Do They Know What They are Doing? Risk Perceptions and Smoking Behaviour Among Swedish Teenagers

Transcription

1 The Journal of Risk and Uncertainty, 28:3; , 2004 c 2004 Kluwer Academic Publishers. Manufactured in The Netherlands. Do They Know What They are Doing? Risk Perceptions and Smoking Behaviour Among Swedish Teenagers PETTER LUNDBORG petter.lundborg@luche.lu.se BJÖRN LINDGREN Lund University Centre for Health Economics (LUCHE), Department of Community Medicine, Lund University, P.O. Box 705, Lund, Sweden Abstract Cross-sectional survey data on Swedish adolescents aged was used to analyse perceived risks of smokingrelated lung cancer, the determinants of these risk perceptions, and how these perceptions related to smoking behaviour. Three major conclusions were drawn: (1) that both smokers and non-smokers overestimated the risks of lung cancer, (2) that these risk perceptions fell substantially with age, but nevertheless implied risk overestimation, and (3) that individuals with higher perceived risks were less likely to be smokers but that risk beliefs had no effect on the number of cigarettes smoked. Keywords: smoking, risk perception, young people JEL Classification: D81, I10, J13 A number of studies have shown that adults have substantial awareness of the risks posed by smoking and, in fact, quite substantially overestimate the risks compared to scientific evidence concerning the actual risks (Viscusi, 1990, 1991; Liu and Hsieh, 1995; Antoñanzas et al., 2000; Rovira et al., 2000; Viscusi et al., 2000). Two studies also report similar findings for the age-group (Viscusi, 1990, 1991). The results also indicate that individuals do take their perceived risks into account when deciding whether to smoke or not. However, most smokers began smoking in their teens or even earlier, i.e. during ages not considered in the studies mentioned above. In Sweden, it is estimated that 8 out of 10 smokers became continuing smokers during their teens (SOU, 1999:137). For U.S. conditions, it is reported that initiation of smoking most often occurs between ages 11 and 15 with rather little further initiation after high-school (Johnston et al., 2001). The result that individuals overestimate smoking risks is not surprising, given the general tendency for people to overassess risks that are highly publicized, such as smoking hazards (Lichtenstein et al., 1978; Combs and Slovic, 1979; Viscusi, 1985). Biases in risk perceptions are often systematic, as has been frequently demonstrated in the literature on the psychology of risk perceptions (Lichtenstein et al., 1978; Fischhoff et al., 1981). A common result found is that individuals tend to overestimate low-probability events and underestimate high-probability events. 1 However, the health hazards of smoking can hardly To whom correspondence should be addressed.

2 262 LUNDBORG AND LINDGREN be characterised as low-probability events. Rather, the overestimation of smoking risks appears to be a result of the high publicity given to them. Also, the message communicated to the public is usually that smoking is very risky, without any references to objective risk levels. A common view, though, is that young people are generally unaware of the risks associated with smoking. A report by the World Bank states, for instance, that: most smoking starts early in life, and children and teenagers may know less about the health effects of smoking than adults, and: young people may be less aware than adults of the risk to their health that smoking poses (World Bank, 1999, ch. 3). It is also often believed that young people disregard the future health hazards of smoking when deciding to smoke: It is difficult for most teenagers to imagine being 25, let alone 55, and warnings about the damage that smoking will inflict on their health at some distant date are unlikely to reduce their desire to smoke (World Bank, 1999, ch. 3). Consequently, two important questions arise: (1) do young people understand the risks posed by smoking, and (2) do their risk beliefs influence their smoking decision? In Sweden, government and independent organisations have conducted a large number of anti-smoking campaigns, mainly aimed at teenagers. However, while smoking rates among Swedish adults have declined steadily since the beginning of the 1980s, the same pattern does not apply to teenagers smoking rates. Compared to the beginning of the 1980s, teenagers smoking rates have remained almost unchanged. In 1983, smoking rates among girls and boys aged 15 were 18 and 11 percent, respectively, compared to 16 and 11 percent in 2001 (CAN, 2002). 2 It should be noted that smoking rates among girls have been consistently higher than for boys. Economic research on the relationship between risk perceptions and consumption of addictive goods began with Viscusi (1990). Using data from a US national survey, Viscusi (1990) measured the perceived risks of smoking by asking respondents about their perception of the lung-cancer risk per 100 smokers. The findings suggested that smokers, former smokers, and non-smokers greatly overestimated the risks of lung cancer and that these risk perceptions in turn significantly affected the probability of smoking. Furthermore, in Viscusi (1991) the formation of risk perceptions was examined. The results were supportive of a Bayesian learning model in which the youngest age cohorts have higher risk perceptions than the population at large. Using the approach developed by Viscusi (1990), Liu and Hsieh (1995) examined smoking and risk perceptions in Taiwan. The results were similar to those obtained by Viscusi (1990, 1991) for US conditions, except for the fact that the degree of overestimation was somewhat lower in Taiwan. The perceptions of other risks than lung cancer associated with smoking, including heart disease, lung disease, and various risk measures for passive smoking, were examined in Antoñanzas et al. (2000), Rovira et al. (2000), and Viscusi et al. (2000), using a Spanish data set. The results suggested that these other risks were overestimated as well. The approach developed by Viscusi (1990) has also been applied to alcohol consumption among young people and the perceived risks of alcoholism with similar results (Lundborg and Lindgren, 2002). 3 Other studies addressing smoking and knowledge of risks include Kenkel (1991) and Hsieh et al. (1996). Kenkel (1991) found that knowledge about the health effects of smoking

