Sport Participation and Adolescent Use of Performance Enhancing Drugs: Evidence from the YRBSS

Transcription

1 Sport Participation and Adolescent Use of Performance Enhancing Drugs: Evidence from the YRBSS Brad R. Humphreys University of Alberta Department of Economics Jane E.Ruseski University of Alberta Department of Economics July 2007 Abstract Much of the evidence on the use of performance enhancing drugs in sport comes from professional athletics. However, evidence also indicates that adolescents use performance enhancing drugs but that their use is not limited to those participating athletics. While the incentives faced by professional athletes, and the economic motivations for the use of performance enhancing drugs in this population, are clear, the economic rationale for the use of performance enhancing drugs by youths has received little attention. In this paper we examine the determinants of performance enhancing drug use by adolescents from an economic perspective. The empirical analysis makes use of data from the Youth Risk Behavior Surveillance System (YRBSS), a nationally representative sample of risky behavior by adolescents that contains questions about the use of steroids and participation in sport. Among high school athletes, males, blacks, and those who participate in more sports have a higher probability of heavy steroid use. Infrequent steroid use is associated with different factors that are consistent with motivations to change physical appearance and experimentation with illicit substances. These results collectively suggest there are economic rationales, although different, for adolescent use of steroids. JEL Codes: I180, L830 Brad R. Humphreys Jane E. Ruseski University of Alberta University of Alberta Department of Economics Department of Economics 8-14 HM Tory 8-14 HM Tory Edmonton, AB T6G 2H4 Canada Edmonton, AB T6G 2H4 Canada Phone: Phone: brad.humphreys@ualberta.ca ruseski@ualberta.ca 1

2 Introduction The use of performance enhancing drugs by professional and elite athletes has been observed for thousands of years. Since the 1960s, the use of anabolic-androgenic steroids (AAS), whether anecdotal or actual, has been attributed to professional football players, baseball players, cyclists, weightlifters, powerlifters, bodybuilders and track and field athletes [Yesalis and Bahrke(1995)]. Professional and elite athletes who use performance enhancing drugs are typically adults. However, the use of drugs to enhance sports and physical performance and appearance is also prevalent among adolescents. Until the mid-1970 s, knowledge about adolescent use of performance enhancing drugs was based on anecdotes and rumors. Recent evidence, primarily derived from survey data, suggests that adolescents use drugs in an effort to improve athletic performance and physical appearance. These drugs include caffeine, amphetamines, human growth hormone, erythropoietin, creatine and AAS. Much of the recent evidence is on AAS use among adolescents; very little is known about the use of other performance enhancing drugs and nutritional supplements among adolescents [Yesalis and Bahrke (2000)]. From a theoretical perspective, several economic models have been developed to explore the economic motivations and incentives for doping among professional and elite adult athletes. These theories have not been applied to adolescents who may have different incentives, economic or not, to use performance enhancing drugs. Indeed, the economic rationale for the use of performance enhancing drugs by youths has received little attention. In this paper, we conduct an exploratory analysis of the economic and behavioral determinants performance enhancing drug use by adolescents using data from the Youth Risk Behavior System (YRBS). The YRBS is a survey of nationally representative sample of adolescents that contains questions about the use of steroids, use of other harmful substances such as cigarettes, alcohol and cocaine, and participation in sports. The next section reviews some of the clinical and epidemiological literature on the prevalence of and risk factors associated with the use of AAS both in the United States and internationally. Much of what is known about the prevalence of AAS use is based on cross-sectional survey data and unconditional statistical analyses. Previous studies have found that AAS use is associated with a number of factors such as availability, personal behavior, athletic participation, ethnicity, socioeconomic status and education level; however these studies have produced conflicting results [Bahrke et al (2000)]. However, the existing literature does not provide insight to the economic motivations or behaviors behind adolescent performance enhancing drug use and often does not condition the use on economic, social or demographic characteristics. In this paper, we undertake both these tasks. Further research is needed to gain a better understanding of the fundamentally different behavioral characteristics and motivational factors of adolescents who use performance enhancing drugs. The review of the empirical literature is followed by a brief review of the theoretical literature on the economics of doping. Our empirical analysis of the YRBS data and suggestions for future research conclude the paper. Factors Associated With Steroid Use Among Adolescents Bahrke et al (2000) undertook a literature search of earlier studies to identify risk-factors associated with the use of anabolic-androgenic steroid use by adolescents. The studies reviewed generally employed cross-sectional survey data and used self-administered questionnaires. The studies were reviewed for findings regarding the association between several demographic, academic and sports participation and personal risk factors and AAS use. The demographic factors examined were gen- 2

