Teenage Indulgence in Cigarettes, Alcohol and Marijuana: Evidence of a "Gateway" Effect Bisakha Sen University of Central Florida Rajshree Agarwal University of Illinois at Urbana Champaign Richard Hofler University of Central Florida Abstract We examine the possible existence of a gateway effect between the consumption of three different substances cigarettes, alcohol, and marijuana among adolescents. A gateway effect exists when consumption of one substance increases the likelihood of subsequent initiation of consumption of other substances. We find evidence that smoking and/or alcohol consumption serve as gateways for initiating marijuana use, and each of smoking and alcohol use serve as gateways for initiating the other. After controlling for unobserved heterogeneity, we also find that marijuana use serves as a gateway for initiating alcohol and smoking. The results thus indicate complementarities in the use of addictive substances, and suggest that policies aimed at preventing adolescents usage of one addictive substance can have beneficial effects of reducing adolescents initiation of other addictive substances. Published: 2002 URL: http://www.business.uiuc.edu/working_papers/papers/02 0103.pdf
Teenage Indulgence in Cigarettes, Alcohol and Marijuana: Evidence of a Gateway Effect January 2002 Bisakha Sen Department of Economics University of Central Florida Orlando, FL 32816-1400 Phone: 407-823-2232 Email: bisakha.sen@bus.ucf.edu Rajshree Agarwal Department of Business Administration University of Illinois at Urbana-Champaign Champaign, IL 61820 Phone: 217-265-5513 Email: agarwalr@uiuc.edu Richard Hofler Department of Economics University of Central Florida Orlando, FL 32816-1400 Phone: 407-823-2606 Email: richard.hofler@bus.ucf.edu
ABSTRACT We examine the possible existence of a gateway effect between the consumption of three different substances cigarettes, alcohol, and marijuana among adolescents. A gateway effect exists when consumption of one substance increases the likelihood of subsequent initiation of consumption of other substances. We find evidence that smoking and/or alcohol consumption serve as gateways for initiating marijuana-use, and each of smoking and alcohol use serve as gateways for initiating the other. After controlling for unobserved heterogeneity, we also find that marijuanause serves as a gateway for initiating alcohol and smoking. The results thus indicate complementarities in the use of addictive substances, and suggest that policies aimed at preventing adolescents usage of one addictive substance can have beneficial effects of reducing adolescents initiation of other addictive substances. 1
1. Introduction In his introductory chapter, Gruber (2001) states The simple fact that we can explain so little of the time series trends over the past decade highlights the importance of future investigations of risky behavior, and calls for future research on how these risk-taking decisions fit together for more than two behaviors. This research attempts to increase our knowledge about factors influencing risky behaviors by investigating their inter-temporal relationship with each other, while controlling for individual, parental and neighborhood effects. Several empirical studies in the health sciences find evidence of a consistent pattern of sequencing in consumption of addictive substances (Kandel, 1975; Ellickson et al, 1992; Kandel & Yamaguchi, 1993). Initiation of alcohol and cigarette use is typically followed by marijuana use, which is then followed by other harder drugs. Prevalence of these patterns has led to the gateway hypothesis, whereby it is conjectured that some addictive substances serve as gates through which consumers move to other addictive substances. We present an empirical analysis for the gateway effect among adolescents by employing alternate models. We attempt to control for effects of observable economic and demographic characteristics and unobserved heterogeneity. The next section presents a brief literature review and the model. The methodology and data are described in Section 3; the empirical analysis is presented in Section 4, followed by conclusions in Section 5. 2. Literature Review and Conceptual Framework Many studies in the economic literature find evidence of contemporaneous correlation between consumption of different addictive substances like alcohol and marijuana ( DiNardo & Lemieux, 1992; Model, 1993; Chaloupka & Laixuthai, 1997), and alcohol and cigarettes (Farrelly at al., 1999; Dee, 1999). However, to our knowledge, only Pacula (1997a, 1998) considers inter- 2