3 RISK PERCEPTIONS AND SMOKING BEHAVIOUR AMONG SWEDISH TEENAGERS 263 wasfairly widespread but not complete. 4 Hsieh et al. (1996) used a methodology similar to Kenkel s (1991) to measure health knowledge in Taiwan. The results indicated that awareness of health hazards of smoking in Taiwan was far from complete. In both studies, smoking was found to be negatively related to health knowledge. However, both studies examined the knowledge of health hazards of smoking rather than the degree of underestimation or overestimation of the perceived risks of facing these health hazards. Even though individuals may be unaware of all health hazards associated with smoking, the risk of death from smoking may still be overestimated. The current study adds to the understanding of smoking-risk beliefs among teenagers. By utilising data on young people s smoking-risk beliefs and smoking behaviour, we are able to address explicitly the question of whether individuals have correct perceptions of the risks of smoking during the ages in which they most often initiate smoking and whether they respond to their risk beliefs within a decision context. The structure of the paper is as follows: First, using probability assessments, we will examine to what degree the smokingrisk beliefs of teenagers reflect the actual risks. The smoking-risk under consideration is the risk of lung cancer. Second, the determinants of lung-cancer risk beliefs will be examined and especially how these risk perceptions vary with regard to age, smoking experience and information sources. Third, we will examine whether these risk beliefs influence actual smoking behaviour. The study uses the approach developed by Viscusi (1990, 1991) and a set of Swedish cross-sectional survey data on 2618 individuals aged In Section 2 we describe the tobacco regulatory environment in Sweden. Section 3 presents our theoretical framework and Section 4 our data. After the description of the econometric models in Section 5, Section 6 reports on the results of the empirical analysis. The results suggest that Swedish teenagers quite substantially overestimate the lung-cancer risk, believing on average that 44 percent of all smokers will eventually develop lung cancer. As a comparison, the true lung-cancer risk, for U.S. conditions, is estimated to be in the range from 6 to 13 percent (Viscusi, 1998). Significant differences in lung-cancer risk beliefs were found between age groups, and perceived risks dropped with age. However, lung-cancer risks were overestimated in all age groups, for both smokers, former smokers, and non-smokers. The results do not support the view that teenagers in general are unaware of the risks posed by smoking. The regression analysis of the determinants of perceived risks of lung cancer revealed that age and being a smoker were negatively correlated with perceived risks, while being female and being born outside Sweden were positively correlated. Further, perceived lung-cancer risks showed a significant negative correlation with the probability of being a smoker. However, no correlation was found between the number of cigarettes smoked and perceived risks. Section 7 concludes the paper. 1. The tobacco regulatory environment in Sweden In Sweden, the efforts to provide smoking risk information and regulate the use, sales, and advertising of tobacco have shared many characteristics with the U.S. approach. The first research results regarding the negative health effects of smoking in Sweden were published already in the 1950s, and they gave rise to a public debate considering tobacco use. The first official information brochure, aimed at the general public, was published in 1960 by the

4 264 LUNDBORG AND LINDGREN Swedish Tobacco Monopoly, reporting, among other things, that carcinogenic substances had been identified in tobacco smoke. 5 The monopoly also started providing yearly financial support to research on the health effects of tobacco use (Magnusson and Nordgren, 1994). Beginning in 1963, government bodies began to sponsor information campaigns aimed at communicating research on the health consequences of smoking to the general public. During the 1960s, the information campaigns were mainly aimed at reducing tobacco use among children and young adults, and free information material was distributed to all schools in Sweden. In the 1970s, the information campaigns were to a larger extent also aimed at the adult population. A new law was introduced in 1977, which made on-product warnings and declaration of contents on cigarette packages mandatory. There were 16 rotating warnings in place. Typical warnings were as follows: Smokers are more often sick than non-smokers, Smokers face an increased risk of vascular diseases and some diseases in the blood-vessels, Smoking during pregnancy may damage the child, and Smoking damages the lungs! It starts with coughing and may end with lung-cancer or another lungdisease. Further, in 1979, a law that restricted tobacco advertising was introduced, which in practice meant that warnings and declarations of contents had to be included in the advertisements. The warnings were the same as those included on the cigarette packages. The law was further tightened in 1983, by restricting the use of tobacco in public places. The Swedish tobacco policy was made more coherent during the 1990s by the introduction of a special tobacco law. The law provided stricter rules for advertising, smoking in public places, warnings and declarations of contents, and a regulated maximum limit of tar in cigarettes. All packages now had to have two separate warning texts. On one side of the package, the text tobacco seriously endangers your health had to be included. On the other side, the package must contain one of the following eight warnings: Smoking causes cancer, smoking causes cardiovascular diseases, smoking causes life-threatening diseases, smoking kills, smoking during pregnancy harms your baby, protect children: don t allow them to breathe tobacco smoke, smoking harms those around you, and smoking causes addiction. In 1997, sales of cigarettes to people below the age of 18 were banned. Prior to 1997, no age restrictions were in place. In the same year, cigarette vending machines were banned. Two independent organisations, VISIR ( VI Som Inte Röker = We Who Do Not Smoke ) and A Non-Smoking Generation, were very active during the 1990s and conducted numerous campaigns at the national level, including campaigns in television and newspapers. In particular, A Non-Smoking Generation conducted a number of controversial campaigns, for instance, showing pictures of fake dead bodies and detailed pictures of smoking-related damage to the body (Torell, 2002). 2. Theoretical framework 2.1. The smoking decision model Following Viscusi (1990) and Liu and Hsieh (1995), we treat the discrete smoking decision as a lottery with a number of different payoffs. In this simple one-period model we will assume that there are two states of the world, life and death. The individual is unaware of

