Adolescent marijuana use and school attendance

Transcription

1 Economics of Education Review 23 (2004) Adolescent marijuana use and school attendance M. Christopher Roebuck a,, Michael T. French b, Michael L. Dennis c a AdvancePCS, McCormick Road, Executive Plaza II, 9th Floor, Hunt Valley, MD 21031, USA b Medical University of South Carolina, Health Administration and Policy, 19 Hagood Avenue, Suite 408, P.O. Box , Charleston, SC 29425, USA c Chestnut Health Systems, Lighthouse Institute, 720 West Chestnut, Bloomington, IL 61701, USA Received 15 May 2002; accepted 13 June 2003 Abstract This paper explores the relationship between adolescent marijuana use and school attendance. Data were pooled from the 1997 and 1998 National Household Surveys on Drug Abuse to form a sample of adolescents, aged years, who had not yet complete high school. The analysis determined the role of marijuana use in adolescent school dropout and, conditional on being enrolled, estimated the number of days truant. The potential endogeneity of marijuana use was tested in all specifications. The results indicate that any marijuana use was positively associated with school dropout and truancy in all models. However, when chronic marijuana use (weekly or more frequent) was distinguished from non-chronic marijuana use (less frequent than weekly), chronic marijuana use was found to be the dominant factor in these relationships. The results have important implications for educators, substance abuse treatment providers, and policymakers Elsevier Ltd. All rights reserved. JEL classification: I20; I10 Keywords: School dropout; Truancy; Marijuana use 1. Introduction and background Despite small recent declines, the current prevalence of marijuana use among adolescents in the United States is still nearly twice its 1990 level (Office of Applied Studies [OAS], 2000a). Marijuana has been associated with a wide range of negative consequences (Dennis, Babor, Roebuck & Donaldson, 2002; Dennis, Dwaud- Noursi, Muck & McDermeit, 2002; OAS, 2000a). For example, marijuana was the most likely substance detected in adolescent arrests overall (50%) and among those who tested positive for any drug (94%) (National Corresponding author: Tel.: ; fax: address: chris.roebuck@advancepcs.com (M.C. Roebuck). Institute of Justice [NIJ], 2000). Similarly, marijuana was mentioned in 24% of adolescent emergency department admissions (OAS, 2000b) and 20% of drug related deaths (OAS, 2000c). In addition to legal and health problems, marijuana use has also been linked to undesirable labor market outcomes. Studies of employment and labor force participation generally point to an overall negative effect of drug use (e.g. French, Roebuck, & Alexandre, 2001; Gill & Michaels, 1992; Kaestner, 1994; Register & Williams, 1992). The direct relationship between drug use and wages, however, has in some studies been reported to be positive (Gill & Michaels, 1992; Kaestner, 1991; Register & Williams, 1992). To address a possible indirect effect of drug use on earnings, Register, Williams and Grimes (2001) modeled educational attainment as a function of adolescent drug use and socio-demographic controls and found adolescent drug use reduced /$ - see front matter 2003 Elsevier Ltd. All rights reserved. doi: /s (03)

2 134 M.C. Roebuck et al. / Economics of Education Review 23 (2004) eventual educational attainment by about 1 year. Given that education has been well established in human capital models to be a strong determinant of earnings (e.g. Hotz, Xu, Tienda and Ahituv, 2002; Light, 2001), the net effect of drug use on earnings is still uncertain. Several other economic studies have linked reduced educational attainment with drug use (Bray, Zarkin, Ringwalt and Qi, 2000; Cook and Moore, 1993; Dee and Evans, 2003; Yamada, Kendrix and Yamada, 1996). In their seminal work, Cook and Moore found that heavy drinking negatively affected the number of years of education attained beyond high school. Yamada et al. modeled adolescent demand for alcohol and marijuana, and explored the relationship between the use of these substances and high school completion. These authors found monthly or more frequent marijuana use and heavy drinking to be negatively associated with the probability of graduating from high school. Subsequently, Dee and Evans questioned the results of these two studies, citing possible statistical bias introduced through the instrumental variables employed in both analyses (state-level measures of alcohol laws and taxes). Contrary to the prior studies, Dee and Evans concluded that alcohol use by teenagers did not significantly reduce educational attainment. Noting the limitations and inconclusive findings of the above studies, Bray, Zarkin, Ringwalt and Qi (2000) expanded the literature in two ways. First, they controlled for