temporal correlation in substance consumption among young adults, and no study specifically considers whether consumption of one substance increases the subsequent likelihood of initiation of consumption of other substances (what may be considered a true gateway effect ). Pacula uses the 1983-84 waves of the NLSY97 and investigates the effects of prior period s prices on current period s consumption, based on the simple demand theory that if prior period s prices of substance B negatively (positively) affect current period s consumption of substance A, then the substances are inter-temporal complements (substitutes). However, a cross-sectional analysis using past year s consumption of substances may not be appropriate for testing the gateway effect, i.e. initiation of consumption. While last year s consumption of substance B might increase this year s consumption of substance A, this gives no indication that initiation of consumption of substance B actually preceded initiation of consumption of substance A. Our study specifically addresses this issue. Our model is based on that developed by Pacula (1997b), who extended the rational addiction model of Becker and Murphy (1998) 1 to include multiple substances. The utility function is assumed separable in general consumption (of a composite good C t price normalized to 1) and in consumption of addictive substances. The model considers two addictive substances, A t and B t, and S t represents the consumption capital stock namely the user s experience with past consumption of these same substances. The utility function to be maximized is: T β t [U(C t ) + b t (Z t ).V (A t, B t, S t )] where S t = (A t-1, B t-1 ), (1) t= 1 U >0, U <0, V A, V B 0, V AA, V BB <0, V S 0. subject to the standard lifetime budget constraint. b t is a function of observables (Z t ) that affect marginal utility from consuming addictive substances, and β t is the future discount factor. 1 Becker and Murphy s (1998) rational addiction model is particularly well-known. Earlier works on rational addiction include those by Phlips (1974), Marcel (1978; 1981) and Spinnewyn (1981). 3
In context of the gateway effect, we focus on the initiation rather than the continuance of substance consumption. An individual will initiate consumption of a drug in period t if the marginal utility of consumption, evaluated at zero consumption, is greater than the marginal cost (P A, t ) : (U ) -1 {b t (Z t ) V A, t + b t (Z t ) T β j V S, t+j S A, t } A=0, T=t P A, t >0 (2) j= 1 T β j V S,t+j S A,t j= 1 is the negative effect on future utility/well-being of increasing consumption capital stock of A in period t. The extent to which this affects the initiation of A depends on the magnitude of β, which depends upon the rate of time preference. Chaloupka (1991) finds empirical evidence that 17-24 year olds tend to discount the future more heavily than 25-64 year olds, and behave myopically when making decisions about consuming addictive substances. 2 Since our data cover the population aged 12-16 in 1996, we therefore assume that they behave myopically. Specifically, we assume that their rate of time preference is high enough (i.e., β is low T enough) that the b t (Z t ) β j V S, t+j S A, t term in equation (2) is of negligible magnitude. 3 Hence, we rewrite eq. (2) as: j= 1 (U ) -1 { b t (Z t ) VA, t } A =0, T=t P A, t >0. (3) The presence of a gateway effect exists if the instantaneous marginal utility from initiating consumption of A, that is {V A, t } A =0, is positively impacted by past consumption of B. Thus, the condition for a gateway effect to exist is ( 2 V t / A t S t ). ( S t / B t-1 ) A =0 >0. (4) 2 Specifically, Chaloupka finds that the effects of future consumption on present consumption are insignificantly different from zero for 17-24 year olds, while they are positive and significant for 25-64 year olds. 3 For a short discussion of the possible effects of assuming r=0, i.e. myopic rather than rational behavior, please contact the authors. 4
Note that it is also possible for a counter gateway effect to exist if ( 2 V t / A t S t ). ( S t / B t-1 ) A =0 <0. (5) This implies that the hazard rate, i.e. the probability of indulging in substance A in time period t conditional on not having indulged in it until then, is a function of both the observable factors (Z t ), and whether there is B t in the stock of past consumption capital. i.e. h A (t) = Prob (A t >0 Z t, S t (B t ); A k = 0, k = 1, 2, t-1) (6) 3. Data Description and Empirical Methodology We use data from the first round of the National Longitudinal Survey of Youth, 97 (NLSY97). The data consist of a sample of 6,748 respondents who are representative of the U.S. population aged 12-16 years on Dec. 31, 1996, and a supplemental over-sample of 2236 Hispanic and black people of the same age group. A self-administered part of the survey asks respondents questions about use of three substances, alcohol, cigarettes, and marijuana, including whether, respondents had ever consumed cigarettes, alcohol, and/or marijuana, and if so, their age at time of first consumption of each substance. Definitions and descriptive statistics of the key individual characteristics are reported in Table 1. 39 percent of all respondents reported smoking, 43 percent reported alcohol use, and 20 percent reported marijuana use. Other observable characteristics included in the models are race, gender, indicators of familial stability (whether the respondent lives with both own parents at various ages, and the total number of living situations the respondent was in from birth to survey date), economic situation indicators such as family income level dummies 4 and if the respondent 4 Actual family income is missing for a substantial proportion of the sample, which means that we would lose a large part of our sample if we tried to include actual family income. To circumvent this, we use dummies indicating that family income was reported as being 5 times or greater than poverty level, or less than poverty level. 5
