Abstract
This paper studies the channels through which political television advertising influences individuals' voting decisions. Scholars are interested to learn whether advertising primarily persuades people to change their choices of candidates or mobilizes people to vote. I find that advertising does both: about 60% to 70% of advertising's effect is persuasion, and 30% to 40% of it is mobilization. Advertising's effects are stronger on those who did not plan to vote for a major-party candidate. To decompose the impact into its components, the present paper estimates a multinomial probit model that permits analysis of decisions of turnout and candidate choice jointly in a Markov chain framework. In contrast to most studies that estimate the effects of aggregated exposure to advertising on voters' choices on Election Day, I study how advertising influences peoples' monthly voting intentions leading up to Election Day. In the context of the 2008 presidential election, the magnitude of the advertising effect is not large enough to overcome John McCain's significant deficit, but it potentially could have changed the outcomes of other close elections such as those in 2000 and 2016.
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Notes
From the Center for Responsive Politics, a charitable organization that follows money in politics.
Based on Federal Election Commission data, 41% of campaign expenditure went to the media in 2008; about 58% did so in 2012 and 2016.
Media spending is estimated from the costs of TV advertisements taken from the Wisconsin Advertising Project and the Wesleyan Media Project.
In my sample, very few voters choose a minor-party candidate. If respondents did not choose a major-party candidate, they usually did not vote at all.
One standard deviation of advertising exposure corresponds to approximately 4000 rating points, where one rating point represents exposure to advertising by 1% of the voting age population in the relevant media market.
The model readily can accommodate additional choices such as splitting Neither into two options: voting for a minor party candidate and not voting. However, expanding the choice set expands the number of observations required to estimate the model precisely. Most people who chose Neither did not vote. Focusing on the competition between the two major-party candidates who placed virtually all of the TV advertising is the primary objective of this paper.
The 2004 decision is a proxy for the August 2008 decision. I discuss the implications in the Sect. 5.
Whether or not the variations in survey completion time are correlated with other campaign activities is difficult to ascertain. In the supplementary materials, I show that the time required for survey completion is not easy to predict from observable variables.
Since the integral of the choice probability does not have a closed form solution, the estimation is performed by simulation (Train 2009) using the Geweke-Hajivassiliou-Keane algorithm implemented in Stata. I limit the number of alternatives to three to minimize the dimensions of the covariance matrix (Ω).
Let \({P}_{j}\) be the probability of choosing \(j\) and \(A\) be advertising exposure. We know that \({P}_{D}+{P}_{R}+{P}_{N}=1\). Total differentiation of the equation with respect to \(A\) yields the required relationship.
The estimated response rate is 43%.
Some states do not allow citizens to register immediately before Election Day. For example, voters must register 30 days prior to elections in Texas.
John McCain secured enough delegates in March 2008 to win his party’s nomination. Republican advertising subsided afterward. Hillary Clinton and Barack Obama were in a tight nomination race until June 7th, when Clinton conceded. After June 7th, TV advertising resumed; the ads almost exclusively supported McCain and Obama.
More details on advertising volume are in the supplementary materials online.
The distribution for November wave is similar. However, in November, everyone had decided on Election Day. Therefore, I use the four weeks before Election Day as the exposure window.
For additional exploration of the relationship between time to complete surveys, demographic characteristics, and turnout, see the discussion in the supplementary materials online.
The estimated coefficients of the individual characteristics and candidate-time-specific dummies are reported in the supplementary materials online.
The numerical results are reported in the supplementary materials online.
The full election results are available in the supplementary materials online.
For the complete election results, see supplementary materials online.
I multiply \({M}_{Rs}\) by 4 when considering 4000 rating points.
The four states are Michigan (16 electoral votes), New Hampshire (4), Pennsylvania (20), and Wisconsin (10).
Full results and discussion appear in the supplementary materials online.
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Acknowledgements
I benefited from the valuable insights of editor Pete Leeson, two anonymous reviewers, Barry Burden, Kenneth Hendricks, Alan Sorensen, Ilia Murtazashvili, Van Pham, Lourenço Paz, Chris Taber, Bradley Jones, Samantha Vortherms, and participants of various workshops at the UW-Madison, UW-Whitewater, Baylor University, and the participants of the 2018 Public Choice Society Annual Meetings. I thank Scott Althaus for generously sharing his zip code data. The advertising data were obtained from a project of the University of Wisconsin Advertising Project includes media tracking data from TNSMI/Campaign Media Analysis Group in Washington, D.C. The opinions expressed in this article are those of the author and do not necessarily reflect the views of the University of Wisconsin Advertising Project. All remaining errors are my own.
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Law, W. Decomposing political advertising effects on vote choices. Public Choice 188, 525–547 (2021). https://doi.org/10.1007/s11127-020-00849-8
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DOI: https://doi.org/10.1007/s11127-020-00849-8