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Natural Disasters, ‘Partisan Retrospection,’ and U.S. Presidential Elections

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Abstract

Research investigating whether natural disasters help or hurt politicians’ electoral fortunes has produced conflicting results. Some find that voters punish elected officials indiscriminately in the wake of a natural disaster (i.e. ‘blind retrospection’). Others find that voters instead incorporate elected officials’ subsequent relief efforts in their assessment (i.e. ‘attentive retrospection’). We argue that an additional consideration affects voters’ response to natural disasters: the elected official’s partisan affiliation. We contend that whether voters reward or punish incumbents following a disaster is influenced by whether or not the official is a co-partisan. We look for evidence of such ‘partisan retrospection’ by examining the effects of Hurricane Sandy on the 2012 presidential election, and find that voters’ reactions to disaster damage were strongly conditioned by pre-existing partisanship, with counties that previously supported Obama reacting far more positively to disaster damage than those that had earlier opposed him. We then use existing data to investigate the relationship between disasters and presidential elections between 1972 and 2004. We find that incumbent-party candidates performed no worse in disaster-affected co-partisan counties than in non-affected co-partisan counties, but that they underperformed in disaster-affected counties safely in the opposing party column.

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Fig. 1

Source Spatial Hazard Events and Losses Database 18.1 (SHELDUS)

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Notes

  1. It is debatable whether natural disasters are randomly distributed: certainly, some geographic areas are more likely than others to experience earthquakes, hurricanes, droughts, tornados, and other natural disasters. Additionally, voters may hold elected officials responsible for any long-term policies they believe might have contributed to the natural disaster or its severity. However, politicians do not have direct influence on the specific timing or location of the occurrence of a natural disaster.

  2. But see also Fowler and Hall (2018) and Achen and Bartels (2018).

  3. Heersink, Peterson, and Jenkins (2017) rely on one measure in their statistical model to reflect both disaster and relief, because, in the case of the 1927 Mississippi Flood, disaster severity and aid distribution were highly correlated. As a result, the measure of disaster damage includes the effect of both the disaster and subsequent relief efforts.

  4. Though see also Mummolo and Peterson’s (2011) note on interpreting the findings presented in Gasper and Reeves (2011).

  5. Similarly, Eriksson (2016) argues that Swedish voters punished the incumbent party following Storm Gudrun in 2006, as a result of the government’s poor response in the wake of the disaster. Ramos and Sanz (2018) find that in the wake of wildfires in Spain incumbent parties’ electoral performance improves. In contrast, Bovan, Banai, and Banai (2018) find no electoral effect either way in the wake of flooding in Croatia in 2014 and 2015 – even when incorporating relief efforts.

  6. Our study is distinct from that of Malhotra and Kuo (2008) in several ways. We focus on vote outcomes, rather than survey responses regarding blame for poor disaster response. We also analyze the disaster itself – in line with most retrospection literature – in addition to relief efforts, while Malhotra and Kuo focus exclusively on disaster response. Finally, Malhotra and Kuo rely on a national survey sample, meaning that most, if not all, respondents were not directly affected by Hurricane Katrina or the botched relief efforts that followed it.

  7. https://www.usatoday.com/story/news/nation/2013/10/29/sandy-anniversary-facts-devastation/3305985/.

  8. Note that we do not explicitly limit the data to Hurricane Sandy, but the geographic reach of all damage between October 26th and November 2nd suggests that it is all related to that event.

  9. https://www.nj.com/news/2012/11/hurricane_sandy_causes_294b_to.html.

  10. Our results are substantively unchanged when using a continuous measure of partisanship; results are presented in the Supplementary Materials.

  11. There are nine census divisions: New England, Middle Atlantic, South Atlantic, East North Central, East South Central, West North Central, West South Central, Mountain, and Pacific. We use Census Division fixed effects as a happy medium between the four Census Regions and the fifty states, but show robustness to using either state or region fixed effects in the Supplementary Materials.

  12. To be clear, if the marginal effect in both types of counties was negative, but the interaction was positive, this would suggest that both co-partisan and contra-partisan counties punished the incumbent in the wake of a disaster, but that co-partisan counties punished the incumbent less than contra-partisan counties, consistent with a theory of partisan retrospection.

  13. We expect the effect of damage in swing counties to lie between that for co-partisan and contra-partisan counties. Our expectation is borne out perfectly in the ordering of the point estimates (although they are not statistically distinguishable from each other).