lived through any spells of acute hard times (defined as living in a homeless shelter or living without amenities like electricity and water), and neighborhood quality experiences instrumented by whether, before reaching age 12, the respondent witnessed violent crime (e.g. saw gun shooting). Table 2 provides the distribution of the number of substances the respondent reported ever using, and the order of use (for multiple substances). 4942 respondents indicated experimenting with at least one of the three substances. The general pattern seems to be that initiation of cigarette use predates initiation of alcohol use, and initiation of cigarette and/ or alcohol use predates initiation of marijuana use. We estimate the gateway effect by using both hazard rate analysis and fixed effects linear probability models that further control for unobserved heterogeneity. The relevant time frame is from the individual s birth year to the year of the survey (in 1997). For the hazard rate analysis, in each model, individuals exit when they initiate consumption of that particular substance, and those who have never used that substance by the time of the survey are treated as censored. Thus, the existence of a gateway effect is investigated by testing whether prior initiation of one or both of the other two substances (that is, having positive amounts of either or both of the other substances in the consumption capital stock) increases the risk of initiating consumption of the primary substance. We use the Cox's semi-parametric proportional hazards model to estimate the relative rate of failure (hazard function) as a function of the independent variables, since it does not make a priori assumptions regarding the shape of the underlying hazard function. 4. Empirical Results: To test the gateway effect, we begin by investigating whether the risk of initiating consumption of either smoking, drinking or marijuana is increased due to prior initiation of either of 6
the other two substances. Accordingly, we create time varying values for the variables smoke drink and marijuana-use as follows: The variables smoke, drink and marijuana-use take values of 0 for each age prior to the respondent s initiation of the respective substance, and 1 thereafter. For respondents that have never used a particular substance, the relevant variable has a value of zero for every observed age. Among the control variables, additional time varying variables include hard times experience and living with natural or adoptive parents. 5 Data on household income were available only for 1997, hence the dummy variables for rich and poor households are time invariant. We believe that the error in assuming no change of status in household income category over the respondent s lifetime is small. Birth year dummies are included to control for environmental differences such as country-level prices of substances, 6 cultural norms and attitudes regarding substance use and the extent of law enforcement. The first three columns of Table 3 report the results from the proportional hazards regression for marijuana use, cigarettes and alcohol respectively. For brevity, we limit our discussion to the results of primary interest the effects of prior consumption of other two substances on the initiation of the third, and invite readers interested in the effects of the other observables on substance initiation to inspect Table 3. 7 5 For respondents who ever experienced hard times, the survey asks at precisely which age(s) that occurred. Retrospective information on living with parents is provided for ages 2, 6, 12 and the survey year itself. We assume that the information for age 2 is relevant for ages 0-5, that for age 6 is relevant for ages 6-11, and that for age 12 is relevant up to the age just before the survey year. 6 We are unable to control for any state level prices since we do not know which states the respondents resided in at different ages prior to the survey year. 7 The result that African Americans and sometimes Hispanics are less likely to indulge in any of the three substances relative to white adolescents is contrary to popular perception, but corresponds to results of Pacula (1998) and Chatterji (1999). 7
