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Illustration by Michael Gibbs
But does all that spending work? After all, as any parent will testify, it can be difficult getting through to teenagers. So we decided to investigate whether the target audience of the advertising – adolescents – was listening. Fortunately, there were good data available. Before it aired the ads, the PDFA began conducting annual surveys to independently test whether the advertising campaign was associated with a change in adolescents’ drug use. These were known as the Partnership Attitude Tracking Surveys (PATS) and were obtained by getting teenagers to fill out anonymous questionnaires at central locations like malls. The first “wave” of PATS was initiated during February and March, 1987, three months before the first anti-drug messages were aired. Additional waves, which took place in 1988, 1989, and 1990, measured respondents’ recall of PDFA advertisements. (The sample sizes of adolescents aged 13–17 years were 797, 1031, 870, and 1497, respectively.) These four waves formed a “natural experiment.” Respondents during the first wave were not exposed to PDFA advertising, whereas respondents in subsequent waves were. A preliminary examination of the PATS data reveals that the percentages of respondents who reported marijuana or cocaine/crack use in the previous 12 months did, in fact, decrease significantly between 1987 and 1990. Survey data from the University of Michigan’s Institute of Social Research and National Household Survey on Drug Abuse corroborate this trend. But while this pattern is consistent with the hypothesis that anti-drug advertising reduces drug consumption, this analysis does not accommodate other potential explanations for changes in drug consumption over time, such as exposure to school-based anti-drug campaigns. To adjust for such other factors, we developed a detailed behavioral economic model that investigated the relationship between adolescents’ recall of anti-drug advertising and their probability of using marijuana, cocaine, or crack – as well as the volume of use for those already using these drugs.
Model Behavioral We began with an individual-level behavioral economic model of drug use, focusing on the impact of advertising. This well-established economic framework provided the rigorous link between the underlying theory and the statistical model needed to estimate individual behaviors. We then relied on health behavior theory to select the specific variables used within this empirical specification. The measures used in the analysis represented the predominant benefits and costs of drug use identified in major health behavior theories. We analyzed marijuana use separately from cocaine/crack use because reasons for use differ for specific drugs. And we combined cocaine and crack into a single category because 92% of respondents reported using both with equal frequency. Respondents indicated how often in the past 12 months they had used each drug by selecting a number on a scale running from 1 – meaning no use – to 7 – meaning 40 or more times. These responses allowed us to determine both the percentages of respondents who reported using each drug in the previous 12 months and the volumes of use. In the case of users of both drugs, we divided their volume of use at the median and considered those below the median to be light users and those above the median to be heavy users.
The probabilities of a respondent’s reporting use of marijuana and cocaine/crack over the previous 12 months were expressed in a standard “probit” formulation as a function of both the attributes of the individual (e.g., demographic characteristics) and his or her attitudes towards drugs and drug users, and perceptions of drug use itself (e.g., perceived severity). We considered three versions of this formulation, each of which involved a slightly different assumption about the relationship between the cocaine/crack and marijuana use decisions.
An Independent Choice? First, we estimated the marijuana and cocaine/crack equations independently, assuming that the decision to try the two drugs is independent. (Empirical research suggests that the process may be sequential; that is, one first tries marijuana and then cocaine/crack.) Second, the common syndrome theory suggests that individuals have a “predisposition” to use drugs that manifests itself first in marijuana use. Third, certain factors associated with the experience of using marijuana could lead people to use harder drugs, such as cocaine/crack. This has been referred to as a “gateway” or “stepping stone” theory. These three alternatives resulted in different statistical specifications, which allowed us to test the hypotheses with the available data. In addition to the “use” choice, we investigated the decision regarding how much to use (the “volume” decision), given that an individual has reported using marijuana or cocaine/crack. For this analysis, individuals were categorized as “light” or “heavy” users. The result is a classic sequential-choice decision: an individual uses the drug and then, on the basis of his or her experience and additional information (e.g., anti-drug advertising), decides whether or not to use the drug again. Accordingly, for each drug, we initially estimated stage one probability equations and then estimated the probability of a given individual’s being a light or heavy user conditional on previous use. Thus, including only those who had previously used drugs, we estimated each second-stage equation using a dichotomous dependent variable indicating heavy or light usage. The first “wave” of PATS (conducted before the initiation of anti-drug advertising) provided us with the data necessary to assess the determinants of drug use in the absence of PDFA advertising. This was the “control” in our natural experiment. We were then able to assess the significance of recall of PDFA advertising in terms of use and volume decisions via a series of “treatment” groups consisting of each of the subsequent waves exposed to advertising. We began by estimating the three sets of probability-of-use equations (“independent,” “gateway,” and “predisposition”) using the wave one data for marijuana and cocaine/crack. Then, on the basis of the best fitting of these equations, we estimated the second stage regressions for the probability of being a light vs. heavy user, also using the wave one data. This provided us with a detailed analysis of the factors influencing the decision to use and the volume of use for each drug before the commencement of PDFA advertising. So what did we find? Using nested tests, we concluded that the “predisposition” formulation – i.e. that individuals have a “predisposition to use drugs” that manifests itself first in marijuana use – fit significantly better than the notion that the decision to try the two drugs is independent. Consequently, we used this formulation throughout. In addition, the data led us to reject the hypothesis that marijuana use increases the probability of cocaine/crack use. To be sure, individuals who have used marijuana in the past are indeed more likely to use cocaine/crack. But the reason is that – statistically speaking – individuals who are predisposed to try marijuana are also predisposed to try cocaine/crack.