multiple substances by including measures for the initiation of marijuana, alcohol, and other drug use in the same equations. These comprehensive specifications were compared with models that included marijuana initiation only. With other substances controlled, the significant positive impact of marijuana initiation on high school dropout endured throughout the analysis. Second, Bray et al. found that the effects were comparable for 16, 17, and 18 year olds, although the magnitude and significance of the probability of dropping out of high school varied slightly with age. Despite the contributions of Bray et al. (2000), several limitations were noted in the article, one of which pertained to the data used in the study. Based upon four surveys of southeastern US public school students, the results could not be generalized to the national adolescent population. Classifying individuals according to whether or not they had initiated drug use was also cited as a limitation of the study. By this criterion, daily drug users were statistically identical to casual or experimental drug users. Thus, Bray et al. recommended that future research on the relationship between drug use and educational attainment address differing levels and frequency of use. The present study extends the literature in several ways. First, in addition to modeling the relationship between adolescent drug use and school dropout, the analysis also explores the relationship between adolescent drug use and truancy (conditional upon being enrolled). Prior studies have all measured educational attainment in whole school years, but to our knowledge none has looked at the impact of drug use on partial attendance. Second, this study overcomes the limitations of Bray et al. (2000) by pooling two recent and nationally representative US datasets to acquire a very large sample of adolescents and by examining more-frequent marijuana users (chronic) relative to less-frequent marijuana users (non-chronic) and non-users. Recent studies that have defined drug use through quantity/frequency criteria have found significant differences in employment status, health services utilization, and criminal activity among drug-using groups (Buchmueller & Zuvekas, 1998; French, McGeary, Chitwood, & McCoy, 2000; French, Roebuck, McGeary, Chitwood, & McCoy, 2001; McGeary & French, 2000). Third, the analysis controls for alcohol and other drug use by estimating models that include these variables in addition to the marijuana-use measures. 2. Models and methods To explore the hypothesis that marijuana-using adolescents were more likely to be school dropouts and truant more often, the following implicit functional form was assumed: SA f(du,i,f) (1) where SA is a measure of school attendance (either a dichotomous measure of school dropout or a count measure of truancy), DU includes a vector of drug use measures, I represents a vector of characteristics of the individual, and F is a vector of family, geographic, and socio-economic factors Dropout analysis The probability of being a school dropout was estimated using four univariate probit models. Model 1, the core model, included a single binary measure of any marijuana use during the past year. Model 2 segmented marijuana users into two discrete groups: chronic marijuana users, with weekly or more frequent use during the past year, and non-chronic marijuana users, with less than weekly use during the past year. Based upon criteria defined by the Office of National Drug Control Policy (ONDCP, 1996), these measures of chronic and nonchronic drug use have been determined to be significantly correlated with clinically-based criteria for problematic drug use, producing similar results in health services demand models (French, Roebuck, McGeary, Chitwood, & McCoy, 2001). Model 3 controlled for other drug use by starting with model 2 and then adding

3 M.C. Roebuck et al. / Economics of Education Review 23 (2004) a dichotomous measure of any drug use other than marijuana during the past year. Finally, model 4 augmented model 3 with a dichotomous measure of any alcohol use in the past month Truancy analysis To test the hypothesis that marijuana-using adolescents skipped more school days relative to non-marijuana users, the four models outlined for school dropout were estimated using zero-inflated negative binomial (ZINB) specifications. Careful analysis of the distribution of the dependent variable, a count measure of truancy (days absent from school during the past 30 days), revealed both overdispersion and excess zeros. A likelihood ratio test for overdispersion (Stata Corporation, 2001) and a Vuong (1989) statistic comparing the ZINB with the standard negative binomial model were calculated for each of the four models of truancy. All of these tests were highly significant (P 0.01), which