5 RISK PERCEPTIONS AND SMOKING BEHAVIOUR AMONG SWEDISH TEENAGERS 265 the actual probability attached to each pay-off and, thus, the actual probability is replaced by the perceived probability. Being alive, the individual reaps a discounted lifetime utility U(smoke) when deciding to smoke, and U(don t) when deciding not to smoke. In the state of death, the individual reaps a discounted lifetime utility U(smoke/die), and the perceived probability of premature death is π if a smoker and zero otherwise. In deciding whether to smoke or not, the individual will compare the expected utility of smoking with the expected utility of not smoking: Max[(1 π)u(smoke) + πu(smoke/die), U(don t)]. (1) The individual will choose to smoke if the expected utility of smoking exceeds the expected utility of not smoking: (1 π)u(smoke) + πu(smoke/die) > U(don t). (2) From (2) a latent variable that determines the consumption decision can be formed: I = [U(smoke) U(don t)] + π[u(smoke/die) U(smoke)]. (3) In (3), the first term on the right-hand side represents the net utility gain of smoking while the second term represents the expected cost of smoking. If I > 0, the individual will decide to be a smoker. If this condition does not prevail, the individual will not become a smoker. Put simply, the individual will decide to become a smoker if the perceived benefits of smoking exceed the perceived expected costs of smoking. The discrete-decision model above could easily be developed to include the continuous case. If the expected marginal benefit is positive but declines with the number of smoked cigarettes and if the expected marginal cost is positive but increases or is constant, then there is an equilibrium level of smoking-intensity that maximises the individual s discounted lifetime expected utility. An increase in the perceived probability of premature death will increase the expected cost of smoking, as can be seen from Eq. (3). Consequently, it is expected that perceived risk will be negatively related to smoking. Besides perceived risk, gender, age, and ethnicity are variables that may affect smoking behaviour. Such results have been found in a number of previous studies (Liu and Hsieh, 1995; Norton et al., 1998; DeCicca et al., 2000; Viscusi et al., 2000; Gaviria and Raphael, 2001). Some individuals play truant at school more frequently than others. This may indicate that these individuals are more present-orientated than others and care less about the long-run benefits of education. If this is the case, expected cost of present smoking will be lower for these individuals, since less weight will also be attached to the potentially negative future health effect of current smoking. Consequently, we included variables indicating the degree of truancy. A positive correlation between smoking and truancy is expected.

6 266 LUNDBORG AND LINDGREN Dramatic life experiences, such as parents divorcing, may affect substance use among teenagers. In the rational addiction framework, such experiences, as well as past substance use, may increase the addictive stock and thereby raise the marginal utility of substance use (Becker and Murphy, 1988). Therefore, having divorced parents, a proxy being living in a singe-parent household, may be positively related to smoking. However, living in a singleparent household could also result in lower household income and thereby lower income for the child. If this is the case, living in a single-parent household may be negatively related to smoking. Individuals having being engaged in sniffing of solvents or other substances, having felt a desire to try narcotics, or using smokeless tobacco, may be less risk averse than other individuals. They are therefore also more likely to participate in other risky behaviour as well, such as smoking. However, different substances may be substitutes or complements. If they are substitutes a negative correlation between them is expected The risk perception model We will use a Bayesian learning model to represent the formation of smoking risk perceptions. The model follows the parameterization outlined by Viscusi (1991) in which the individual is assumed to have three sources of information. First, the individual has a prior risk belief p, and an associated informational content ω 0. Second, the individual has his or her own experience regarding smoking. The individual may be a smoker, thereby having formed risk perceptions based on the observed health effects of his or her smoking, such as increased coughing. Experience of smoking may also be gained from observing the health effects of other individuals smoking. Individual experience will, thus, also be affected by factors such as age, gender, and consumption of other substances than cigarettes, since those factors may be related to exposure to smoking. Younger individuals, obviously, have been less exposed to smoking compared to older individuals. Individuals born outside Sweden may have been more exposed to smoking, since smoking is more common among people born outside Sweden. Furthermore, individuals that have used substances such as alcohol and smokeless tobacco are more likely to have been exposed to smoking. Gender may also be related to exposure to smoking, since smoking is more common among women than men. However, sex differences in attitudes towards risk is well documented and several studies have found that men tend to judge risks as smaller than women (for an overview see Slovic, 1999). The risk perception derived from experience is denoted q, and γ 0 is the associated informational content of experience. The third source of information consists of direct information transfer received, stemming, for instance, from education on alcohol, narcotics, and tobacco at school (ANT-education), newspapers and campaigns. Let r denote the risk that is described by direct information transfer, and let ζ 0 be the associated informational content. The fraction of the total informational content associated with p (prior risk belief) can then be written as: ω = ω 0 ω 0 + γ 0 + ζ 0, (4)