Does Anti-Drug Advertising Work? This analysis, conducted with the wave one “control” group, provided the basis for analyzing the significance of recall of PDFA advertising in waves two, three, and four. The findings demonstrate that recall of anti-drug advertising was associated with a decreased probability of marijuana use. The advertising coefficients in the marijuana use equation were all statistically significant and of the “correct” sign. In the case of cocaine/crack use, the advertising variables were also significant in waves two through four. The estimated advertising coefficients in the volume portion of our results were all statistically nonsignificant with the exception of the wave four marijuana volume-of-use equation. This suggests that recall of PDFA’s anti-drug advertising had little or no impact on the volume of use among existing users.
To ensure that the negative advertising coefficients imply that recall of advertising leads to lower marijuana and cocaine/crack use and are not due to the omission of variables like exposure to other anti-drug programs, we examined the correlation between the advertising-recall variable and the estimated equation error. This correlation was found to be statistically nonsignificant for each equation, suggesting that omitted-variable bias was not a significant problem. Finally, we estimated the marginal impact of the advertising-recall variable to determine the change in the probability of use associated with a 1-point change in advertising recall, with recall being rated on a three-point scale. We estimated the cumulative impact on use probability given a particular wave’s level of advertising awareness by subtracting the average predicted probability of use in the absence of PDFA advertising from the average predicted probability given the level of recall generated by PDFA advertising in that wave. The marginal effects of PDFA advertising on the probability of drug use were significantly greater for marijuana than for cocaine/crack across each wave. The cumulative effects suggest that, after three years of PDFA advertising, approximately 9.25 percent fewer adolescents were using marijuana and 3.6 percent were using crack/cocaine. Our results are consistent with the hypothesis that anti-drug advertising reduces the probability of marijuana and cocaine/crack use among adolescents. However, our results also suggest that recall of anti-drug advertising is not associated with adolescents’ decisions regarding how much marijuana or cocaine/crack to use among those already using each drug. This study was not without limitations. Although the sample was constructed to be representative of American adolescents, central-location sampling was used. It is also possible that respondents were exposed to other anti-drug intervention programs in addition to their exposure to anti-drug advertising. However, past research has demonstrated that these alternative programs have been largely ineffective. Despite these potential limitations, our findings have important public policy implications. Our model, based on survey data from 1987 to 1990, indicates that increases in amounts of anti-drug advertising are associated with decreases in teenage drug use. During this time period, media financial support for anti-drug advertising increased, from a low of $115 million in 1987 to a high of $365 million in 1991. Given the results, this increase appears to have been a worthwhile investment. A longer version of this research appeared in the American Journal of Public Health, August 2002, Vol 92, No. 8. Lauren G. Block is associate professor of marketing at the Zicklin School of Business at Baruch College. Vicki G. Morwitz is associate professor of marketing at NYU Stern. William P. Putsis Jr is professor of marketing at University of North Carolina at Chapel Hill. Subrata K. Sen is Joseph F. Cullman III Professor of Organization, Management, and Marketing at the Yale School of Management. |
ver the years, advertisements
run by the Partnership for a Drug-Free America (PDFA) have turned
into popular culture icons.
Spots like “This is your brain . . . this is your brain on drugs” have
become part of the lingua franca. Over the years, PDFA, a non-profit
started in 1986 and backed by the American Association of Advertising
Agencies, has received more than $3 billion in donated media from the
broadcast, cable, and radio networks, more than 1000 newspapers, and
more than 100 magazines and medical journals. The massive amount of
donated media PDFA receives annually makes it the largest advertiser
of a “single product” in the United States – after
McDonald’s.
he PATS surveys also included questions related to a variety of factors
associated with drug usage. We used responses to these questions as
input to our model. Perceived susceptibility was measured by asking
respondents to rate three items (on 4-point scales) indicating the
degree to which people risk harming themselves by using drugs. Perceived
severity was measured by having respondents rate four items (on 4-point
scales) indicating the degree to which they would fear the consequences
of being caught with drugs. Attitudes toward drugs were measured by
having respondents indicate their level of agreement with 14 items
(on 5-point scales) describing benefits of drug use. Attitudes toward
drug users were measured by having respondents indicate whether each
of 27 personality characteristics would describe a marijuana, cocaine,
or crack user. Other factors measured included peer pressure, and how
difficult it was to obtain drugs. Finally, respondents were asked to
read a short description of six advertisements that were aired nationally,
and to indicate how often they had seen each advertisement.