supported the use of the ZINB over the Poisson, zero-inflated Poisson, and standard negative binomial models. Although the zero-inflation equation can be identified using instrumental variables, we relied on the nonlinearities of the model for identification since no conceptually or empirically sound instruments were available Endogeneity of marijuana use Using the univariate probit technique to estimate school dropout and the zero-inflated negative binomial regression to estimate truancy could lead to biased results if unobserved characteristics that influence both marijuana use and either dropout or truancy are correlated. To test the potential endogeneity of any, chronic, and non-chronic marijuana use in the school dropout equation, the Smith and Blundell (1986) test of exogeneity was performed on models 1 4. Similar to the Hausman Wu test (Hausman, 1983; Wu, 1973), the Smith Blundell (1986) technique involves a c 2 test of the explanatory power of the residuals from the first-stage equation (which predicted the probability of marijuana use) when added to the school dropout equation. All of these tests failed to reject the null hypothesis of the exo- 1 A growing body of literature suggests that binary measures of alcohol use may be inappropriate in economic research of substance abuse since light or occasional drinking may actually be beneficial rather than problematic (Coate, 1993; French & Zarkin, 1995; Heien, 1996; Kannel & Ellison, 1996; Marmot & Brunner, 1991; Shaper, 1990). However, because adolescent alcohol consumption is illegal in the US, our analysis did not control for the possible beneficial effects of alcohol use. 2 We were not able to identify any variable(s) conceptually related to the decision to skip school, but unrelated to the decision regarding how many days to skip. geneity of marijuana use in the school dropout specifications (P 0.05). The instrumental variables used to identify marijuana use in all of the analyses were three measures of religiosity intended to identify strongly religious individuals. Specifically, respondents were asked if they agreed or strongly agreed with the following statements: (a) religious beliefs are important to me, (b) religious beliefs influence my decisions, and (c) it is important that my friends share my religious beliefs. Religious belief measures were used as instruments in previous studies and results consistently showed a negative and significant relationship with drug use (French, Roebuck, & Alexandre, 2001; Kaestner, 1994; Register & Williams, 1992). In our analyses, tests of the joint significance of these religiosity measures in predicting marijuana use were also highly significant (P 0.01). Furthermore, likelihood ratio tests comparing the unrestricted and restricted equations were conducted. The null hypothesis that the exclusion restrictions were valid could not be rejected in any of the specifications (P 0.05), indicating that the instrumental variables were statistically appropriate in the dropout analysis (Bollen, Guilkey, & Mroz, 1995; Norton, Lindrooth, & Ennet, 1998). To further examine the potential endogeneity of chronic and non-chronic marijuana use in the dropout specifications, a first stage ordered probit was estimated where the dependent variable was an ordinal measure of marijuana use, taking on values of 0 for non-marijuana use, 1 for non-chronic marijuana use, and 2 for chronic marijuana use. The resulting linear prediction from this estimation was then entered into the second stage dropout equation for models 2 4. In all cases, the first stage linear prediction was not significant (P 0.05), thereby lending additional support for the hypothesized exogeneity of marijuana use. Finally, as a third examination of the potential endogeneity of marijuana use, seemingly unrelated bivariate probit models were estimated for the marijuana-use and dropout equations. The estimated cross-equation correlation coefficients (rho) were not significant (P 0.05) in any of the specifications. Based upon the consistency of these specification tests, marijuana use was treated as exogenous in the dropout equations, meaning that all ensuing results apply to the univariate probit models. Controlling for the potential endogeneity of marijuana use in the truancy equation was not as straightforward due to the count properties of the dependent variable. The presence of overdispersion and excess zeros in the distribution of truancy further complicated the issue. Following the endogeneity testing techniques used for the dropout equations, we first tested the significance of the residuals from the first-stage models for marijuana use when added to the ZINB regressions. The results of this exercise cast doubt on the exogeneity of marijuana use since all of these c 2 tests were significant (P 0.01).