7 RISK PERCEPTIONS AND SMOKING BEHAVIOUR AMONG SWEDISH TEENAGERS 267 and analogously for γ 0 (own experience) and ζ 0 (ANT-education). The individual s riskperception function can then be written as the additive form: π = ω 0 p + γ 0 q + ζ 0 r ω 0 + γ 0 + ζ 0 = ωp + γ q + ζr. (5) Stated as above, the individual perception of risks associated with smoking is a weighted average of the three sources of information. In order to study the effect on perceived risk of a change in the information content of the information that the individual receives, for instance, coming from education on alcohol, narcotics, and tobacco, we may differentiate Eq. (5) with respect to ζ 0 : π ζ 0 = ω 0(r p) + γ 0 (r q) (ω 0 + γ 0 + ζ 0 ) 2 > 0 ifr > ω 0 p + γ 0 q ω 0 + γ 0. (6) According to (6), an increase in the information content of direct information transfer will cause the individual s risk perception to rise if the new information states a higher risk than the individual s prior belief and experience. Evidence from prior studies, however, suggests that (6) may be negative, since perceived smoking risks tend to be overestimated and thus information will serve to lower risk beliefs. 3. Data The cross-sectional data used in this study came from two surveys conducted in May 2000 and May 2001 by one of the present authors (Petter Lundborg) and a colleague (Martin Stafström) in Trelleborg, a medium-sized town on the south coast of Sweden. The questionnaires were handed out by us in the classrooms to pupils in grades 6, 7, 8 and 9inthe compulsory school and grades 1 and 2 in the upper secondary school and were to be filled in anonymously. The survey covered all schools in Trelleborg. Respondents were years old at the time of the survey. 6 In total, we collected 3253 questionnaires. Females constituted 50 percent of the population. For the variables used in the analysis, responses were incomplete for 635 individuals. Data on risk beliefs was incomplete for 310 individuals, or 10 percent of the sample, and those were, thus, excluded from the analysis. In addition, we excluded 325 individuals, due to missing responses to the various explanatory variables used. Accordingly, 2618 questionnaires, or 80 percent of the sample, were used for the analysis. An analysis of excluded individuals showed that non-respondents were somewhat more likely to have been born outside Sweden, to be smokers and consumers of alcohol, had felt a desire to try narcotics, and had sniffed solvents or other substances. Respondents excluded due to missing responses on the explanatory variables, but who had valid responses to the risk question, were, however, found to have risk perceptions similar to those of individuals included in the analysis. In order to measure risk beliefs, we used the approach developed by Viscusi (1990), in which individuals were asked to state how many of each group of a 100 smokers they thought would get lung cancer. We stated the question as follows: Out of a 100 smokers,

8 268 LUNDBORG AND LINDGREN how many do you think will get lung cancer due to their smoking? The response was then divided by one 100 in order to obtain the perceived probability of getting lung cancer. The mean response for the full sample was In the survey, two questions about individual smoking were asked. The first question was: Do you smoke? The alternatives were: (1) Yes, every day, (2) Yes, almost every day, (3) Yes, but only at parties, (4) Yes, but very rarely, (5) Did smoke but have quit, (6) Have only tested, (7) Have never smoked. When studying the participation decision we defined a smoker as an individual who reported smoking every or almost every day. Thus, a dummy variable was created, giving the value one to individuals reporting smoking every or almost every day and zero otherwise. In the sample, 14.4 percent reported smoking every or almost every day. The second question was stated as: If you answered that you smoke every day, how many cigarettes do you smoke per day? When studying the number of cigarettes smoked per day we, obviously, only considered daily smokers. The proportion of daily smokers was 11.2 percent in the sample. Socio-economic background data such as age, gender, and whether born in Sweden were used as explanatory variables. We coded age as 6 different dummy variables with the omitted category reference group being the oldest age group (ages 17 18). Other explanatory variables included were: use of smokeless tobacco, sniffing of solvents or other substances, desire to try narcotics, alcohol consumption, whether the respondent had received education on alcohol, narcotics, and tobacco at school (ANT-education), 7 truancy, whether the individual lived in a single-parent household, and a variable indicating which year the survey took place. The survey also included information, given by the respondents, about the employment status of their parents. However, these variables consisted of a high proportion of missing values and their inclusion in the analysis would result in a loss of another 479 observations. Since the employment status of parents variables were insignificant in the regressions and did not change the main results of the analysis, they were not included in the following analysis. Information concerning the income of the adolescent was also given. An additional 306 observations were lost when including the income variable. However, when included, the variable was never significant and its inclusion did not change the results. It was therefore not included in the following analysis. Descriptive statistics are shown in Table Econometric methods In order to examine the determinants of smoking-risk perception and its influence on smoking behaviour, we estimated three different equations. The first equation examines the determinants of lung-cancer risk perceptions. From Eq. (5), the formation of risk perception can be stated as a weighted average of three different sources of information. The risk-perception equation to be estimated can then be written as: RISK i = α 0 + α 1 X 1i + α 2 X 2i + α 3 X 3i + u i, (7) where X ji represents a vector of variables pertaining to each source j of information for individual i ( j = 1 represents prior risk perception, j = 2 individual experience, and j = 3 direct information transfer), α j is the associated vector of coefficients, and u i is a random