3 der, age, grade level, race and ethnicity, socioeconomic status, parental characteristics, geographic location and city size. The only demographic factor for which there is relative consensus regarding AAS use was gender. A number of studies found a higher likelihood of AAS use among males than females. Bahrke et al (2000) concluded that the findings about the relationship between age and AAS use in the literature are mixed. Several studies found that older adolescents have higher AAS use rates than younger adolescents, while other studies failed to find an association between age and AAS use. One recent study (van den Berg et al (2007)) used longitudinal data and found that steroid use decreased as adolescents grew older. The studies reviewed by Bahrke et al (2000) contained mixed results about the association between race/ethnicity and steroid use. The academic and sports participation factors examined included school size, academic performance, and athletic participation. The findings with respect to school size were mixed with some studies finding a positive association between school size and AAS use, and others finding a negative association between school size and AAS use; still others found no association. The studies that reported a significant association between academic performance and AAS use concluded that AAS use increased with poor academic performance, truancy, and being dissatisfied in school. There is evidence that AAS users were more likely to participate in school-sponsored athletics than non-users; however, the use of AAS among adolescents who did not participate in school-sponsored athletics is substantial (Barrke et al (2000)). The second most popular reason reported for adolescent use of steroids was to improve physical appearance. Adolescents who use steroids to change their physical appearance clearly have different motivations than athletes. It is important to have a better understanding of the differences among adolescents who use steroids for different reasons in order to craft prevention and intervention strategies that are likely to have the desired effect of limiting illicit steroid use. A number of recent studies further explore the association between steroid use, physical activity and risky behaviors. A number of recent studies further explore the association between steroid use, physical activity and risky behaviors. Miller et al (2005) analyzed data from the 1997 Youth Risk Behavior Survey (YRBS) to determine the relationship between physical activity (including athletic participation and strength training) and various problem behaviors (including cigarette smoking, smokeless tobacco use, binge drinking, marijuana use, cocaine use, fighting and sexual risk). They estimated the odds of adolescent steroid use and lifetime frequency of reported use in the 1997 YRBS. They found that approximately 2.0% of females and 4.1% of males reported having used steroids without a doctor s prescription at some point in their lifetime. The results of a logistic regression analysis indicated a positive and significant relationship between steroid use and other problem behaviors but no association between steroid use and either athletic participation or strength training. In another study, van den Berg et al (2007) examined the prevalence, persistence, secular and longitudinal trends and predictors of steroid use using data from Project EAT II (Eating Among Teens), a 5 year longitudinal study of eating, activity, weight and other related variables. The study sample consisted of 2,516 middle and high school students from 31 urban and suburban communities in the Minneapolis/St. Paul metro area. Data were collected in 1999 (time 1) and 2004 (time 2). One-third of the participants who were in middle school in 1999 comprised the younger cohort. The remaining two-thirds of the participants were in high school in 1999 and comprised the older cohort. Overall, 1.4% of female and 1.7% of male adolescents in the sample reported using anabolic steroids. While the prevalence rates are similar to those found in other cross-sectional studies, the finding of similar prevalence rates among males and females is different. Most cross-sectional studies find significantly higher prevalence of steroid use among male adolescents. Steroid use differed by age cohort, with the younger cohort showing a higher prevalence. Boys in the younger cohort were three times more likely to report steroid use than those in the older cohort. 3