  14. Data on county-level home prices were obtained from Zillow, a real estate research firm. We used data from November 2010 and November 2012 to calculate the change in home prices. Home price estimates are either county-specific, where Zillow has such data, or are state-level estimates attributed to individual counties. Estimates of per capita income and unemployment rate were calculated from Internal Revenue Service data at the county level. Further details on variable construction are provided in the Supplemental Appendix.

  15. One additional variable of interest is counties’ ideology—that is, is it partisanship that moderates reactions to disasters, or ideology? Unfortunately, in the modern period, when estimates of county-level ideology are available (based on methods developed in Tausanovitch and Warshaw (2013) and available at https://americanideologyproject.com), the two concepts are very highly correlated: in our sample, Obama 2008 vote share and Tausanovitch and Warshaw’s estimates of county ideology are correlated at approximately 0.7, inhibiting an ability to distinguish between the two.

  16. We obtained the Gasper and Reeves dataset from the Harvard Dataverse. We thank the authors for making their replication data readily available online. See Gasper and Reeves (2011) for the relevant details. See also Gallagher (n.d.) for a discussion of the handling of “missingness” and spatial correlation in these data and models.

  17. The sole difference between this operationalization and that adopted in our analysis of Hurricane Sandy is that the former covers the six months prior to an election, while the latter included only damage specifically attributed to Hurricane Sandy.

  18. We utilized the Gasper and Reeves’ data on median income and vote share in our primary replication models, though we note areas where we extend or diverge from their models below.

  19. This measure is described as an indicator in Gasper and Reeves (2011), but has unique values of 0, 1, and 2; to maintain consistency with the original analysis, we use the unaltered variable and treat it as a count.

  20. s indexes states; all counties are nested in states, but we suppress this notation for concision.

  21. Note that our results do not exactly match Gasper and Reeves’s (2011) results; this is because we identified duplicate observations in their replication data, which we have removed. The results are substantively unchanged. We are successful in directly replicating their results if we use their unaltered replication data; see the Supplementary Appendix.

  22. When we fully split the sample into the three types of counties and estimate separately in each, we find that turndowns have a negative (but still less negative) effect in co-partisan counties.

  23. Our analysis focuses on both “true incumbents” (e.g., George H.W. Bush in 1992 and Bill Clinton in 1996) and presidential candidates of the incumbent party (e.g., George H.W. Bush in 1988 and Al Gore in 2000). When restricting the sample to true incumbents only, we find results that are substantively similar to those reported in the text, though with an even larger difference between the effect of damage in co- and contra-partisan counties. See Table C.6 in the Supplementary Appendix.

  24. These results are broadly consistent with those of Heersink et al. (2017), which show a large decrease in support for the 1928 Republican presidential candidate in counties affected by a catastrophic flood of the Mississippi River. While Heersink, Peterson, and Jenkins interpret this finding as support for blind retrospection, it is also consistent with partisan retrospection, since most of the affected counties were strongly opposed to the incumbent Republican Party. One advantage of a research design based on replicating Gasper and Reeves’ findings is that it covers a much broader range of counties and provides greater variation in the types of counties affected by disasters.

  25. Most notably, our findings appear to be specific to Republican incumbents; when restricting the sample to Democratic incumbents, we no longer observe a positive difference in treatment effects between co- and contra-partisan counties; instead, the estimate is negative, albeit not significant at the 5 percent level. However, these results should be interpreted with caution, because our sample includes just three elections in which Democrats were the incumbent presidential party (1980, 1996 and 2000).

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Authors and Affiliations

  1. Department of Political Science, Fordham University, New York, USA

    Boris Heersink

  2. Price School of Public Policy, University of Southern California, Los Angeles, USA

    Jeffery A. Jenkins

  3. Department of Political Science, Washington University in St. Louis, St. Louis, USA

    Michael P. Olson

  4. Department of Politics, University of Virginia, Charlottesville, USA

    Brenton D. Peterson

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  1. Boris Heersink
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  2. Jeffery A. Jenkins
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  3. Michael P. Olson
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  4. Brenton D. Peterson
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Correspondence to Jeffery A. Jenkins.

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Replication data for this study can be found at: https://doi.org/10.7910/DVN/CTWSDT.

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Heersink, B., Jenkins, J.A., Olson, M.P. et al. Natural Disasters, ‘Partisan Retrospection,’ and U.S. Presidential Elections. Polit Behav 44, 1225–1246 (2022). https://doi.org/10.1007/s11109-020-09653-y

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