Predicting Support for Trump: An Integrative Model

• In Event: Personality and Predispositions

Abstract

How can we account for the intense devotion of Donald Trump’s supporters? The question demands explanation, given the persistence of their loyalty despite Trump’s apparent violation of many cardinal rules and norms of American politics, as well as research-based criteria for the evaluation of presidents.

For example, Trump’s more than 30,000 documented lies violate the integrity criterion for evaluating presidential character (e.g., Kinder, 1986). His denigration of prominent Republicans (such as John McCain) and cultural heroes (e.g., calling American troops who sacrificed their lives “losers” and “suckers”) would seem to violate the criterion of empathy. His mishandling of the Covid pandemic (in which concealment of its severity and failure to take action to protect the public contributed to thousands of fatalities) impugn his competence, as does presiding over an economic collapse in which millions lost jobs (when a strong economy had been touted as the basis for his reelection). While Trump did lose the 2020 election, turnout was massive; He held huge rallies and garnered more votes than any losing candidate in history.

Using data from a national survey, we tested a theory of strongly-held beliefs that integrates multiple influential variables from prior research, including partisanship (Jacobson 2017), racial resentment (Schaffner et al. 2018), and emotional determinants (e.g., Roseman, Mattes, & Redlawsk, 2020).

Our theory proposes that candidates may attract strong support if they present a narrative encompassing five belief components and related emotions. The evaluative component specifies alternative outcomes differing greatly in desirability (e.g., vulnerability to crime vs. physical security; lost jobs vs. economic prosperity). The behavioral component specifies actions that can influence which outcomes will occur, including actions by the candidate (such as deporting criminal illegal immigrants) and their supporters (such as voting or attending rallies and demonstrations). The identificational component specifies individuals and groups who take the actions and experience the outcomes. These include the candidate as representing his(her) supporters, and opponents such as outgroups (e.g., minorities and immigrants) and members of opposition parties. The normative component typically presents the candidate and supporters as virtuous (in terms of values such as patriotism or loyalty), their actions as justified (e.g., by values of freedom or hard work), and desired outcomes as deserved (e.g., based on due respect or prior possession). In contrast, opponents are depicted as immoral (e.g., selfish, dishonest), their actions as wrongful (e.g., illegal), and the outcomes they seek as undeserved (e.g., unfair). The explanatory component provides evidence indicating that the narrative is true (e.g., that friends agree with it, or news sources support its claims). Emotions amplify the importance of component beliefs (e.g., fear about the occurrence of unwanted outcomes; hope about desirable ones; anger or contempt toward opponents; and pride in one’s own group and its accomplishments).

For the present study, we asked on 5-point scales how satisfied or dissatisfied respondents would be with what would happen if Trump were reelected (evaluative component); whether respondents supported or opposed the actions Trump would likely take (behavioral component); how well “really cares about people like you” describes Trump (identificational component); whether what Trump would do if reelected is morally acceptable or morally wrong (normative component); and how many of the respondent’s close friends would vote for Trump in 2020 (none/almost none/a few/about half/most/almost all/all of them, a social consensus measure of the explanatory component). The survey was administered online in October 2020 by Dynata to 1200 U.S. adults matched to the U.S. Census on age, sex, and race.

OLS regressions found that each of the hypothesized belief components, as well as four emotions (enthusiasm, anger, contempt, embarrassment) each independently predicted significant variance in feeling thermometer ratings of Trump—beyond the variance accounted for by Democrat vs. Republican political party identification. Racism/racial resentment was a marginally significant predictor.

These findings replicate and extend prior support obtained for the narrative theory in accounting for evaluations of Trump in 2016. The theory-driven model predicted over 90% of the variance in 2020 thermometer evaluations of Trump.

Logistic regressions found that the normative and explanatory components, racism/racial resentment, anger when thinking about Biden or Trump, and enthusiasm when thinking about Biden, each predicted significant variance in vote intentions (Biden vs. Trump)--beyond party identification. The differential results may indicate that models of vote choice should encompass narrative components for BOTH candidates, rather than just one of them.

Authors

• Ira Roseman, Rutgers University, CCAS

• Ben Rudolph, Rutgers University

• David P. Redlawsk, University of Delaware

• Kyle Mattes, Florida International University