4 van den Berg et al (2007) find differences in steroid use by race/ethnicity but not across socioeconomic status categories. With respect to race/ethnicity categories, Native American and Hmong male participants and black female participants showed higher prevalence of steroid use than did other race/ethnicity groups. Steroid use was not persistent over time in this population. The majority of participants who reported using steroids in 1999 were not still using them in This finding suggests that adolescents may simply experiment with steroids for a time in a manner similar to experimenting with alcohol and other drugs. There was no significant secular trend in steroid use between 1999 and 2004; however, both the younger and older cohorts of male adolescents showed significant decreases in steroid use between 1999 and 2004 as did the younger female cohort. This result is consistent with findings from some cross-sectional studies that steroid use declines with age. The predictors of steroid use in time 2 differed for males and females. Having an ideal body size and self-report of health weight-control behaviors at time 1 were significant predictors of steroid use in time 2 for males. The significant predictors for females were lower satisfaction with weight and body mass index (BMI). In contrast with other studies, this longitudinal study did not find an association between weight-related sports participation and steroid use for males, although a marginal association for females was present. This study also did not find a significant association between the use of steroids and the use of other substances. These discrepancies suggest that cohort-specific effects my confound the underlying relationship between steroid use and sport participation, and other illicit substance use, in cross sectional data. Additional research using longitudinal data is clearly warranted. Dodge and Jaccard (2006) examined the relationship between high school sports participation and the use of steroids and legal performance-enhancing dietary supplements using data from the National Longitudinal Study of Adolescent Health. They found that males were nearly 7.5 times more likely to use anabolic steroids than females. 2.7% of males and 0.4% of females reported ever having used anabolic steroids during the past year. They also found that sports participation in young adulthood was a significant predictor of adult steroid use but the effect was qualified by a statistically significant gender-sport interaction that indicated greater gender differences in adult steroid use for those who participated in sports during high school [Dodge and Jaccard (2006) page 360]. The use of AAS by adolescents is not unique to the United States. Surveys on steroid use have been conducted in Canada, Sweden, South Africa, the United Kingdom and Australia [Bahrke et al (2000)]. Nilsson et. al. (2001) conducted a survey of 5, and 17 year old male and female adolescents in a county on the southwest coast of Sweden. Females in this study did not report misuse of steroids while 2.9% of male adolescents were found to misuse steroids. The prevalence rates were 3.6% among 16 year olds and 2.2% among 17 year olds. The primary reason given for steroid use was to improve appearance. Steroid users in this study also drank more alcohol, drank alcohol more often, and used more illicit drugs than non-steroid users. A second cross-sectional study was undertaken in eight senior high schools in Uppsala, Sweden to assess the importance of risk factors such as socio-demographics, sports participation and use of other substances in adolescent use of AAS [Kindlundh et al (1999)]. 2,742 high school students participated in this study. 2.1% of male adolescents and 0.2% of female adolescents reported using AAS. 3.3% of the males who participated in sports activities in their leisure time reporting using steroids at least once compared to 1.2% of males who did not participate in sports activities. There was a strong association between participation in strength training and the use of AAS. The majority of male adolescents who reported using doping agents cited acquiring a more attractive body/larger muscles or enhancing sports performance as the reasons for using doping agents. Consistent with other cross-sectional studies, a significant positive association between the 4