Prior initiation of smoking or drinking makes the same person considerably more likely to subsequently experiment with marijuana. Gateway effects also seem to prevail for smoking and drinking, whereby experimentation with either one of alcohol or cigarettes substantially increases an individual s risk of initiation of the other substance. While prior consumption of marijuana appears to actually decrease the likelihood of subsequent initiation of smoking or drinking, additional analysis indicates that this particular result is not valid in alternate specifications. One serious concern is that, even though we track the behavior of the same individual over time, the above results may reflect individual heterogeneity rather than a true gateway effect, i.e. there may be a bad seed tendency, whereby certain adolescents are predisposed towards risky behaviors, while others are not. Thus, the positive effect of past consumption of substance B on current consumption of substance A might simply reflect an associational relationship, in that adolescents who are inclined to experiment with one addictive substance are also inclined to (later) experiment with others, and it cannot be concluded that consumption of substance B in itself increases the likelihood of subsequent initiation of A. We address this problem in the following ways: First, we re-estimate the Cox models using only the sub-sample of respondents who used at least one of the three substances (4942 respondents). The rationale is that all respondents in this sub-sample potentially belong to the bad seed category. Credibility to the gateway hypothesis is increased if, even among this sub-sample, consumption of substance B increases the probability of subsequent initiation of substance A. Second, we obtain estimates of the gateway effects sans the bias resulting from time-invariant unobservable heterogeneity using fixed effects linear probability models by creating panels for each individual from their birth to the survey year. Note that this method allows us to explicitly include only the time varying variables. The variables smoke, drink and marijuana-use are as previously described, and one-year lagged values for 8
consumption of the other two substances in each equation are used to ensure that we obtain gateway effects rather than contemporaneous correlation. We estimate two sets of fixed-effects models. The first set includes all person-year observations. In the second set, we focus on our main variable of interest, initiation of consumption for the substance of interest, and ignore observations for years after the initiation. For example, if marijuana-use is the dependent variable of interest and a respondent initiates consumption at age 13, then the second set of results ignores observations for that respondent from age 14 onwards. Results from estimations controlling for unobserved heterogeneity are presented in Table 4 8. In all cases, we find that prior consumption of alcohol and cigarettes significantly enhance the probability of subsequent initiation of marijuana, while prior consumption of one of alcohol or cigarettes significantly enhances the likelihood of initiation of the other. 9 Further, the negative effect of marijuana on the other substances seen in Table 3 no longer hold, since marijuana-use also has positive effects on subsequent initiation of drinking and smoking. This leads us to speculate that this particular result in Table 3 may have arisen due to selection bias, whereby adolescents who initiate substance use with marijuana are those who have some unobserved non-preference for alcohol and cigarettes. The results that did not control for that unobservable made it appear as if marijuana use, rather than the unobservable characteristics, lowers the likelihood of subsequent consumption of alcohol/cigarettes. These more trustworthy results suggest that, if a random sample 8 For brevity, we only present results pertaining to the effects of consuming the other two substances, with all other estimates being available on request. 9 While the magnitudes of effects from hazard analysis and linear-probability- fixed-effects analysis are not directly comparable, it should be noted that the effects of consuming the other substances on initiation of alcohol and of cigarettes are actually similar in size. The magnitude of effects of prior use of alcohol or cigarette consumption on initiation of marijuana is smaller in the fixed effects model than in the restricted-sample hazard analysis, but in both cases they are very highly significant statistically. 9
of adolescents were given marijuana, then these adolescents would later be more inclined to experiment with alcohol and cigarettes as well. 10 5. Conclusion: Our study provides strong evidence that cigarettes and alcohol serve as gateways for marijuana and for each other, and some evidence that marijuana serves as a gateway to cigarettes and alcohol, and counters the argument that such results arise from unobserved bad seed tendencies only. The evidence of the existence of a gateway effect indicates the presence of complementarities among substance use, and bodes well for the effectiveness of policies aimed at curtailing any of the above substances either through improving teenage awareness of the risks of indulgence or via higher prices. It appears that policies designed to curb any one of the three addictive substances may have the added positive externality of curbing the others as well. 10 We also re-estimated the models using linear first-differencing, and after substituting current consumption in place of lagged consumption of other substances. In all cases, the results confirm that consumption of one or both substances has significantly positive effects on initiation of the third. 10
