Does Political Advertising Persuade?


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


  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

  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.,

  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∼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.


  1. Althaus, S. L., Nardulli, P. F., & Shaw, D. R. (2001). Campaign effects on Presidential voting, 1992–2000. Working Paper, San Francisco, CA, USA.

  2. Ansolabehere, S., & Iyengar, S. (1995). Going Negative: How Political Advertising Shrinks and Polarizes the Electorate. New York: Free Press.

    Google Scholar 

  3. Ansolabehere, S., Iyengar, S., & Simon, A. (1999). Replicating experiments using aggregate and survey data: The case of negative advertising and turnout. American Political Science Review, 93(4), 901–910.

    Article  Google Scholar 

  4. Chang, C. (2001). The impact of emotion elicited by print political advertising on candidate evaluation. Media Psychology, 3(2), 91–118.

    Article  Google Scholar 

  5. Chang, C. (2003). Party bias in political-advertising processing: Results from an experiment involving the 1998 Taipei Mayoral Election. Journal of Advertising, 32(2), 55–67.

    Google Scholar 

  6. Converse, P. E. (1962). Information flow and the stability of partisan attitudes. Public Opinion Quarterly, 26(4), 578–599.

    Article  Google Scholar 

  7. Djupe, P. A., & Peterson, D. A. M. (2002). the impact of negative campaigning: Evidence from the 1998 senatorial primaries. Political Research Quarterly, 55(4), 845–860.

    Google Scholar 

  8. Freedman, P., & Goldstein, K. (1999). Measuring media exposure and the effects of negative campaign ads. American Journal of Political Science, 43(4), 1189–1208.

    Article  Google Scholar 

  9. Freedman, P., Franz, M., & Goldstein, K. (2004). Campaign advertising and democratic citizenship. American Journal of Political Science, 48(4), 723–741.

    Article  Google Scholar 

  10. Garramone, G., Atkin, C. K., Pinkleton, B. E., & Cole, R. T. (1990). Effects of negative political advertising on the political process. Journal of Broadcasting & Electronic Media, 34, 299–311. .

    Google Scholar 

  11. Goldstein, K., & Freedman, P. (1999). Measuring media exposure and the effects of negative campaign ads. American Journal of Political Science, 43(4), 1189–1208.

    Article  Google Scholar 

  12. Goldstein, K., & Freedman, P. (2000). New evidence for new arguments: Money and advertising in the 1996 Senate Elections. Journal of Politics, 62(4), 1087–1108.

    Article  Google Scholar 

  13. Goldstein, K., & Freedman, P. (2002a). Campaign advertising and voter turnout: New evidence for a stimulation effect. Journal of Politics, 64(3), 721–740.

    Article  Google Scholar 

  14. Goldstein, K. M., & Freedman, P. (2002b). Lessons learned: Campaign advertising in the 2000 elections. Political Communication, 19, 5–28.

    Article  Google Scholar 

  15. Green, D. P., & Krasno, J. S. (1988) Salvation for the spendthrift Incumbent: Reestimating the effects of campaign spending in house elections. American Journal of Political Science, 32, 884–907.

    Google Scholar 

  16. Green, D. P., & Krasno, J. S. (1990). Rebuttal to Jacobson’s ‘New Evidence for Old Arguments’. American Journal of Political Science, 34, 363–372.

    Article  Google Scholar 

  17. Haddock, G., & Zanna, M. P. (1993). Impact of negative advertising on evaluations of political candidates: The 1993 Canadian Federal Election. Basic and Applied Social Psychology, 19(2), 205–223.

    Article  Google Scholar 

  18. Hitchon, J. C., & Chang, C. (1995). Effects of gender schematic processing on the reception of political commercials for men and women candidates. Communication Research, 22(4), 430–458.

    Article  Google Scholar 

  19. Holbrook, T. (1996). Do campaigns matter?. London: Sage Publications.

    Google Scholar 

  20. Iyengar, S., & Simon, A. F. (2000). New Perspectives and evidence on political communication and campaign effects. Annual Review of Psychology, 51(1), 149–169.

    Article  Google Scholar 

  21. Jacobson, G. C. (1997). The politics of congressional elections (4th ed.). New York: Longman.

    Google Scholar 

  22. Jacobson, G. C. (1990). The effects of campaign spending in house elections: New evidence for old arguments. American Journal of Political Science, 34(2), 334–362.

    Article  Google Scholar 

  23. Johnston, R., Hagen, M. G., & Jamieson, K. H. (2004). The 2000 presidential election and the foundations of party politics. Cambridge: Cambridge University Press.

    Google Scholar 

  24. Joslyn, M. R. (2003). The determinants and consequences of recall error about Gulf war preferences. American Journal of Political Science, 47(3), 440–452.

    Article  Google Scholar 

  25. Kahn, K. F., & Geer, J. G. (1994). Creating impressions: An experimental investigation of political advertising on television. Political Behavior, 16(1), 93–116.

    Article  Google Scholar 

  26. Kahn, K. F., & Kenney, P. J. (1999). Do negative campaigns mobilize or suppress turnout? American Political Science Review, 93(4), 877–890.

    Article  Google Scholar 

  27. Kaid, L. L. (1997). Effects of the television spots on images of Dole and Clinton. American Behavioral Scientist, 40(August), 1085–1094.

    Article  Google Scholar 

  28. Kaid, L. L., & Boydston, J. (1987). An experimental study of the effectiveness of negative political advertisements. Communication Quarterly, 35, 193–201.