9 RISK PERCEPTIONS AND SMOKING BEHAVIOUR AMONG SWEDISH TEENAGERS 269 Table 1. Descriptive statistics. Variable Mean Standard deviation Lung-cancer risk Smoker (smokes every or almost every day) Daily smoker Former smoker Cigarettes per day (by daily smokers, n = 292) Female Born outside Sweden Ages Ages Ages Ages Ages Ages Living in a single-parent household Alcohol consumer User of smokeless tobacco Sniffed solvents or other substances Felt desire to try narcotics Never plays truant Plays truant a few times per semester Plays truant once a month Plays truant several times a month Plays truant once a week or more No ANT-education (ANT = alcohol, narcotics and tobacco) ANT-education roughly one hour during last semester ANT-education a couple of hours in total during last semester ANT-education roughly one day in total during last semester ANT-education a week or more during last semester Survey-year Observations 2618 term. Prior risk perception is not observable and we will, thus, assume that it is expressed in the constant term of the regression. 8 To reflect individual experience of smoking, we used the following variables: smoking status, experience of other addictive goods than cigarettes (alcohol consumption, use of smokeless tobacco, sniffing of solvents or other substances, and desire to try narcotics), and socio-economic variables (gender, whether born in Sweden, and age). The ANT-education variables were used to capture direct information transfer. Equation (7) was estimated using the ordinary least squares technique (OLS). 9

10 270 LUNDBORG AND LINDGREN The second equation to be estimated is the smoking-decision equation. For person i, it can be written as: SMOKER i = β 0 + β 1 Y 1i + β 2 RISK i + ε i. (8) The latent variable SMOKERi is not observable in practice, and, instead, we define a dummy variable, SMOKER i, taking the value 1 if SMOKERi > 0 and zero otherwise. Further, Y 1i represents a vector of explanatory variables, RISK i represents the risk perception variable, β j is the associated vector of coefficients, and ε i is a random term. Variables included in Y 1i were gender, whether or not the individual was born in Sweden, age, alcohol consumption, use of smokeless tobacco, sniffing of solvents or other substances, desire to try narcotics, truancy, and whether the individual lives in a single-parent household (see Table 1). Since SMOKER i is a binary variable, Eq. (8) was estimated using a probit-model. A potential simultaneity problem exists, since the smoker variable appears as an explanatory variable in Eq. (7) and the risk variable appears as an explanatory variable in Eq. (8). Therefore, we also estimated the equations simultaneously using the methods suggested by Maddala (1983, pp ) and Rivers and Voung (1988). Using the approach by Maddala, we first used a probit model to predict the smoker variable as a function of instruments and all exogenous variables. Next, we estimated Eq. (7) by OLS after replacing the smoker variable with its predicted value from the reduced-form equation. The asymptotic covariance matrix was then derived, using the formula in Maddala (1983, pp ). In Eq. (8), our dependent variable is binary and our potentially endogenous right-hand side variable, RISK, iscontinuous. In this situation, the two-stage conditional maximumlikelihood (2SCML) approach, developed by Rivers and Voung (1988), can be applied. First, we used ordinary least squares to predict the risk variable as a function of instruments and all exogenous variables. Second, we included the residuals from the reduced form regression in the probit Eq. (8) along with the original risk variable. The third equation to be estimated is the number-of-cigarettes equation. In this case, two characteristics of smoking are important to take into account. First, the number of cigarettes smoked per day can take only non-negative integer values. This fact makes the use of a count data model, such as a Poisson or Negative Binomial model, appropriate. Second, an important characteristic of smoking is that it can be regarded as a two-part decision-making process. First, the individual decides whether to become a daily-smoker or not, and second, conditional on being a smoker, he or she decides how many cigarettes to smoke. Neglecting these characteristics of smoking when deciding upon an econometric specification may lead to inconsistent parameter estimates. Therefore, we sought for count-data models taking into account the two-part character of the smoking decision. One such model is the hurdle model for count data as described by Mullahy (1986) and used by Gerdtham (1997), for instance. Applied to the case of smoking, a probit model is first used to estimate the probability of zero counts: Pr(cig i = 0 Z i ) = (Z i β), (9) where cig i denotes the number of cigarettes smoked daily, the standard normal distribution, Z i avector of explanatory variables that includes all explanatory variables used