Table 1. Descriptive Statistics Variable Variable Definition 1 Name N Mean Std Dev Substance Usage Respondent ever smoked cigarettes? Eversm 8991 0.393 0.488 Respondent ever drank alcohol? Everdr 8982 0.429 0.495 Respondent ever used marijuana? Evermj 8981 0.201 0.401 Personal Characteristics Age as of 1997 Age 8984 14.408 1.405 African-American Black 8984 0.266 0.442 Hispanic Origin Hisp 8959 0.212 0.409 Male Male 8984 0.512 0.500 Neighborhood Characteristics Respondent ever lived in acute hard times? Hard Times 8969 0.053 0.225 Respondent seen someone shot? Shot 8833 0.110 0.313 Family Characteristics Total number of living arrangements ever 2 Numres 7869 1.543 1.096 Household Income poverty level Poorhh 8984 0.164 0.370 Household Income > 5 times poverty level Richhh 8984 0.092 0.289 Biological father never identified No Father 8984 0.047 0.211 Lives with both natural/adoptive parents-age 2 Twoparent2 8984 0.408 0.491 Lives with both natural/adoptive parents-age 6 Twoparent6 8984 0.405 0.491 Lives with both natural/adoptive parents-age 12 Twoparent12 8984 0.404 0.491 1 Variable = 1 if either the answer is "yes" or the statement is true 2 Missing values (approximately 100 observations) filled in with modal value of 1 11
Table 2. Distribution of Number of Substances Consumed Group Frequency Percent Used no substance 4173 46.5 Only smoke 769 8.6 Only drink 1083 12.1 Only marijuana 36 0.4 Smoke & drink, drink last 802 8.9 Smoke & drink, smoke last 342 3.8 Smoke and marijuana, marijuana last 133 1.5 Smoke and marijuana, smoke last 15 0.2 Drink and marijuana, marijuana last 129 1.4 Drink and marijuana, drink last 26 0.3 All three, marijuana last 546 6.1 All three, drink last 434 4.8 All three, smoke last 481 5.4 12
Table 3: Cox Hazards Regression Results for Marijuana Use, Smoking and Drinking Smoking Drinking Marijuana Two parents Hard Times See Shot No Father Numres Poorhh Richhh Hisp Black Male YOB81 YOB82 YOB83 Marijuana-Use Smoking Drinking Estimate (Std. Err) [Risk Ratio] Estimate (Std. Err) [Risk Ratio] Estimate (Std. Err) [Risk Ratio] 1.67* ( 0.084) [5.32] 1.07* (0.078) [2.92] -0.44* (0.059) [0.65] 0.35 (0.228) [1.42] 0.53* (0.070) [1.71] -0.14 (0.124) [0.87] 0.05* (0.019) [1.06] -0.06 (0.072) [0.94] 0.06 (0.089) [1.06] 0.02 (0.069) [1.02] -0.21* (0.070) [0.81] 0.11* (0.052) [1.11] -0.30* (0.068) [0.74] -0.78* (0.078) [0.46] -1.20* (0.097) [0.30] 1.12* (0.049) [3.05] -0.24* (0.059) [0.78] -0.41* (0.041) [0.66] 0.59* (0.168) [1.80] 0.50* (0.055) [1.66] -0.01 (0.087) [0.99] 0.06* (0.013) [1.07] -0.04 (0.052) [0.97] -0.13** (0.063) [0.88] -0.34* (0.050) [0.71] -0.77* (0.052) [0.46] 0.03 (0.037) [1.03] -0.06 (0.052) [0.94] -0.09*** (0.057) [0.91] -0.22* (0.065) [0.81] 1.12* (0.048) [3.07] -0.27* (0.054) [0.77] -0.21* (0.039) [0.81] -0.19 (0.211) [0.83] 0.47* (0.054) [1.59] -0.24* (0.090) [0.79] 0.03** (0.015) [1.03] -0.09*** (0.051) [0.91] 0.09 (0.057) [1.10] -0.14* (0.048) [0.87] -0.43* (0.049) [0.65] 0.04 (0.035) [1.05] -0.12** (0.049) [0.89] -0.13** (0.055) [0.87] -0.23* (0.065) [0.79] 13
-1.75* (0.136) [0.17] -0.28* (0.078) [0.76] -0.29* (0. 079) [0.75] YOB84 Likelihood ratio Wald N 8945 8945 8944 * Significant at 1 percent. ** Significant at 5 percent. *** Significant at 10 percent. Hazard risk-ratios are exponential values. The default is 1 (or exp(0) ). Values greater than 1 indicate an increase in the risk of failure (substance initiation) and values less than 1 indicate a decrease in the risk of failure. 14
Table 4: Regression Results for Marijuana Use, Smoking and Drinking after Controlling for Unobserved Heterogeneity Smoking a Drinking a Marijuana a Likelihood ratio Model 1 Cox with Limited Sample Estimate (Std. Err) [Risk ratio] Estimate (Std. Err) [Risk ratio] 1.367* (0.078) [3.924] 0.917* (0.072) [2.502] 0.259* (0.048) [1.296] 0.226* (0.054) [1.253] Estimate (Std. Err) [Risk ratio] 0.291* (0.046) [1.337] 0.163* (0.050) [1.177] 831.41 680.74 565.92 Estimate (Std. Err) 0.260* (0.003) 0.222* (0.002) Model 2 Linear FE Estimate (Std. Err) 0.325* (0.004) 0.340* (0.005) Estimate (Std. Err) 0.358* (0.004) 0.347* (0.005) Model 3 Linear FE, Up to Initiation Only Estimate Estimate Estimate (Std. Err) (Std. Err) (Std. Err) 0.133* (0.002) 0.087* (0.003) 0.136* (0.004) 0.193* (0.009) 0.203* (0.004) 0.297* (0.009) Wald 733.25 713.34 601.21 R 2 -overall 0.311 0.312 0.330 0.199 0.197 0.163 N 4942 4942 4942 8945 8945 8944 8945 8945 8944 Pooled N b 104652 104652 104652 101882 96959 96893 * Significant at 1 percent. ** Significant at 5 percent. *** Significant at 10 percent. a : In case of the fixed effects models, this denotes if the substance had been initiated at least one-year earlier. This is to ensure that we get inter-temporal, rather than contemporaneous, effects. b : Denotes the total number of person-year observations after panels are created for each respondent from birth to survey-year using retrospective information. See notes below table 3 for how to interpret risk ratios.
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