Does Political Advertising Persuade?

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.

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.

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).

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.

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

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

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

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).

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.

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).

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.

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).

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.

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.