The impact of different drinking habits on marijuana use among college-aged youths

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The impact of different drinking habits on marijuana use among college-aged youths Zefeng Bai Bentley University, 175 Forest St, Waltham, MA, USA

a r t i c l e

i n f o

Article history: Received 2 May 2019 Received in revised form 7 August 2019 Accepted 9 August 2019 Available online xxx Keywords: Marijuana Alcohol Drinking habit College-aged youth

a b s t r a c t The present study investigates the impact of two drinking habits – moderate drinking and heavy drinking – on marijuana use among college-aged youths. Utilizing data from the National Longitudinal Survey of Youth 97 (NLSY97), this paper reveals that there is a positive association between both drinking habits and marijuana use in the long run, indicating that alcohol and marijuana are complements. However, in the short run, the association between marijuana and alcohol varies based on different drinking habits. The present study also provides evidence that underage drinking might lead to marijuana use among people younger than 21. © 2019 Western Social Science Association. Published by Elsevier Inc. All rights reserved.

1. Introduction There is a long history of discussion about the deleterious effects of marijuana and alcohol. In the short run, marijuana consumption could lead to impulsive and hostile behavior (Ansell, Laws, Roche, & Sinha, 2015). In the long run, marijuana use could adversely affect a person’s intelligence quotient (Jackson et al., 2016), short-term memory (Glascoff & Haddock, 2013), and neural response (Martz et al., 2016). On campus, marijuana use is strongly correlated with a student’s academic performance. Students who use marijuana show lower GPA compared to those who do not (Arria, Caldeira, Bugbee, Vincent, & O’Grady, 2015; Bolin, Pate, & McClintock, 2017). Likewise, it is evident that using marijuana increases the probability of college dropout, causing education discontinuity in college youths (Arria et al., 2013). For alcohol, recent studies have shown that alcohol consumption is related to impaired driving (Aston, Merrill, McCarthy, & Metrik, 2016; Volkow, Baler, Compton, & Weiss, 2014), unprotected and risky

E-mail address: [email protected]

sexual behavior (Dir et al., 2018; Metrik, Caswell, Magill, Monti, & Kahler, 2016), and property damage (White & Hingson, 2013), jeopardizing both the individual and the public safety. In order to better control substance use and implement corresponding policy, the relationship between marijuana use and alcohol consumption has been discussed extensively by both scholars and practitioners. Substance use on campus has also attracted attention from different researchers. Recently, a few studies have shown a positive association between marijuana and alcohol, suggesting that marijuana and alcohol are complements among college students (Gunn et al., 2018; O’Hara, Armeli, & Tennen, 2016). On the contrary, others have shown that marijuana and alcohol are substitutes on campus (Crost & Guerrero, 2012) and that marijuana and alcohol are complements is not evident (Crost & Rees, 2013). According to the National Survey Results on Drug Use, 1975–2017, College Students & Adults Ages 19–55, the prevalence of annual marijuana use among college students has only dropped 1% from 39% to 38% between 2016 and 2017 (Schulenberg et al., 2018). Given the fact that marijuana and alcohol remain popular on campus

https://doi.org/10.1016/j.soscij.2019.08.002 0362-3319/© 2019 Western Social Science Association. Published by Elsevier Inc. All rights reserved.

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Table 1 Descriptive Statistics – Probability of Marijuana Use by the Characteristics of Respondents. Mean

Standard deviation

Number of observation

Total

Drinking habit Non-drinking Moderate drinking Heavy drinking

0.05 0.21 0.43

0.22 0.40 0.50

10,904 16,374 4,121

31,399

Gender Female Male

0.15 0.21

0.36 0.41

15,095 16,304

31,399

Marital status Married/Cohabiting Others

0.14 0.20

0.34 0.40

8,077 23,322

31,399

Race White Black Asian American Indian Others

0.20 0.15 0.15 0.13 0.16

0.40 0.36 0.35 0.34 0.36

20,396 6,829 537 209 3,428

31,399

Ethnicity Hispanic Non-Hispanic

0.15 0.19

0.35 0.40

6,035 25,364

31,399

and the association between those two substances is still controversial, the present study attempts to investigate the relationship between marijuana and alcohol from a different perspective. Specifically, the present study examines both the long-term and the short-term impact of two drinking habits (i.e., moderate drinking and heavy drinking) on marijuana use among college-aged youths and attempts to answer the question “Do different drinking habits change the association between marijuana and alcohol?” The present study hopes that results derived from the new perspective would shed light on the controversial findings regarding the relationship between marijuana and alcohol in college-aged youths. The rest of this paper is divided as follows: Section 2 discusses the theoretical background. Section 3 introduces the data and the empirical model. Section 4 presents the results. Section 5 shows the robustness check. Section 6 discusses the findings, and Section 7 concludes the paper. 2. Theoretical background College-aged youth is one of the most vulnerable groups regarding the issue of substance use (i.e., alcohol and marijuana). In an attempt to explore possible solutions to prevent marijuana and alcohol use, various studies have shown favor of examining the consumption relationship between those two substances (Guttmannova et al., 2016; O’Hara et al., 2016; Subbaraman, 2016; Wen, Hockenberry, & Cummings, 2015). The relationship between marijuana consumption and alcohol use could be helpful in implementing potential regulations and policies that prevent individuals from accessing those substances. For instance, when marijuana and alcohol are proved to be complements, a policy that prevents marijuana use could also help reduce alcohol consumption. The situation becomes more challenging if marijuana and alcohol are substitutes, which suggests that individuals would turn to marijuana if a policy that prevents people from accessing alcohol is