4 136 M.C. Roebuck et al. / Economics of Education Review 23 (2004) Unlike in the dropout analysis, however, the likelihood ratio test of the validity of the excluded instruments was rejected in the truancy equations (P 0.05), thereby calling into question the results of the exogeneity tests and any two-stage estimation procedure that would utilize these instrumental variables. Nevertheless, we decided to address the potential endogeneity of marijuana use by employing a two-stage quasi-maximum likelihood (2SQML) technique, which has been shown to be more reliable than the common two-stage least squares (2SLS) approach when dealing with count data (Mullahy, 1997). Specifically, predicted probabilities of marijuana use from the first stage probit estimations were entered into the second stage ZINB regressions, and corrected standard errors were derived by bootstrapping. Estimation results for the dropout specifications and the ZINB truancy specifications are presented in the tables that follow. Given concerns about instrument reliability, however, the 2SQML results are not formally presented in a table. The full set of 2SQML estimates is available from the corresponding author upon request. 3. Data The National Household Survey on Drug Abuse (NHSDA; Substance Abuse Mental Health Services Administration [SAMHSA], 1999; 2000) was selected for its comprehensive data on illicit drug use, its oversampling of adolescents, and its nationally representative design. To increase predictive power, data were pooled from the 1997 and 1998 NHSDAs, the 17th and 18th surveys in a series that began in The NHSDA s sample design is a nationally stratified multistage area probability sample of the non-institutionalized household population of the 50 contiguous US states, age 12 or older. Various segments of the population were oversampled, including youth, minorities, and current smokers, age A total of interviews were completed in the 1997 and 1998 surveys, and the overall response rate was 78%. For the present analysis, all respondents between the ages of 12 and 18 were included if they had not either graduated high school or completed the 12th grade. Based upon these criteria, the pooled sample totaled adolescents. Although the NHSDA was one of the largest surveys of drug use ever undertaken in the US, it had certain limitations (SAMHSA, 1999; 2000). Most importantly, the data were self-reported, which raises questions regarding validity and reliability. Although the value of the data depends on respondents truthfulness and memory, a few studies have examined the validity of selfreported drug use information in this context and have found the measures to be satisfactory (Harrison and Hughes, 1997; Preston, Silverman, Schuster and Cone, 1997; Rouse, Kozel and Richards, 1985; Turner, Lessler and Devore, 1992). Another limitation of the NHSDA was the cross-sectional design. It would have been useful to analyze and report longitudinal changes in drug use patterns and how these changes were related to school attendance for a panel of individuals. Unfortunately, these issues could not be examined with the NHSDA because a new cohort was sampled every year. Therefore, the coefficient estimates presented in this paper describe correlational and not causal relationships. Table 1 presents weighted mean values (by marijuanausing status) for all variables included in all of the subsequent specifications. The sample included 858 (5.7%) chronic marijuana users, 1652 (10.9%) non-chronic marijuana users, and (83.5%) non-marijuana users. Non-parametric Kruskal Wallis (1952) rank tests were performed to identify statistically significant differences across the mutually exclusive and collectively exhaustive marijuana-using categories. As expected, the religiosity variables revealed statistically different mean values across the marijuana-use categories. Of greatest importance is the statistically significant difference in mean values for the school dropout and truancy measures. While only 2.2% of non-marijuana users were school dropouts, more than 2.5 times as many nonchronic marijuana users (5.8%), and almost six times as many chronic marijuana users (12.8%) were school dropouts. Among those adolescents enrolled in school, the average non-marijuana user skipped 0.19 days of school during the past 30 days, while the average non-chronic and chronic marijuana users skipped school 0.77 and 1.34 days, respectively. 3 Specification tests were performed to determine if coefficient estimates remained stable across the 1997 and 1998 NHSDA samples. In all models, the independent variables were interacted with the 1998 data year indicator, and a Wald test (also referred to as a Chow test) was conducted to determine the joint significance of these interaction terms. The interaction terms were never jointly significant (P 0.05), suggesting that it was appropriate to pool the data from both years. 4 Due to this complex sampling design, person-level weights were used throughout the analysis where allowed by Stata (e.g. svy commands, pweight or aweight options). 4. Results Table 2 presents the estimated coefficients from the four univariate probit models described earlier. The explanatory variables of primary importance for this paper were the substance use measures. In model 1, any marijuana use was positive and significantly related to school dropout. Specifically, the marginal effect for marijuana use on the probability of being a school dropout was This suggests that marijuana users have

