Does Political Advertising Persuade?

Abstract

Well over $1 billion was spent on televised political advertising in the U.S. in 2004. Given the ubiquity of the 30 second spot, one might presume that ads must affect viewers’ vote choices. Somewhat surprisingly, though, scholars have yet to make much progress in confirming this claim. In this paper, we leverage a comprehensive dataset that tracks political ads in the nation’s top media markets and a survey of presidential and U.S. Senate voters in 2004. We ask whether exposure to presidential and Senate advertising influences voters’ evaluations of candidates and the choices that they make at the ballot box. In the end, we find considerable evidence that advertising persuades—and that its impact varies depending on the characteristics of the viewer.

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Fig. 1

Notes

  1. 1.

    We do not distinguish here between the different processes by which advertising might influence candidate preferences; that is, we do not distinguish between attitude change brought by conversion, the activation of predispositions or the reinforcement of prior preferences.

  2. 2.

    GRPs are a measure of the size of the audience for a television program. One rating point, on average, is equal to 1% of the television household audience in a particular media market. GRPs are helpful in determining the audience for a particular political commercial. For example, if an ad were aired 80 times, each with an average 10-point rating, that ad would achieve a total 800 GRPs. This is the equivalent of all households with a television viewing the spot 8 times or half of all households with a television viewing the spot 16 times.

  3. 3.

    Althaus, Nardulli, and Shaw (2001) examined advertising in the 1992, 1996, and 2000 presidential campaigns. Their county-level analysis found significant advertising effects only during the 1996 campaign. And these effects were smaller than those reported by Shaw (1999). It also suffers from some of the same weaknesses of the Shaw study, namely, that it does not account for non-candidate expenditures, which was an even bigger percentage of total spending in 2000 (see Goldstein & Freedman 2002b).

  4. 4.

    Literature speaking to this hypothesis has offered a mixed assessment. Chang (2003) reported that it was partisans who were influenced most by ad exposure, not political independents. Moreover, a series of experiments by Ansolabehere and Iyengar (1995) supported the claim that non-partisans voters are “the least receptive to political advertising” (p. 77). Instead, the authors reported that the effect of advertising is mainly reinforcement, moving voters to cast ballots in line with their partisan inclinations. But a different experimental study (Kaid, 1997) found some evidence for the opposite conclusion: that political independents were more influenced by watching a political spot than were partisans. Pfau and his colleagues (Pfau, David, Holbert, & Cho, 2001; Pfau, Holbert, Szabo, & Kaminski, 2002) went further, finding differences between partisans and unaffiliated subjects depending on ad sponsor and type; candidate contrast ads appeared to have the strongest effect on Republicans, while candidate positive ads and interest group ads had the strongest effect on independents.

  5. 5.

    Survey details are available on the web at http://csp.polisci.wisc.edu/BYU_UW.

  6. 6.

    Because of panel attrition (and competitive state over-sampling in the BYU/UW survey) we weight all models using the pweight command in STATA.

  7. 7.

    We match each respondent’s reported county of residence with the media market that covers that county.

  8. 8.

    Our specific formula for creating this “shows-based” measure of exposure was: (Number of ads during Jeopardy in respondent’s market * Jeopardy viewing) + (Number of ads during Wheel of Fortune * Wheel of Fortune viewing) + (Number of talk show ads * daytime talk show viewing) + (Number of morning news ads * morning news program viewing) + (Number of early evening news ads * early evening news viewing) + (Number of late evening ads * late evening news viewing) + (Number of ads aired during all other programs * mean television viewing).

  9. 9.

    One assumption of this approach to measuring exposure is that each ad has an equal impact, and we grant that this assumption may not hold in all cases. Some ads may be more effective because of their message or production value; other ads may be more effective because of when they were aired. Given, though, our focus on the overall ad environment of each campaign—an environment in which typically dozens of different ads were aired hundreds of times each over a couple of months—we expect that particularly effective or ineffective ads from competing campaigns will cancel out.

  10. 10.

    All exposure measures include interest group, party, candidate, and candidate/party coordinated advertisements. Thus, when discussing exposure to Democratic ads in the presidential race, we are referring to all Kerry ads, Democratic Party ads, and pro-Kerry interest group ads (e.g., MoveOn.org).

  11. 11.

    Twenty-nine percent of respondents were classified as low-information because they incorrectly answered all four knowledge questions (identifying the positions held by Bill Frist, William Rehnquist, Tony Blair and John Ashcroft). Forty-eight percent of respondents were classified as medium-information because they answered one or two questions correctly. Twenty-three percent of respondents answered three or four questions correctly, earning them the high-information designation.

  12. 12.

    The candidate travel data are reported by Eric M. Appleman of George Washington University at https://www.gwu.edu/∼action/2004/ (accessed on June 7, 2005). Appleman uses public schedules provided by the campaigns supplemented by press accounts to record in which city or cities Bush and Kerry made public appearances on each day. We matched each city with its media market to calculate the total number of visits by each candidate to each media market. We do not count visits to a media market in which a candidate attended only a fundraiser because fundraisers generally attract a relatively small number of attendees and are not well reported on by the local news media.