11 RISK PERCEPTIONS AND SMOKING BEHAVIOUR AMONG SWEDISH TEENAGERS 271 in Eq. (8), and β the associated vector of coefficients. Second, a zero-truncated Negative Binomial model is specified to model the number of cigarettes smoked per day. The expected value of positive consumption, conditional on consumption being positive, can be written: ( ) E(cig i Z i, cig i > 0) = e Z i β 1. (10) 1 P 0 Here β is the estimated vector of coefficients and P 0 is the probability of observing zero counts. The term 1/(1 P 0 )isanadjustment factor that ensures that the probabilities of positive counts sum to one (Pohlmeier and Ulrich, 1995). The hurdle model allows the statistical process governing the decision to be a daily smoker or not to be different from the process governing the number of cigarettes smoked conditional on being a daily smoker. Estimates of the parameter vectors can be obtained by separate maximization of the two log-likelihood functions (Mullahy, 1986). In order to check the robustness of the results, we also tested an alternative specification, the zero-inflated Negative Binomial model as described by Greene (1997, pp ). 5. Results 5.1. Smoking-risk perceptions Table 2 presents mean risk perceptions for the full sample and for different age groups and smoking statuses. For the full sample, the mean value of RISK was Table 2 reveals that for the youngest individuals (ages 12 13), the mean value of RISK for the full sample was significantly higher than mean RISK for all the other age groups, except the next to youngest (ages 13 14), where the difference was not statistically significant. Certainly, a Table 2. Variations in mean risk perception with age and smoking status. Mean of risk perception (sd.) by age group. Age group All respondents Smokers Former smokers Non-smokers Ages (0.283) (0.410) (0.287) (0.282) Ages (0.289) (0.309) (0.125) (0.291) Ages (0.294) (0.308) (0.259) (0.294) Ages (0.291) (0.295) (0.356) (0.286) Ages (0.272) (0.254) (0.269) (0.277) Ages (0.271) (0.254) (0.307) (0.274) All ages (0.289) (0.272) (0.294) (0.288) Men, all ages (0.291) (0.267) (0.324) (0.292) Women, all ages (0.277) (0.271) (0.255) (0.277) Observations

12 272 LUNDBORG AND LINDGREN clear pattern emerges where perceived risks drop off with age, although the differences between certain age groups were not always statistically significant. 10 The same pattern, including significant differences, was found for the non-smoker category. To some extent, this pattern could also be found for smokers. However, due to the low number of smokers in the youngest age groups, no statistically significant differences in mean RISK across age groups could be found. 11 The number of former smokers was low in all age groups, and consequently no significant differences in mean RISK across age groups were found. Significant differences in mean RISK between smokers and non-smokers and between smokers and former smokers were found for the full sample. Smokers were found to have lower mean risk than both former smokers and non-smokers. This pattern holds for all age groups except for the age groups and 14 15, where former smokers were found to have lower mean risk than smokers. The differences were not, however, statistically significant when each age group was examined separately. Perceived risks were found to be significantly higher among females than males. We obtained this result both for the full sample and across the different smoking categories. Compared to scientific evidence concerning the actual risks of lung cancer, the results suggest that individuals in all age groups overestimate the risks of smoking-related lung cancer. For U.S. conditions, the true lung-cancer risk has been estimated to be in the range from 0.06 to 0.13 (Viscusi, 1998). 12 In Table 3 the percentage of the sample at different intervals of risk beliefs is shown. The calculations are made for the full sample, smokers, former smokers, and non-smokers. Looking first at the column for the full sample, it shows that 10 percent believed that the risk of lung cancer from smoking is less than 10 percent. Thus, only a small proportion of the sample underestimated the risk although the exact proportion depends on the objective risk measure used. This proportion was somewhat larger among smokers, 16.5 percent, Table 3. Distribution of lung-cancer risk perception. Distribution of lung-cancer All Former risk perception respondents Smokers smokers Non-smokers Risk < Risk < Risk < Risk < Risk < Risk < Risk < Risk < Risk < Risk < Risk < Risk = Observations

13 RISK PERCEPTIONS AND SMOKING BEHAVIOUR AMONG SWEDISH TEENAGERS 273 Table 4. Cigarettes smoked per day as a function of risk perceptions. Only daily smokers considered. Lung-cancer risk Mean of cigarettes Standard perception smoked per day deviation N Risk < Risk < Risk < Risk < Risk < Risk < Risk < Risk < Risk < Risk < Risk < Risk = Total but quite similar across the other categories. Interestingly, 3.5 percent of the smokers believed that the probability of developing lung cancer was equal to 1, yet they chose to smoke. An explanation may be that these individuals plan to quit before they face the risk of lung cancer. Alternatively, they may heavily discount future health consequences of smoking. Table 4 shows the number of cigarettes smoked by daily smokers at different intervals of risk beliefs. The first column shows the perceived risk broken into different intervals. In the second column, for each lung-cancer risk interval, the mean number of cigarettes smoked per day is shown. The third and fourth columns show the standard deviation and the number of observations in each interval. Interestingly, the mean number of cigarettes smoked per day is almost constant across the different intervals. Only for the next-to-highest risk interval (0.90 Risk < 1) is the mean number of cigarettes smoked less than in the other intervals. However, the number of individuals in this particular interval is very small, indeed (n = 1). An interesting issue is whether the observed patterns of smoking risk beliefs are specific for smoking or if the patterns are more general and can be found for other risk-taking activities as well, such as drinking. Since the data set contains information also on alcoholism risk-beliefs, we were able to examine whether the perceptions of smoking and alcohol risks followed each other. The measure of alcoholism-risk beliefs was obtained by asking respondents how many in a group of 100 school pupils they thought would become alcoholics sometime during their lifetime. Table 5 presents mean alcoholism-risk beliefs at given intervals of lung-cancer risk beliefs. Clearly, perceived alcoholism-risk increased with perceived lung-cancer risks. 13