5 M.C. Roebuck et al. / Economics of Education Review 23 (2004) Table 1 Weighted variable means a, by marijuana-using status Variable Chronic marijuana user Non-chronic marijuana Non-marijuana Total sample (N = 858) user (N = 1652) user (N = ) (N = ) School dropout Days truant in past 30 days Any other drug use (past year) Any alcohol use (past year) Age Male White Black Hispanic Number of moves (past year) Annual family income ($1000) Excellent health Very good health Good health Fair health Poor health New England and mid-atlantic resident North-Central resident South Atlantic resident South-Central resident Mountain resident Pacific resident NHSDA 1998 cohort Religious beliefs important Religious beliefs influence decisions Friends share religious beliefs Chronic marijuana user, weekly or more frequent marijuana use in the past year; Non-chronic marijuana user, less than weekly marijuana use in the past year; Non-marijuana user, no marijuana use in the past year; Days truant in the past 30 days is conditional on being enrolled in school.kruskal Wallis (1952) rank test was used to determine statistically significant differences in variable means across the drug-using categories: P 0.10; P 0.05; P a Weighted means estimated using final person-level sampling weights with Stata svymean command. about a one percentage point higher probability of being a dropout compared to non-marijuana users; a statistically significant yet relatively small effect. When chronic marijuana users were distinguished from non-chronic marijuana users in model 2, interesting results emerged. Chronic marijuana use likely drove much of the effect of any marijuana use in model 1, as the marginal effect for chronic marijuana use was compared to a marginal effect of for non-chronic marijuana use. Controlling for other (non-marijuana) drug use in model 3 altered the coefficient estimates of chronic and non-chronic marijuana use to a small degree, but the marginal effects and significance levels remained virtually unchanged. Somewhat surprisingly, other drug use was not significantly related to school dropout. In model 4, the significant and positive relationship between chronic marijuana use and school dropout continued to persist even when alcohol consumption was included. The coefficient estimate for non-chronic marijuana use, on the other hand, declined in both magnitude and significance in model 4, suggesting a significant correlation between alcohol consumption and non-chronic marijuana use. Aside from this result, all marijuana-use measures were positive and significantly related to school dropout in all four models. Across all specifications in Table 2, several control variables yielded noteworthy results. As one would expect, a nonlinear relationship between age and dropping out of school was present, showing the youngest and oldest adolescents at greater risk. Whites, Blacks, and Hispanics were more likely to drop out of school compared to the excluded category of mostly Asians. Adolescents in families who moved more often or had lower incomes were also more likely to be dropouts. Finally, adolescents reporting very good or excellent health were less likely to be dropouts.

6 138 M.C. Roebuck et al. / Economics of Education Review 23 (2004) Table 2 Probit models of school dropout Variable Model 1 Model 2 Model 3 Model 4 Marijuana user (any marijuana use in past year) (0.0883) [0.0093] Chronic marijuana user (weekly or more (0.1583) frequent marijuana use in past year) (0.1238) [0.0156] (0.1509) [0.0196] [0.0134] Non-chronic marijuana user (less than weekly (0.1063) (0.1107) (0.1222) marijuana use in past year) [0.0064] [0.0073] [0.0036] Other drug user (any non-marijuana drug use (0.1533) (0.1510) in past year) [ ] [ ] Alcohol user (any alcohol use in past year) (0.0920) [0.0059] Age (0.3612) (0.3601) (0.3600) (0.3587) Age squared (0.0118) (0.0118) (0.0118) (0.0117) Male (0.0755) (0.0756) (0.0752) (0.0751) White (0.1341) (0.1332) (0.1332) (0.1345) Black (0.1502) (0.1489) (0.1504) (0.1513) Hispanic (0.0819) (0.0822) (0.0822) (0.0810) Number of moves (past year) (0.0447) (0.0443) (0.0439) (0.0437) Annual family income ($1000) (0.0018) (0.0018) (0.0018) (0.0018) Excellent health (0.3300) (0.3312) (0.3297) (0.3241) Very good health (0.3312) (0.3325) (0.3313) (0.3259) Good health (0.3320) (0.3328) (0.3317) (0.3262) Fair health (0.3704) (0.3716) (0.3711) (0.3622) NHSDA 1998 cohort (0.0777) (0.0775) (0.0767) (0.0763) Constant (2.7467) (2.7371) (2.7371) (2.7280) All estimates are weighted using final person-level sampling weights with Stata svyprobit command. Standard errors reported in parentheses. Selected marginal effects reported in brackets and calculated using the mean values for the independent variables. Coefficient estimates for five geographical controls not reported. P 0.10; P 0.05; P Table 3 presents the results from the ZINB models of truancy. 