5 use of tobacco and psychotropic drugs and steroids was found for both males and females. High consumption of alcohol was positively related to steroid use among male adolescents. Handelman and Gupta (1997) report results from two surveys conducted in 1992 in the states of Victoria and New South Wales, Australia, to identify patterns and prevalence of drug and alcohol use in high school children. Unlike the surveys conducted in Sweden, the sample size in this survey was rather large (n=13,121). Use of AAS at some point in their life was reported by 3.2% of the male respondents and 1.2% of the female respondents. The risk factors most strongly associated with use of steroids were truancy and unsupervised recreation. Other risk factors associated with the use of steroids included ethnicity, student s income and social support. Pallesen et al (2006) examined the prevalence and risk factors for life-time and current anabolic steroid use in a sample of youths in Hordaland County, Norway. The prevalence of lifetime steroid use was 3.6% for males and 0.6% for females. They examined the association between steroid use and behavioral and psychological factors such as smoking, alcohol use, anxiety, depression, and illegal narcotic use. They found a positive association between steroid use and smoking, illegal narcotic use, alcohol use and symptoms of depression. Symptoms of anxiety and place of residence were not associated with steroid use. Four different factors associated with adolescent steroid use emerge from this literature. Males clearly use steroids more frequently than females. In most studies, adolescent athletes use steroids more frequently than non-athletes. Steroid use has often been linked to the use of other illicit substances like drugs. Some adolescents may use steroids to alter their physical appearance. These characteristics do not appear to describe a single profile of an adolescent steroid user. Instead, it may be the case that different groups of adolescents use steroids for distinctly different reasons. The existing clinical literature focuses entirely on documenting the characteristics of adolescent steroid users and discussing possible policy interventions that target these steroid users. Demographic and economic variables are sometimes included as covariates in these empirical studies, but they do not consider the possible economic motives for adolescent steroid use, or assess the effectiveness of the proposed policy interventions in light of economic motives for adolescent steroid use. In the next section, we review the existing economic literature on the use of steroids, which economists refer to as doping. Theoretical Analyses of Doping The central questions explored in the theoretical literature on the economics of doping focus on the incentives for athletes to use illegal substances and solutions to alter the incentives in an effort to reduce the prevalence of doping. The economics literature contains two different types of doping models: game theoretic models which treat doping as a strategic decision and rational choice models that treat the decision to use drugs as a utility maximizing choice. Game theory provides a construct to characterize the decision to take illegal substances as an equilibrium strategy in a prisoners dilemma framework and other related strategic games. The rational choice theoretical approach to analyzing doping is based on an economic analysis of criminal behavior in a seminal article by Becker (1968). These two frameworks allow for modeling the behavior of a wide variety of individuals, including professional and elite athletes, and account for a large number of different factors that influence the decision to engage in illicit behavior, including the use of steroids. Doping as Strategic Behavior In much of the existing game theoretical literature, doping is modeled in the context of the prisoner s dilemma [Breivik (1987), Wagner (1993), Bird and Wagner (1997) and Haugen (2004)]. In this 5

6 context, there are N athletes engaged in competition. Within this group of athletes, there is a distribution of tastes over victory, long-run health, and the probability of winning the contest. As long as all athletes have equal prospects of winning, the probability of winning is increased for athletes that dope and doping by other athletes decreases the probability of winning. As long as all athletes have preferences for winning, they will chose to dope regardless of how many other athletes are doping. In equilibrium, all athletes dope. Berentsen (2002) considers a strategic game in which two players move simultaneously and decide to use performance enhancing drugs in advance of the competition. The athletes have unequal prospects of winning the contest. There is a mixed strategy equilibrium in this game in which the athlete with a greater chance of winning is more likely to use performance-enhancing drugs than the other player. An interesting result in this model is that for some parameter values, the favored player is more likely to use performance-enhancing drugs but is less likely to win with doping than without. In a related paper, Berentsen (2003) develops and analyzes an evolutionary game between two athletes with differing abilities (strong and weak) who can use performance-enhancing drugs. There is a cost associated with using the drugs. The equilibria in this game are not necessarily stable and depend on the cost of doping and the fraction of strong players. Doping is the dominant strategy if the cost of doping is sufficiently small and the fraction of weak players is large. However, in the case where the fraction of strong players is large, eventually all strong players dope but weak players have an incentive to stay clean because they are unlikely to win even with doping. Although the time frame implicit in game theoretic doping models is not directly applicable to adolescents, these models do provide a useful construct for thinking about the economic incentives facing adolescents for using performance-enhancing drugs. Just as professional and elite amateur adult athletes might view doping as a strategic choice to improve the probability of winning a contest held today, adolescents may view doping as a strategically sound decision not only to win a contest today but also to improve strength and size for future competition. Taking performanceenhancing drugs as an adolescent holds the promise of a successful athletic career at the college, elite amateur and professional level. This improved current performance might also lead to an athletic scholarship to college, which can provide important economic benefits in the near and distant future. Doping as Illicit Behavior Becker (1968) developed an economic model to examine the interactions between criminal activity, the social costs associated with enforcing laws and punishing crimes, and the optimal amount of enforcement. In the context of Becker s model, taking steroids can be interpreted as an illicit behavior like cocaine or heroin use that involves both costs and benefits. Becker contends that a useful theory of criminal behavior can be constructed by simply extending the typical economic analysis of choice in which individuals weigh the benefits and costs of both legal and illegal activities given constraints. The outcome resulting from this rational decision is the course of action that generates the greatest expected net utility at the time the decision is made. Maennig (2002) bases his model for analyzing illicit behavior, in terms of doping and corruption, on Becker s (1968) model of crime and punishment. In this rational choice model, individuals choose to take illicit drugs if doing so maximizes expected net utility. The expected utility function is given by: E(U n i ) = (1 p i )[U i (p j Y i DC i P OC i )+p j NP B i NOC i ]+p i [U i ( F i DC i P OC i ) LR i NOC i ] where E(U n i ) is the expected net utility of an illicit behavior; p i is the probability of being caught and convicted; and p j is the probability of success of the illicit behavior [Maennig (2002)]. U i is the 6