  13. 13.

    We estimated probabilities for a non-southern, married, 40-year-old white woman who identifies as a moderate and an independent. She has the mean of all other control variables, and she was undecided in both her vote choice and favorability during the pre-election interview. To simulate predicted probabilities, we used the Clarify program developed by King, Tomz, and Wittenberg (2000).

  14. 14.

    Because we suppressed the constant, STATA reports directly the test of whether the effect for each group is significantly different from 0. It is analogous to estimating the model with the main effect and interactive effects (minus one category) and then testing whether the main and interactive effect are combined to be different from 0. In addition, because the results for control variables are substantively the same as those reported in Table 1, we do not report complete model estimates for either table. These results are, however, available from the authors.

  15. 15.

    In 23 of those 25 instances, the coefficient is in the expected direction; that is, increasing exposure to Democratic advertising increases the probability of voting for the Democratic candidate or increases favorability of the Democratic candidate, and increasing exposure to Republican advertising boosts the likelihood of voting for the Republican candidate or evaluating the Republican candidate favorably.

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Acknowledgements

We would like to acknowledge the assistance of many in the preparation of this research, including the editors of this journal, Michele Claibourn, Erika Franklin Fowler, Travis Pratt and anonymous reviewers. We thank Ken Goldstein and Paul Freedman, both of whom helped generate the idea for this article several years ago. We are also indebted to Ken for the use of the Wisconsin Advertising Project data.

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Correspondence to Michael M. Franz.

Appendices

Appendix

Table A.1 Full presidential model results from Table 1
Table A.2 Full Senate model results from Table 1

Variable Coding

2004 BYU–UW Study

Presidential Vote Intent (Wave 2)—“If the election for President were held today, would you vote for” 0 = Bush, 1 = Kerry, other = missing

Presidential Vote (Wave 3)—“In the November general election for president, who did you vote for?” 0 = Bush, 1 = Kerry, other = missing

Senate Vote Intent (Wave 2)—“If the election for Senate were held today, would you vote for” 0 = Republican candidate, 1 = Democratic candidate, other = missing

Senate Vote (Wave 3)—“In the election for U.S. Senate, who did you vote for?” 0 = Republican candidate, 1 = Democratic candidate, other = missing

Kerry (Bush) favorability (Wave 2 and 3)—“Is your opinion of [Bush, Kerry] very favorable ... very unfavorable?” Ranging from −2 (very unfavorable) to 2 (very favorable)

Democratic (Republican) Senate candidate favorability (Wave 2 and 3)—”Is your opinion of [partisan Senate candidate] very favorable ... very unfavorable?” Ranging from −2 (very unfavorable) to 2 (very favorable)

Bush job approval (Wave 3)—“How would you rate the overall job President George W. Bush is doing as president: Excellent, pretty good, only fair, or poor?” 1–4 scale with 4 indicating “excellent.”

Educational attainment (Wave 1 only)—“What is the highest level of education you completed?” 1 = Elementary school only; 2 = Some high school, did not finish; 3 = Completed high school; 4 = Some college, didn’t finish; 5 = two-year college degree/A.A./A.S; 6 = four-year college degree/B.A./B.S; 7 = Some graduate work; 8 = Completed Masters or Professional degree; 9 = Advanced graduate work or Ph.D.

Age (Wave 1)—in years

South—1 = the respondent lives in the south (Alabama, Arkansas, Florida, Georgia, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, Texas, Virginia, Kentucky, Maryland, Oklahoma, West Virginia), non-South respondent = 0

Married (Wave 1 only)—“Are you currently married, widowed, divorced, separated or never been married?” 1 = married, 0 = all other responses

Race (Wave 1 only)—“Would you describe yourself as white, black, Asian, Hispanic, American Indian, other?” 1 = black, 0 = all else

Sex (Wave 1 question)—1 = female, 0 = male

Party (Wave 3 branching questions used to create five dummy variables)—strong Democrat, weak Democrat, Independents (includes party leaners and pure independents), weak Republicans, and strong Republicans

Ideology (Wave 2 only)—“Do you consider yourself generally liberal, moderate or conservative?” −1 = conservative; 0 = moderate; 1 = liberal

Sociotropic Economic Evaluation—“Would you say that over the past year the nation’s economy has gotten worse, gotten better, stayed about the same?” 1 = gotten worse; 2 = stayed about the same; 3 = gotten better

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Franz, M.M., Ridout, T.N. Does Political Advertising Persuade?. Polit Behav 29, 465–491 (2007). https://doi.org/10.1007/s11109-007-9032-y

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Keywords

  • Political advertising
  • Elections
  • Campaign effects
  • Persuasion