14 274 LUNDBORG AND LINDGREN Table 5. Mean risk-perception of alcoholism, conditional on different levels of lung-cancer risk perception. Lung-cancer risk perception Alcoholism-risk perception Risk < Risk < Risk < Risk < Risk < Risk < Risk < Risk < Risk < Risk < Risk < Risk = Total Determinants of smoking-risk perceptions Table 6 reports the results for the lung-cancer risk-perception equation using three different specifications. In order to examine the robustness of the results, the first equation in Table 6 omits the smoker variable. Significant age differences in risk perceptions were found. Compared to the omitted reference group (ages 17 18), risk perceptions were significantly higher for the age groups 12 13, and Females were found to have significantly higher risk perceptions than males. Individuals born outside Sweden had significantly higher risk perceptions than Swedish-born individuals. ANT-education did not affect risk perceptions in any significant matter. 14 Among the variables measuring risky behaviour other than smoking, only being an alcohol consumer showed a significant correlation with risk beliefs. The effect was to lower the perceived risk of lung cancer. The variable indicating which year the survey took place showed a small but significant positive effect on risk perception. The second equation in Table 6 includes the smoker variable. The effect of being a smoker wastolower the perceived risk probability by 0.05 compared to being a non-smoker. 15 The inclusion of the smoker variable did not change the significance or magnitudes of the other variables to any large extent. The third equation shows the results where the smoker variable is treated as an endogenous variable. 16 However, a Hausman test for endogeneity could not reject the hypothesis that the smoker variable was exogenous (Hausman, 1978).

15 RISK PERCEPTIONS AND SMOKING BEHAVIOUR AMONG SWEDISH TEENAGERS 275 Table 6. Risk-perception equations. Ordinary least squares. Coefficients a (std. errors) Independent variables (1) b (2) c (3) d Female (0.013) (0.013) (0.013) Born outside Sweden (0.020) (0.020) (0.021) Ages (0.017) (0.018) (0.018) Ages (0.022) (0.022) (0.019) Ages (0.023) (0.023) (0.023) Ages (0.027) (0.027) (0.026) Ages (0.021) (0.021) (0.029) ANT-education roughly one hour during (0.014) (0.014) (0.015) last semester ANT-education a couple of hours in total (0.016) (0.016) (0.018) during last semester ANT-education roughly one day in total (0.019) (0.019) 0.06 (0.021) during last semester ANT-education a week or more (0.020) (0.020) (0.022) during last semester Alcohol consumer (0.014) (0.014) (0.022) User of smokeless tobacco (0.022) (0.022) (0.025) Sniffed solvents or other substances (0.028) (0.029) (0.029) Felt desire to try narcotics (0.016) (0.017) (0.021) Survey-year (0.014) (0.014) (0.013) Smoker (0.016) (0.016) Constant (0.041) (0.054) Observations R Notes: significant at 5% level; significant at 1% level. a Robust standard errors in parentheses. b Smoker variable omitted. c Smoker variable included. d Smoker variable treated as endogenous Smoking-participation equation Table 7 presents two different specifications of the smoking-participation equation. The first equation excludes the potentially endogenous risk variable in order to examine the robustness of the results. Marginal effects are reported. In both specifications, females had a significantly higher probability of being smokers. Furthermore, individuals born outside Sweden were more likely to be smokers than Swedish-born individuals. Regarding age effects, only the youngest age group (ages 12 13) were significantly less likely to be smokers compared to the omitted reference group (ages 17 18). Plays truant had a strong,

16 276 LUNDBORG AND LINDGREN Table 7. Smoking participation. Probit model. Marginal effect a (std. errors) Independent variables (1) b (2) c Female (0.012) (0.012) Born outside Sweden (0.020) (0.020) Ages (0.019) (0.020) Ages (0.016) (0.016) Ages (0.019) (0.019) Ages (0.017) (0.017) Ages (0.013) (0.013) Plays truant a few times per semester (0.017) (0.017) Plays truant once a month (0.038) (0.038) Plays truant several times a month (0.049) (0.049) Plays truant once a week or more (0.051) (0.051) Living in a single-parent household (0.013) (0.013) Alcohol consumer (0.012) (0.012) User of smokeless tobacco (0.029) (0.029) Sniffed solvents or other substances (0.033) (0.034) Felt desire to try narcotics (0.021) (0.021) Survey-year (0.014) (0.013) Lung-cancer risk (0.017) Observations McFadden s R McKelvey and Zavoina s R Notes: significant at 5% level; significant at 1% level. a Robust standard errors in parentheses. b Lung-cancer risk omitted. c Lung-cancer risk included. significant, and positive effect on smoking probability, and the magnitude of the effect grew larger with the degree of truancy. Individuals living in a single-parent household, a proxy for having divorced parents, were significantly more likely to be smokers. All of the variables measuring risky behaviour, i.e. alcohol consumption, use of smokeless tobacco, and sniffing of solvents or other substances, were significantly positively correlated with smoking. This may reflect the fact that these substances are complements to smoking. Also, having felt a desire to try narcotics showed a significant positive effect on the probability of smoking. The second equation in Table 7 includes the risk variable. The variable was significantly negatively correlated with smoking and reduced the smoking probability by Inclusion of the risk variable did not alter the significance and magnitude of the coefficients of the other variables to any large extent. We also tested the interactions between