5 Overall, the coefficient estimates for the drug use variables in Table 3 qualitatively mirror the results reported in Table 2 for school dropout. Namely, marijuana use was positively related to days truant in all four models, while the other drug use and alcohol use measures showed no significant relationships with truancy. Marginal effects from the ZINB regressions are reported in square brackets. In model 1, the findings suggest that marijuana users skipped school more days than 5 Due to space constraints, results from the zero-inflation equation of the truancy models are not reported. These estimates are available from the corresponding author upon request. non-marijuana users during the past 30 days. Model 2 estimates that during the past 30 days chronic marijuana users were truant more days than non-marijuana users and non-chronic marijuana users were truant more days than non-marijuana users. While the coefficient estimates for chronic and non-chronic marijuana use remained significant and roughly the same magnitude in models 3 and 4 as in model 2, corresponding marginal effects declined as other drug use and alcohol use were added to the analysis. This occurred despite the insignificance of other drug use and alcohol use in models 3 and 4. In model 4, chronic marijuana use was associated with more days truant, and nonchronic marijuana use was associated with more days truant during the past 30 days. Regarding the other

7 M.C. Roebuck et al. / Economics of Education Review 23 (2004) Table 3 Zero-inflated negative binomial models of days truant in past 30 days Variable Model 1 Model 2 Model 3 Model 4 Marijuana user (any marijuana use in past (0.2050) [0.5975] year) Chronic marijuana user (weekly or more (0.2949) [0.9826] (0.3374) [0.7544] (0.3306) [0.4877] frequent marijuana use in past year) Non-chronic marijuana user (less than (0.2518) [0.4818] (0.2467) [0.3986] (0.2508) [0.2108] weekly marijuana use in past year) Other drug user (any non-marijuana drug (0.3011) [0.1950] (0.3114) [0.1503] use in past year) Alcohol user (any alcohol use in past year) (0.2102) [0.2048] Age (1.0164) (1.0207) (0.9341) (0.9285) Age squared (0.0334) (0.0337) (0.0307) (0.0304) Male (0.1626) (0.1637) (0.1657) (0.1607) White (0.2621) (0.2654) (0.2685) (0.2595) Black (0.3151) (0.3102) (0.3023) (0.2802) Hispanic (0.2124) (0.2175) (0.2110) (0.1990) Number of moves (past year) (0.1422) (0.1357) (0.1465) (0.1357) Annual family income ($1000) (0.0031) (0.0031) (0.0031) (0.0029) Excellent health (0.5040) (0.5451) (0.5411) (0.5337) Very good health (0.4978) (0.5863) (0.5982) (0.5550) Good health (0.4845) (0.5101) (0.5011) (0.5040) Fair health (0.5446) (0.5899) (0.5870) (0.5834) NHSDA 1998 cohort (0.1922) (0.1960) (0.1990) (0.1895) Constant (7.5305) (7.5547) (6.9967) (6.9378) All estimates are weighted using final person-level sampling weights. Robust standard errors reported in parentheses. Selected marginal effects reported in brackets and calculated using the mean values for the independent variables. Coefficient estimates for five geographical controls not reported. Days truant in past 30 days is conditional on being enrolled in school. P 0.10; P 0.05; P 0.01.

8 140 M.C. Roebuck et al. / Economics of Education Review 23 (2004) control measures, only family income was significantly (negative) related to truancy. As noted earlier, 2SQML regressions were estimated to address both the count data properties of truancy and the potential endogeneity of marijuana use. In general, while the 2SQML approach yielded qualitatively similar findings to the ZINB, the marginal effects for the marijuana use measures were much larger in the two-stage models. Similar to the ZINB models, measures for alcohol and other drug use were not related to truancy. As noted earlier, however, the 2SQML results should be viewed cautiously, given concerns about instrument reliability. The full set of 2SQML estimates is available from the corresponding author upon request. 