    Google Scholar 

  29. King, G., Michael, T., & Jason, W. (2000). Making the most of statistical analyses: Improving interpretation and presentation. American Journal of Political Science, 44(2), 347–361.

    Article  Google Scholar 

  30. Klapper, J. (1960). The effects of mass communication. New York: Free Press.

    Google Scholar 

  31. Krosnick, J. A., & Brannon, L. A. (1993). The impact of the Gulf war on the ingredients of presidential evaluations: Multidimensional effects of political involvement. American Political Science Review, 87(4), 963–975.

    Article  Google Scholar 

  32. Lazarsfeld, P. F., Berelson, B., & Gaudet, H. (1944). The people’s choice: How the voter makes up his mind in a presidential campaign. New York: Duell, Sloan and Pearce.

    Google Scholar 

  33. Lau, R. R., & Pomper, G. M. (2004). Negative campaigning: An analysis of U.S. Senate Elections. Lanham, Md.: Rowman and Littlefield Press. .

    Google Scholar 

  34. Leighley, J. E. (2004). Mass media and politics: A social science perspective. Houghton Mifflin Company, New York.

  35. Martin, P. S. (2004). Inside the black box of negative campaign effects: Three reasons why negative campaigns mobilize. Political Psychology, 25(4), 545–562.

    Article  Google Scholar 

  36. McGuire, W. J. (1969). The nature of attitudes and attitude change. In: G. Lindzey & E. Aronson (Eds.), The handbook of social psychology (2nd ed., pp. 136–314). Reading, Mass: Addison-Wesley. .

  37. McGuire, W. J. (1986). The myth of a massive media impact: Savagings and salvagings. Public Communication Behavior, 1, 173–257.

    Google Scholar 

  38. Memmott, M., & Drinkard, J. (2004). Election ad battle smashes record in 2004: Group questions the value of costly campaigns to USA. USA Today. November 24.

  39. Meirick, P. (2002). Cognitive responses to negative and comparative political advertising. Journal of Advertising, 31(1), 49–62.

    Google Scholar 

  40. Peterson, D. A. M., & Djupe, P. A. (2005). When primary campaigns go negative: The determinants of campaign negativity. Political Research Quarterly, 58(1), 45–54.

    Google Scholar 

  41. Pfau, M., Park, D., Holbert, R. L., & Cho, J. (2001). The effects of party- and PAC-sponsored issue advertising and the potential of inoculation to combat its impact on the democratic process. American Behavioral Scientist, 44(12), 2379–2397.

    Article  Google Scholar 

  42. Pfau, M., Holbert, R. L., Szabo, E. A., & Kaminski, K. (2002). Issue-advocacy versus candidate advertising: Effects on candidate preferences and democratic process. Journal of Communication, 52(2), 301–315.

    Article  Google Scholar 

  43. Pinkleton, B. E. (1997). The effects of negative comparative political advertising on candidate evaluations and advertising evaluations: An exploration. Journal of Advertising, 26(1), 19–29.

    Google Scholar 

  44. Pinkleton, B. E. (1998). Effects of print comparative political advertising on political decision-making and participation. Journal of Communication, 48(4), 24–36.

    Article  Google Scholar 

  45. Ridout, T. N., Shah, D. V., Goldstein, K. M., & Franz, M. M. (2004). Evaluating measures of campaign advertising exposure on political learning. Political Behavior, 26(3), 201–225.

    Article  Google Scholar 

  46. Schenck-Hamlin, W., & Procter, D. (2000). The influence of negative advertising frames on political cynicism and politician accountability. Human Communication Research, 26(1), 53–75.

    Article  Google Scholar 

  47. Scott, D. R., & Solomon, D. (1998). What is wearout anyway? Journal of Advertising Research, 27(5), 19–28.

    Google Scholar 

  48. Shaw, D. R. (1999). The effect of TV ads and candidate appearances on statewide presidential votes, 1988–1996. American Political Science Review, 93(2), 345–361.

    Article  Google Scholar 

  49. Shaw, D. R. (2006). The race to 270: The electoral college and the campaign strategies of 2000 and 2004. Chicago: University of Chicago Press.

    Google Scholar 

  50. Thorson, E., Christ, W. G., & Caywood, C. (1991). Effects of issue-image strategies, attack and support appeals, music, and visual content in political commercials. Journal of Broadcasting & Electronic Media, 35(4), 465–486.

    Google Scholar 

  51. Valentino, N. A., Hutchings, V. L., & Williams, D. (2004). The impact of political advertising on knowledge, internet information seeking, and candidate preference. Journal of Communication, 54(2), 337–354.

    Article  Google Scholar 

  52. Wattenberg M. P., & Brians, C. (1999). Negative campaign advertising: demobilizer or mobilizer?. American Political Science Review, 93(4), 891–899.

    Article  Google Scholar 

  53. West, D. (1994). Political advertising and news coverage in the 1992 California United States Senate Campaigns. Journal of Politics, 56(4), 1053–1075.

    Article  Google Scholar 

  54. Zaller, J. (1992). The nature and origins of mass opinion. New York: Cambridge University Press.

    Google Scholar 

  55. Zaller, J. (1996). The myth of massive media impact revisited. In: D. C. Mutz, P. M. Sniderman, & R. A. Brody (Eds.), Political persuasion and attitude change (pp. 17–78). Ann Arbor: University of Michigan Press.

    Google Scholar 

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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.



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).

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  • Political advertising
  • Elections
  • Campaign effects
  • Persuasion