7 utility function for individual i; Y i is the gross pecuniary income realized from successful cheating; DC i is the direct cost of undertaking the illicit behavior; P OC i are the pecuniary opportunity costs; NOC i is the nonpecuniary opportunity cost; NP B i is the nonpecuniary utility from illicit behavior; F i is the financial penalty arising from limited competition bans; and LR i is the loss of utility in the form of reputational loss in the case of conviction [Maennig (2002)]. In this model, an individual will behave illicitly only if expected net utility (E(Ui n )) is greater than the nonpecuniary cost of illicit behavior. While Maennig applies this model specifically to doping in the context of Olympic competition and corruption in the International Olympic Committee, it could also be applied to adolescent steroid use. There is evidence in the clinical literature that doping among adolescents is not confined to those who participate in sports and other types of physical activity [Johnson et al (1989), Miller et al (2002a), and Windsor and Dimitru (1989)]. The existing evidence suggests that some adolescents use steroids to improve physical appearance. Similarly, some evidence indicates an association between use of steroids and other risky behaviors such as smoking, excessive drinking and suicidal tendencies [Miller et al (2002b)]. The arguments of the expected utility function in this model are sufficiently general to simultaneously take into account the effect of different risk factors such as socioeconomic status, parental influence, peer pressure, psychological factors, health risk, and other risky behaviors, on steroid use both for performance-enhancing motives and other nonathletic related motives. So Becker s (1986) rational choice model of illicit behavior can motivate adolescent steroid use to improve physical appearance and experimental use of illicit substances including steroids. These two economic frameworks can encompass the conflicting explanations for adolescent steroid use provided by sociological theories such as strain theory and problem behavior theory as described by Miller et al (2005). They can also be applied to the differing groups of steroid users identified in the clinical literature survey above. The game theoretic models of steroid use apply to adolescent athletes. These students can be thought of as participants in a contest, where the outcome of the contest is the awarding of economically valuable athletic scholarships to college, or lucrative professional sports contracts, to the top performers. The rational choice models apply to adolescents who use steroids to change their physical appearance, or who take steroids while experimenting with the use of other illicit substances like cigarettes, alcohol, or drugs. These students use steroids because their expected total benefits outweigh the expected total costs. In the next section, we develop some empirical evidence consistent with these motivations. Empirical Analysis We hypothesize that adolescent use of steroids has an economic component. In order to explore this hypothesis, we analyze data from the Youth Risk Behavior Survey (YRBS). The YRBS is a biennial, nationally representative survey given to high school students in the United States. The survey is carried out by the Centers for Disease Control and Prevention (CDC) and was first conducted in The CDC has released publicly available YRBS data through Much of the research on adolescent use of steroids is based on cross-sectional surveys with samples that are typically not nationally representative using unconditional statistical techniques (exceptions are van der Berg et al (2007), Dodge and Jacard (2006), and Miller et al (2005)). As discussed in the review of the evidence on the prevalence and risk factors of anabolic steroid use, a mixed picture of steroid use among adolescents emerges. For example, studies do not find a consistent relationship between age and steroid use. Some studies find a positive relationship between steroid use and other risky behaviors such as drinking excessively, smoking, and using other illicit drugs. 7

