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Examining Brand Strength of Political Candidates: a Performance Premium Approach

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Abstract

Despite the growth of research on political marketing, fundamental questions concerning brand value of political candidates and its relationship with marketing mix activities remain unanswered. This research extends premium-based brand valuation methods to the political context by presenting a performance premium approach to assessing the strength of politicians’ brands and exploring the relationship between politician brand strength and political advertising. We develop a joint hierarchical Bayesian model of brand performance and marketing activity and estimate our model using data on the election performance and advertising expenditures of political candidates from US House of Representatives elections. Our findings suggest that politicians who possess a strong brand—those that perform better than expected given the partisan leanings of their districts, advertising spending, and other model controls—face a consequence for their brand strength in which political advertising by their opponents has a stronger negative impact on their performance compared to the effect of political advertising against politicians with weaker brands. We discuss the implications of our findings for political marketing.

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Notes

  1. See Gordon and Hartman [16] for more information on CMAG’s political advertising data.

  2. Four uncontested races and 19 races that did not feature a Republican vs. a Democratic were excluded (2.7% of the data). These races were excluded because vote share, fundraising, and advertising (by candidates, opponents, parties, and outside groups) are systematically different in such races. Thus, inclusion of these races may incorrectly impact our inferences about the relationship between brand strength and advertising effectiveness.

  3. Own advertising is defined in this way because the data provider codes candidate and party advertising spending together. This definition is fitting because the candidate has control over the message content for advertising funded by the candidate’s campaign and his/her national party.

  4. PVI captures district competitiveness, a key measure of expected candidate performance [19]. PVI is calculated from an average of a district’s past voting behavior, compared to national voting behavior, in the two most recent presidential elections; thus, the measure captures the Republican or Democratic leanings of districts. Positive PVI scores indicate a Republican-leaning district while negative PVI scores indicate a Democrat-leaning district.

  5. We construct this set of variables based on past research [16, 41]. For all districts, we were also able to collect a district-level measure of education (percentage of district population with bachelor’s degrees) and two additional measures of racial makeup (percentage of district population that is black or Hispanic). We found the education variable to be very highly correlated with median income. Given the importance of median income in predicting voting behavior [11], we include it in our analysis and exclude the education measure. The additional measures of district-level racial makeup are both strongly negatively correlated with percentage of district population that is white. We test an alternative model with all three measures of racial makeup. We find that this model has inferior model fit to the proposed model and that the two new measures are not significant in any equation. As such, we include only percentage of population that is white in our model.

  6. Future research may generalize our model framework to consider other outcomes, such as voter turnout.

  7. Modeling a log transformation of vote share as a function of the log of advertising spending is consistent with past work in marketing on political advertising [41]. Note that the advertising measures in Eq. (2) are the log of the summed total of own and outside advertising for a particular candidate (which differs from the advertising measures in Eqs. (6)–(9) which breakout own vs. outside advertising). As detailed below, we test several alternative specifications for the measures in Xi, including advertising without log transformations, share advertising variables, and breakouts of own vs. outside advertising.

  8. We assume that the coefficients βi· do not include time-varying unobservable factors. We leave the development of such a dynamic model to future research.

  9. The proposed model takes into account that campaign expenditures and political outcomes are intertwined. To test the robustness of the results, we also consider three more parsimonious model specifications in which we exclude the fundraising Eqs. (4)–(5), exclude the equations of advertising expenditures (6)–(9), or exclude the advertising expenditure and fundraising Eqs. (4)–(9). We find that the key result related to brand strength and advertising effectiveness holds in each of these alternative analyses.

  10. Priors, initial values, trace plots of each coefficients, and Gelman and Rubin statistics for each coefficient are available in the online supplementary materials. The supplementary materials also include model fit illustrations which plot the actual vs. predicted values for Eqs. (1)–(9).

  11. We also explore a model that excludes the demographic controls and find that it has inferior model fit compared to the proposed model (DIC = 32,599; MAE = 0.0365). This analysis further highlights the robustness of the findings as we see that the key result related to brand strength and advertising effectiveness holds in this alternative model.

  12. As of July 2018, 16 out of the 20 strong brand candidates in Table 4 were still in office. Of the four not in office, two retired and one ran for higher office. Of the weak brands, only four out of the 20 were in office as of July 2018.

  13. To provide an illustration of effect size of our brand strength measure (ηj), we use the posterior mean estimates of the coefficients, median values of the continuous measures, and most common value for dummy variables (e.g., most data is for 2010, so we use 2010 dummy). We vary the value of the brand strength measure (ηj) at its minimum, first quartile, median, third quartile, and maximum values. We estimate Eqs. (2)–(3) with these values and find the following Republican vote share (VS) estimates: ηj at minimum, VS = 23.03%; ηj at 1st quartile, VS = 44.40%; ηj at median, VS = 47.16%; ηj at 3rd quartile, VS = 49.21%; ηj at maximum, VS = 57.70%.

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Authors and Affiliations

  1. Kelley School of Business, Indiana University, 1309 East Tenth Street, Bloomington, IN, 47405, USA

    Beth L. Fossen

  2. Goizueta Business School, Emory University, 1300 Clifton Road NE, Atlanta, GA, 30322, USA

    David A. Schweidel & Michael Lewis

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  1. Beth L. Fossen
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  2. David A. Schweidel
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Correspondence to Beth L. Fossen.

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Fossen, B.L., Schweidel, D.A. & Lewis, M. Examining Brand Strength of Political Candidates: a Performance Premium Approach. Cust. Need. and Solut. 6, 63–75 (2019). https://doi.org/10.1007/s40547-019-0092-z

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