonetheless, we achieve similar Cronbach’s alpha to Munis (2020). Importantly, the Cronbach’s alpha score exceeds thresholds deemed satisfactory regarding internal consistency (e.g., Taber, 2018), which is particularly noteworthy for a three-item measure.

  7. If one were to strike out specifically to develop a new measure of place-based empathy from scratch, it would likely look different than the ANES measure that we use. However, it would likely have a similar structure because place empathy inherently involves perspective taking.

  8. We acknowledge that this alpha score is at the low end of the acceptable range and is only narrowly acceptable on account of there being only three items (Griethuijsen et al., 2014) – all else equal, a greater number of scale items will equate to a higher alpha score (Taber, 2018). We urge other researchers, including those who help design and administer the ANES, to interrogate its qualities more directly. We further note that this is not a measurement article, and that we instead contribute to multiple substantive literatures in which these measures (and, in some cases, this same dataset) have already been deployed as a primary measure of interest (e.g., Buyuker et al., 2021; Jardina & Mickey, 2022; McCarthy, 2022; Nelsen & Petsko, 2021). Furthermore, this White consciousness measure has been extensively validated by Jardina (2019) and it derives, both theoretically and structurally, from the broader group consciousness literature (especially Gurin, 1985). It is also worth mentioning that the alpha score for these same three items is typically higher (e.g., Jardina, 2019), including in the 2016 ANES where it was 0.68.

  9. See Table A4 and the associated supporting information for additional modeling that includes ideology.

  10. Statistic 2 in the replication files.

  11. Table A5 includes regressions of each of the three components of the indexed place resentment/empathy dependent variable. We find that place-based empathy toward rural areas is driven primarily by disparate resources, while place-based resentment exhibits no such pattern. We take this as additional evidence that place-based resentment and place-based empathy are indeed distinct concepts.

  12. Table A6 includes regressions with the traditional measures of racial resentment.

  13. Sample collected using Amazon’s Mechanical Turk, coupled with Cloud Research’s mTurktoolkit, in March 2022. Given that the sample drawn from mTurk is a convenience sample, generalizability is always an issue. However, the congruence of the mTurk data with the 2020 ANES lends credibility to our claim that Americans associate rural with White. We restrict our analysis to White respondents (N = 767).

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Acknowledgements

The authors wish to thank the University of Calgary’s Department of Political Science, the Political Psychology Group at the University of Kent, Joseph B. Phillips, Nicholas F. Jacobs, Ashley Jardina, and the anonymous reviewers for their helpful comments.

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Dawkins, R., Nemerever, Z., Munis, B.K. et al. Place, Race, and the Geographic Politics of White Grievance. Polit Behav (2023). https://doi.org/10.1007/s11109-023-09897-4

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