8 Steroid use is not limited to those who participate in sports and other physical activities such as strength training. These findings are interesting and suggest that the incentives and motivations for steroid use are different for those engaged in athletic activity from those who do not actively participate in competitive sports. The theoretical literature on the economics of doping provides a framework for examining these differing behavioral incentives to use steroids among adolescents. Our empirical analysis makes use of all of the publicly available surveys from 1991 to 2005 to explore the presence of economic incentives for adolescent steroid use. We posit that adolescents who participate in sports have a greater economic incentive to use performance-enhancing drugs and thus will have different characteristics than adolescents who use steroids for other reasons such as experimentation with illicit substances or to improve appearance. We explore our hypotheses by estimating probit models of the determinants of steroid use for the full sample and two subsamples. The two subsamples separate the total sample into heavy steroid users who participated in team sports and infrequent steroid users who did not participate in team sports. We hypothesize that the former group has an incentive to use steroids to improve athletic performance and the latter group is experimenting with drugs. Data Description The YRBS is a nationally representative survey of all students in grades 9 through 12 in the United States. The survey methodology is based on a three-stage cluster design. The first stage samples large counties or groups of smaller contiguous counties, called Primary Sampling Units (PSUs) in the YRBS. The second stage samples schools within these groups. The third stage samples one or two entire classes within each sampled school. The methodology includes oversampling of black and Hispanic students to ensure adequate representation of these groups. The YRBS is intended to track the prevalence of risky behavior by high school students. The survey contains questions on topics like violent behavior (fighting and carrying weapons), smoking, drug use, sexual behavior, exercise, and eating habits. It also contains demographic questions, and a limited number of questions about socio-economic factors. For the purposes of this research, we use the YRBS question designed to elicit information about steroid use by high school students. Since the 1991 survey, the YRBS has included the following question: During your life, how many times have you taken steroid pills or shots without a doctor s prescription? 1 0 times 2 1 or 2 times 3 3 to 9 times 4 10 to 19 times 5 20 to 39 times 6 40 or more times Summary Statistics The YRBS has been administered biannually since The questions about risky behavior and physical activity change little over the period, although there are some changes to the drug-related questions. The demographic and socio-economic questions seem to change frequently, as questions about parental education, health, and grades appear and disappear throughout the period. The incidence of reported steroid use among high school students is small, but persistent over this fifteen year period. Table 1 shows the fraction of males and females in the surveys who 8

9 Table 1: Incidence of Risky Behavior Among High School Students % Using Steroids % Smoking % Drinking Year Observations Female Male Female Male Female Male , , , , , , , , reported using steroids at least once during their lifetime, along with the percent who reported smoking cigarettes and drinking alcohol within the last 30 days. The second column shows the number of students surveyed in each year. Clearly, steroid use is not rampant among high school students. Compared to smoking and drinking, steroid use is relatively rare. However, steroids are much harder to obtain than cigarettes or alcohol, and many types of steroids must be injected, further raising the cost of using these substances. Also note the clear difference in steroid use by gender. Males report using steroids at a much higher rate than females. In most years, males report using steroids more than twice as often as females. Finally, the results on Table 1 indicate that the 2003 survey may have different results than the other years. The reported rates of drinking are much lower in 2003, and the reported rates of steroid use are much higher. Table 2 shows the frequency distribution for the steroid use question over the period for males. The table shows the percent of males surveyed that reported using steroids for each answer to the steroid use question in each YRBS survey. The frequency distributions show an interesting U-shaped pattern. Many of the males who reported using steroids did so only one or two times. These individuals were probably experimenting with steroids, as it is unlikely that using steroids one or two times provides much benefit in terms of enhanced athletic performance. The number of males reporting steroid use declines as the number of times used increases until the highest category, using steroids 40 or more times. In each year in the sample, the number of males who reported using steroids 40 or more times is larger than the number who reported using steroids between 10 and 39 times. These heavy users of steroids may be the group trying to gain an advantage in sport. Table 3 shows summary statistics for a number of key variables in the YRBSS survey. Note that some of these variables are not available in every year in the sample. The parental education variables were only collected in a few years and the height and weight variables were only added in A significant fraction of the sample population want to change their weight in some way. About 40% want to lose weight and about 20% want to gain weight. Exercise and participation in team sports are also common in the sample population. Table 4 shows the mean of the exercise and sport participation variables by gender. All of these means are significantly different at the 1% level based on t-tests, indicating that team sport participation rates, the average number of sports played, the rate of participation in exercise to lose weight, and the number of days exercised in the past week all differ by gender. Males have a higher team sport participation rate, participate in more team sports, and exercise more days per 9