17 RISK PERCEPTIONS AND SMOKING BEHAVIOUR AMONG SWEDISH TEENAGERS 277 risk perceptions and the age variables. No evidence was found, however, for differential responses to risk perceptions across age groups, as the interaction variables were insignificant. Further, we found no evidence for differential effects of risk perceptions on smoking participation across gender, since the interaction between being female and risk was insignificant. Estimations were also carried out treating risk as an endogenous variable, using the method of Rivers and Voung (1988). 17 However, only one instrument was significant in the first-stage regression, and an F-test of the hypothesis that the instruments were jointly equal to zero could not be rejected, indicating a problem of weak instruments. Consequently, using the two-step probit model in this case seems inappropriate and further tests for overidentification and endogeneity might be severely biased. As expected, the estimate of the risk variable from the two-step probit model was very imprecise, and therefore it is not presented. Unfortunately, it is not possible to predict the direction of the potential bias in the estimate of risk. If reverse causality is present, i.e. higher perceived risk reduces the probability of smoking and smoking reduces perceived risk, the negative effect of perceived risk on smoking probability is likely to be overstated. However, since the model contains more than one independent variable, the direction of endogeneity bias becomes less clear and will depend on several other factors (Ettner, 1996). Further, measurement errors in the risk variable may lead to underestimation of the effect Daily-smoker equation In order to identify a correct specification of the daily-smoker equation, we carried out a number of tests. First, we tested a Poisson model against a Negative Binomial model. The LR-test statistic was [χ 2 (1)] , which was significant at the 1% level. Thus, the Poisson model was rejected in favour of the Negative Binomial model. The alpha parameter was highly significant, which indicated substantial overdispersion in the data. Estimated marginal effects from the Negative Binomial model are shown in the second column of Table 8. Second, we tested the Negative Binomial model against the hurdle Negative Binomial model using a LR-test. The test statistic was [χ 2 (19)] , which was significant at the 1% level, thus rejecting the Negative Binomial model in favour of the hurdle Negative Binomial model. This result calls attention to the importance of allowing for different processes to drive the decisions in a multi-decision framework. The estimated marginal effects from the second stage of the hurdle Negative Binomial model are shown in the third column of Table Clearly, restricting the two processes governing (a) the decision to be a daily smoker and (b) the decision regarding the number of cigarettes to smoke per day to be identical might lead to serious misinterpretations, since the risk variable was significantly negative in the Negative Binomial model but insignificant in the second stage of the hurdle Negative Binomial model. Hence, the risk variable influenced the participation decision but not the decision regarding the number of cigarettes to smoke per day. 19 However, the sample of daily smokers in the second stage of the hurdle equation was small (n = 292). Not many of the other variables were significant, but it should be noted that being a female had a significant negative effect on the number of cigarettes

18 278 LUNDBORG AND LINDGREN Table 8. Daily-smoker equation. Marginal effect (std. errors) Negative binomial 2nd stage of hurdle negative Independent variables model a binomial model Female (0.07) (0.810) Born outside Sweden (0.228) (0.986) Ages (0.118) (0.986) Ages (0.098) (1.136) Ages (0.072) (1.248) Ages (0.071) (5.043) Ages (0.109) (4.511) Plays truant a few times per semester (0.129) (1.132) Plays truant once a month (0.382) (1.322) Plays truant several times a month (0.690) (1.310) Plays truant once a week or more (1.162) (1.383) Living in a single-parent household (0.072) (0.727) Alcohol consumer (0.085) (1.856) User of smokeless tobacco (0.195) (0.995) Sniffed solvents or other substances (0.118) (0.927) Felt desire to try narcotics (0.121) (0.731) Survey-year (0.085) (0.757) Lung-cancer risk (0.167) (1.295) Observations Pseudo R Notes significant at 5% level; significant at 1% level. a Robust standard errors in parentheses. smoked. We also tested the interaction between being female and risk and between grade and risk. However, none of these interaction terms were significant. We also tested a truncated Poisson model against the truncated Negative Binomial model. The LR-statistic of χ 2 (1) , which was significant at the 1% level, indicated that the truncated Poisson model must be rejected in favour of the truncated Negative Binomial model. In addition to the hurdle Negative Binomial model, we also estimated a zero-inflated Negative Binomial model. 20 However, the results were similar to those obtained by the hurdle Negative Binomial model Sensitivity analysis The concept of lung-cancer risk may be hard to understand for the youngest age groups in our analysis or it may even be unknown to them. Therefore, the risk question may