5. Discussion A general conclusion from this research is that all marijuana users are more likely to be school dropouts and, conditional on being enrolled in school, skip more school days relative to non-marijuana users. This result is consistent with findings from prior studies that have investigated marijuana use, school dropout, and educational attainment. Unlike earlier studies, however, the present paper also explored the relationships between frequency of marijuana use, school dropout, and truancy. In all specifications, weekly or more frequent marijuana use (chronic) had a larger positive marginal effect on school attendance than less than weekly marijuana use (non-chronic). Indeed, when non-chronic marijuana users were distinguished from chronic marijuana users in the dropout equation, the marginal effect of chronic marijuana use was more than four times the marginal effect of non-chronic marijuana use. When control variables for alcohol and other drug use were added to the models, relatively small and insignificant coefficient estimates emerged for these measures. Nevertheless, in seven of the eight single-stage specifications, any, chronic, and non-chronic marijuana use remained positive and significantly related to school dropout and truancy. While the exogeneity of marijuana use in the dropout models was not rejected, exogeneity was rejected in all of the truancy models. Therefore, 2SQML models were estimated and compared to the single-stage ZINB results. The marginal effects of the marijuana use measures in the 2SQML models were much larger than those from the ZINB models, but the qualitative results were similar. However, the validity of the instrumental variables employed in the 2SQML estimations was rejected, raising concerns about the reliability of the exogeneity tests and the 2SQML results. This study has several important implications for educators, substance abuse treatment providers, and policymakers. First, in general, all levels of marijuana use were associated with increased truancy and dropout. The fact that higher frequencies of use showed larger marginal effects than lower frequencies of use strengthens a common belief among educators that adolescent marijuana use is associated with increases in school attendance problems. Second, these analyses suggest that one size fits all prevention programs are probably inappropriate. While general prevention programs may be sufficient for non-users or even low-frequency users, high frequency marijuana users may require more intensive early intervention or addiction treatment. Finally, intervening with chronic marijuana users via school-based programs may be both challenging and problematic given these adolescents attend school much less than their peers. Acknowledgements Financial assistance for this study was provided by the Center for Substance Abuse Treatment (CSAT) through the Cannabis Youth Treatment Cooperative Agreement (Grant Nos. TI11317, TI11320, TI11321, TI11323, TI11324, and contract No ), and the National Institute on Drug Abuse (grant Nos. R01 DA13968, R01 DA13298, and R01 DA11506). The opinions expressed herein are those of the authors and do not reflect official positions of the US Government, AdvancePCS, Medical University of South Carolina, or Chestnut Health Systems. The authors would like to thank Pierre K. Alexandre, Sara De Ojeda, Suzanne Gresle, William Russell, and Carmen D. Martinez for their substantive and editorial contributions. Furthermore, we are very grateful for the suggestions offered by an anonymous reviewer. References Bollen, K., Guilkey, D., & Mroz, T. (1995). Binary outcomes and endogenous explanatory variables: Tests and solutions with an application to the demand for contraceptive use in Tunisia. Demography, 32, Bray, J., Zarkin, G., Ringwalt, C., & Qi, J. (2000). The relationship between marijuana initiation and dropping out of high school. Health Economics, 9, Buchmueller, T., & Zuvekas, S. (1998). Drug use, drug abuse, and labour market outcomes. Health Economics, 7, Coate, D. (1993). Moderate drinking and coronary heart disease mortality: Evidence from NHANES I and the NHANES I follow-up. American Journal of Public Health, 83, Cook, P., & Moore, M. (1993). Drinking and schooling. Journal of Health Economics, 12, Dee, T., & Evans, W. (2003). Teen drinking and educational attainment: Evidence from two-sample instrumental variables (TSIV) estimates. Jounal of Labor Economics, 21(1), Dennis, M., Babor, T., Roebuck, M., & Donaldson, J. (2002). Changing the focus: The case for recognizing and treating cannabis use disorders. Addiction, 97(S1), 4 15.

9 M.C. Roebuck et al. / Economics of Education Review 23 (2004) Dennis, M., Dawud-Noursi, S., Muck, R., & McDermeit, M. (2002). The need for developing and evaluating adolescent treatment models. In S. J. Stevens, & A. R. Morral (Eds.), Adolescent substance abuse treatment in the United States: Exemplary models from a national evaluation study (pp. 3 34). Binghamton, NY: Haworth Press. French, M., & Zarkin, G. (1995). Is moderate alcohol use related to wages? Evidence from four worksites. Journal of Health Economics, 14, French, M., McGeary, K., Chitwood, D., & McCoy, C. (2000). Chronic illicit drug use, health services utilization, and the cost of medical care. Social Science and Medicine, 50, French, M., Roebuck, M., & Alexandre, P. (2001). Illicit drug use, employment, and labor force participation. Southern Economic Journal, 