10 Table 2: Frequency Distribution of Steroid Use Among Males, % Using Frequency of Use times or 2 times to 9 times to 19 times to 39 times or more times Table 3: Summary Statistics, Full Sample Variable Mean StDev Min Max Used Steroids (lifetime) Plays Team Sports # Team Sports Played Exercises to Lose Weight # of Days of Exercise, last % Male %White %Black Grade Age Height (Meters) Weight (kg) Wants to Gain Weight Wants to Lose Weight Smoked last 30 days Drank last 30 days Smoked Dope last 30 days Did Hard Drugs last 30 days Mother Attended College Father Attended College

11 Table 4: Summary Statistics, Exercise and Sport Participation % Participating in Number % Exercising to Days Exercised Gender Team Sports of Sports Lose Weight in Last Week Female Male week than females. Females exercise more to lose weight than males. Conditional Evidence on Steroid Use The unconditional results presented above are informative, but in order to better understand the determinants of steroid use we need to examine the factors that affect adolescent steroid use conditionally. To do this, we estimate the probit model P r(y i 0 x j ) = Φ (x j β) (1) where y i is an indicator variable for lifetime reported steroid use, Φ( ) is the standard cumulative normal distribution, and x j β is the probit score using using the maximum likelihood estimator. Table 5 presents the results of the probit estimation, estimated parameters and P-value, for three different samples. The first column, labeled C1, uses the entire YRBS sample. These results include year dummy variables for each year that the YRBS was administered (1991, 1993, 1995, 1997, 1999, 2001, 2003, 2005). 1 The second two subsamples separate the total sample into heavy steroid users who participated in team sports and infrequent steroid users who did not participate in team sports. That is, the sample is partitioned into students who did not participate in team sports (N = 53, 506) and students who did participate in team sports (N = 59, 854), and the steroid use variable is restricted to infrequent users in the former case and heavy users in the latter case. We hypothesize that students in the second group have an incentive to use steroids to improve athletic performance and students in the first group are simply experimenting with drugs. The second column on Table 5, labeled C2, contains results for the subsample of students who reported using steroids either 1 to 2 times or 3 to 9 times in their lifetime and who reported that they did not participate in any team sports. These are casual steroid users who may be experimenting with drugs. The third column on Table 5, labeled C3, contains results for the subsample of students who reported using steroids more than 10 times in their lifetime and who reported that they participated in team sports. These are the steroid users who may have an incentive to use steroids to improve athletic performance. Discussion The full sample results are presented to confirm that the YRBS data produce results similar to those reported in the cross sectional clinical literature and to establish some empirical regularities in the data. The full sample results in column C1 of Table 5 show a clear pattern of declining steroid use as adolescents age. Males are about 1% more likely to use steroids than females. There is little difference in reported steroid use across ethnic groups, although hispanics are slightly more 1 These results are available on request. Interestingly, the incidence of reported steroid use, conditional on the other factors, is higher in the 2001, 2003 and 2005 sample years. 11

12 Table 5: Probit Results - Probability Derivatives and P-values (C1) (C2) (C3) Used Steroids Used Steroids Variable Full Sample < 10 Times 10 Times Age Male Asian Black Hispanic Smokes Drinks Uses Marijuana Uses Hard Drugs Wants to Gain Weight Wants to Lose Weight # Days of Exercise # Team Sports N 113,360 53,506 59,854 Psuedo R

13 likely to use steroids. Smoking and drinking are associated with a higher probability of steroid use. Using marijuana and hard drugs (cocaine, heroin, methamphetamines, LSD, and crack cocaine) are also associated with a higher probability of steroid use, conditional on other factors. Hard drug use has the largest probability derivative, suggesting that hard drug users are almost 8% more likely to report using steroids. These results are generally consistent with those reported in the clinical literature. We posit that infrequent steroid users are experimenting with drugs. This type of steroid use can be motivated by Becker s (1986) rational choice model of illicit behavior. The results in column C2 show that, although males are still more likely to use steroids infrequently than females, the probability derivative for males is much smaller in this sub-population that for the full sample. This difference may reflect economic factors. In this group of non-athletes, males will have less to gain, in terms of future athletic scholarships or professional sports careers, which is consistent with a smaller probability derivative. In this sub-population, steroid use is also associated with the desire to change body appearance. Although using steroids to change body appearance has little relationship to direct economic benefits like athletic scholarships or professional sports contracts, it can be motivated by rational choice models of illicit behavior. Among infrequent steroid users, using other drugs, smoking, and drinking is also associated with a higher probability of steroid use, which supports the idea that infrequent steroid users are experimenting with illicit substances. This behavior can also be motivated by rational choice models of illicit behavior. Finally, we posit that the most likely sub-population to use steroids for pecuniary reasons is students who participate in team sports. For students who participate in sports and report using steroids frequently, steroids may be used to improve athletic performance and may lead to future economic benefits like college athletic scholarships and professional athletic careers. Steroid use declines much less with age in this group than the other two groups. This is consistent with an economic rationale for steroid use, because most athletic scholarships are awarded in the senior year of high school, so the incentives to continue to use steroids should decline relatively little in this population. Blacks are more likely to be heavy steroid users in this population, which is also consistent with an economic rationale for steroid use. The probability derivatives on drug use are smaller in this population than for infrequent steroid users, and a desire to change body appearance is not associated with a higher probability of heavy steroid use. Finally, greater participation in team sport is associated with a higher probability of heavy steroid use. In general, the results for the subsample of participants in sports is supportive of an economic rationale for all of the observed types of adolescent steroid use. Among high school athletes, males, blacks, and those who participate in more sports have a higher probability of using steroids heavily. These are also characteristics of students with the highest expected future payoff from improved athletic performance in high school. Infrequent adolescent steroid use is associated with a different set of explanatory variables. This behavior may reflect both attempts to change physical appearance and experimentation with illicit substances. Both motivations can be explained in the context of rational choice models of illicit behavior. The evidence presented here is consistent with an important economic motivation for adolescent steroid use. This possibility has been largely ignored in the existing literature. If economic factors pay a role in adolescent steroid use, this might have important consequences for policy interventions aimed at reducing adolescent steroid use, and should be taken into account by policy makers. 13

14 Suggestions for Further Research While the conditional and unconditional results presented in this paper are interesting, and suggest that economics plays a role in adolescent steroid use, considerable work remains to be done on on this project. We intend to expand this research along several lines. First, the YRBS surveys contain some socioeconomic variables that might help us to get at the economic basis for adolescent steroid use. In a few surveys, the YRBS contains questions about parental education. The level of education that an adolescent s parents have should be correlated with family income. Adolescents with lower family income will have more trouble paying for a college education, and these students might feel increased pressure to succeed in athletics in order to get a college athletic scholarship and reduce their expected cost of attending college. Second, a few YRBS surveys contain questions about academic performance. Better students will have a greater probability of obtaining an academic scholarship, and thus reducing the expected cost of attending college. Third, a few YRBS surveys contain questions about the students reported height and weight. Several recent studies, including Perisco, Postlewaite, and Silverman (2004), show that height at adolescence is an important predictor of adult labor market outcomes. Including height as a covariate may explain adolescent steroid use, perhaps through participation in sport, and provide evidence of an economic rationale for adolescent steroid use. Finally, the CDC makes YRBS data files that contain a geographic descriptor available to researchers. In these files, the state where each student attends high school can be identified. There is considerable variation across states in opportunities to attend college, the cost of attending college, the number of institutions of higher education that offer athletic scholarships, and the pool of athletes who are talented enough to get an athletic scholarship. using the YRBS data with these geographic descriptors, we can match the YRBS data with state-level higher education variables like average room, board, and tuition and number of institutions of higher education in the state that offer athletic scholarships. If state-level variation in these factors explains any of the observed variation in adolescent steroid use, this can be interpreted as evidence that adolescent steroid use has an economic component. 14

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