68(2), French, M., Roebuck, M., McGeary, K., Chitwood, D., & McCoy, C. (2001). Using the Drug Abuse Screening Test (DAST-10) to analyze health services utilization for substance abusers in a community-based setting. Substance Use and Misuse, 36(6), Gill, A., & Michaels, R. (1992). Does drug use lower wages? Industrial and Labor Relations Review, 45(3), Harrison, L., & Hughes, A. (1997). The validity of self-reported drug use: improving the accuracy of survey estimates. NIDA Research Monograph No 167, Hausman, J. (1983). Specification and estimation of simultaneous equation models. In: Griliches, Z., Intrillgator, M.D. (Eds.), Handbook of Econometrics, Vol. I. North-Holland, Amsterdam. Heien, D. (1996). Do drinkers earn less? Southern Economic Journal, 63, Hotz, V., Xu, L., Tienda, M., & Ahituv, A. (2002). Are there returns to the wages of young men from working while in school? Review of Economics and Statistics, 84(2), Kaestner, R. (1991). The effect of illicit drug use on the wages of young adults. Journal of Labor Economics, 9(4), Kaestner, R. (1994). New estimates of the effect of marijuana and cocaine use on wages. Industrial and Labor Relations Review, 47(3), Kannel, W., & Ellison, R. (1996). Alcohol and coronary heart disease: The evidence for a protective effect. Clinica Chimica Acta, 246, Kruskal, W., & Wallis, W. (1952). Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47, Light, A. (2001). In-school work experience and the returns to schooling. Journal of Labor Economics, 19(1), Marmot, M., & Brunner, E. (1991). Alcohol and cardiovascular disease: The status of the U-shaped curve. British Medical Journal, 303, McGeary, K., & French, M. (2000). Illicit drug use and emergency room utilization. Health Services Research, 35, Mullahy, J. (1997). Instrumental-variable estimation of count data models: Applications to models of cigarette smoking behavior. The Review of Economics and Statistics, 79(4), National Institute of Justice. (2000) Annual report of drug use among adult and juvenile arrestees. NCJ Washington, DC: US Department of Justice. Norton, E., Lindrooth, R., & Ennet, S. (1998). Controlling for the endogeneity of peer substance use on adolescent alcohol and tobacco use. Health Economics, 7, Office of Applied Studies. (2000a). National household survey on drug abuse: main findings Rockville, MD: Substance Abuse and Mental Health Services Administration. Office of Applied Studies. (2000b). Mid-year 2000 emergency department data from the drug abuse warning network (DAWN). DAWN Series, D-17. Rockville, MD: Substance Abuse and Mental Health Services Administration. Office of Applied Studies. (2000c). Year-end 1999 medical examiner data from the drug abuse warning network (DAWN). DAWN Series, D-16. Rockville, MD: Substance Abuse and Mental Health Services Administration. Office of National Drug Control Policy (ONDCP). (1996). The national drug control strategy. Washington, DC: The White House. Preston, K., Silverman, K., Schuster, C., & Cone, E. (1997). Comparison of self-reported drug use with quantitative and qualitative urinalysis for assessment of drug use in treatment studies. NIDA Research Monograph No 167, Register, C., & Williams, D. (1992). Labor market effects of marijuana and cocaine use among young males. Industrial and Labor Relations Review, 45(3), Register, C., Williams, D., & Grimes, P. (2001). Adolescent drug use and educational attainment. Education Economics, 9(1), Rouse, B., Kozel, N., & Richards, L. (1985). Self-report methods of estimating drug use: Meeting current challenges to validity. Rockville, MD: National Institute on Drug Abuse. Shaper, A. (1990). Alcohol and mortality: A review of prospective studies. British Journal of Addiction, 85, Smith, R., & Blundell, R. (1986). An exogeneity test for a simultaneous equation tobit model with an application to labor supply. Econometrica, 54, Stata Corporation. (2001). Zero-inflated Poisson and negative binomial models. In: Stata reference manual release 7, Vol. 4 (pp ). College Station, TX: Stata Press. Substance Abuse and Mental Health Services Administration (SAMHSA). (1999). National household survey on drug abuse: 1997 Public release codebook. Rockville, MD: US Department of Health and Human Services. Substance Abuse and Mental Health Services Administration (SAMHSA). (2000). National household survey on drug abuse: 1997 Public release codebook. Rockville, MD: US Department of Health and Human Services. Turner, C., Lessler, J., & Devore, J. (1992). Effects of mode of administration and wording on reporting of drug use. In Survey measurement of drug use: methodological studies. Rockville, MD: National Institute on Drug Abuse. Vuong, Q. (1989). Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica, 57, Wu, D. (1973). Alternative tests of independence between stochastic regressors and disturbances. Econometrica, 41, Yamada, T., Kendrix, M., & Yamada, T. (1996). The impact of alcohol consumption and marijuana use on high school graduation. Health Economics, 5,