implemented. Therefore, in the case of substitution, dualpolicy that prevents the use of both substances is favorable. Prior studies have also shown the possibility of exploring solutions curbing substance use in youths from a different angle. The gateway theory, so-called the steppingstone theory or the progression hypothesis, proposes that the use of substances such as alcohol and cigarettes could lead to the use of other substances such as marijuana and cocaine. Substance use, under the proposition held by the gateway theory, is not a random process. Instead, one substance could serve as a “gateway” to another (Kandel, 2002). This is often reflected by the phenomena that the use of one substance could increase the probability of using other drugs. The gateway effect of alcohol on other illicit drugs has been well-documented. For example, Kirby and Barry (2012) have found that alcohol is the gateway substance that leads to the use of tobacco, marijuana, and other illicit substances in the 12th graders. More recently, taking a further step, Barry et al. (2016) have also identified alcohol as a potential gateway substance to other illicit drugs and proposed that alcohol prevention program should begin in elementary schools (the 3rd grade). Although extensive studies have offered evidence that alcohol consumption is correlated with marijuana use, and alcohol could be the gateway substance to other illicit drugs, only a few have mentioned the likelihood of marijuana use in response to different amounts of alcohol intake. Evidence has shown that the human body could react in different ways when digesting different amounts of alcohol and the long-term persistent effect of alcohol on the human body could also vary based on different drinking habits (Fernandez-Sola, 2015). Heavy drinking and binge drinking are often found to be harmful to a person’s neuro response (Squeglia et al., 2012). Others have shown that a high dose of alcohol consumption increases the risk of depression (Boden & Fergusson, 2011). On the other hand, moderate drinking could be beneficial to the cardiovascular health of special groups of people (Fagrell et al., 1999; Nova,

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Table 2 Logistic regression and fixed effect model results. Variables

(1) Logistic (dy/dx) Marijuana use

(2) Standardized effect Marijuana use

(3) FE (dy/dx) Marijuana use

(4) Standardized effect Marijuana use

Moderate drinking

0.206*** (0.005) 0.332*** (0.006) −0.011*** (0.002) −0.016*** (0.005) −0.054*** (0.005) −0.023*** (0.003) 0.043*** (0.004) −0.004 (0.008) 0.015* (0.009) −0.031* (0.017) −0.042*** (0.007)

0.225*** (0.006) 0.335*** (0.006) −0.010*** (0.017) −0.015** (0.006) −0.071*** (0.006) −0.067*** (0.009) 0.061*** (0.006) −0.003 (0.010) 0.017* (0.010) −0.011* (0.006) −0.046*** (0.007)

0.079*** (0.005) 0.178*** (0.010) −0.010 (0.008) −0.021*** (0.006) 0.006 (0.011) −0.003 (0.002)

0.106*** (0.007) 0.155*** (0.008) −0.087 (0.069) −0.027*** (0.008) 0.006 (0.013) 0.004 (0.011)

0.447** (0.248)

0.279 (0.594)

31,399 0.034 7,858

31,399 0.034 7,858

Heavy drinking Age Marital status Education level Standardized income Gender White Black Asian Ethnicity Constant Observations R-squared Number of PUBID

31,399 0.133

31,399 0.121

Standard errors in parentheses. Year dummies and regional dummies are omitted from this output. All results are reported in marginal effects. Columns (2) and (4) are standardized marginal effects based on standardized variables in the logistic model and the fixed effect model respectively. * p < 0.1. ** p < 0.05. *** p < 0.01.

Fig. 1. Mean probability of moderate drinking (full sample) in response to the relative age of respondents (0 for respondents aged 252 months). Derived from data NLSY97. Allowing different trends on both sides.

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Table 3 RDD moderate drinking and marijuana use (full sample). Variables

Linear trend Moderate drinking

Linear trend Marijuana use

Quadratic trend Moderate drinking

Quadratic trend Marijuana use

IV Marijuana use −0.155 (0.170)

Moderate drinking 0.071 (0.013) 0.004*** (0.000)

−0.018 (0.009) −0.000 (0.000)

−0.003*** (0.001)

−0.000 (0.000)

Constant

0.576*** (0.010)

Observations R-squared

23,239 0.026

***

Post 21 Relative Age

0.161*** (0.007)

0.088 (0.019) 0.000 (0.002) −0.000** (0.000) 0.001 (0.002) 0.000 (0.000) 0.554*** (0.015)

−0.014 (0.014) −0.000 (0.001) −0.000 (0.000) −0.001 (0.002) 0.000 (0.000) 0.160*** (0.011)

23,239 0.002

23,239 0.026

23,239 0.002

*

Relative Age2 Post 21*Relative Age 2

Post 21*Relative Age

***

−0.000 (0.001) −0.000 (0.000) −0.000 (0.002) 0.000 (0.000) 0.246** (0.103) 23,239

Robust standard errors in parentheses. * p < 0.1. ** p < 0.05. *** p < 0.01.

Fig. 2. Mean probability of marijuana use associated with respondents having a moderate drinking habit (full sample). Derived from data NLSY97. Allowing different trends on both sides.

Baccan, Veses, Zapatera, & Marcos, 2012). For women, moderate drinking of up to one drink per day could reduce the likelihood of cognitive decline (Stampfer, Kang, Chen, Cherry, & Grodstein, 2005). Hence, given the fact that the impact of alcohol drinking on the human body is largely determined by the alcohol consumption amount, investigating the impact of different drinking habits on marijuana use could be helpful in explaining the still controversial consumption relationship between alcohol and marijuana in college-aged youths.

In economics, the consumption relationship between two goods are often examined via the cross-price elasticity of demand (Andreyeva, Long, & Brownell, 2010; Colchero, Salgado, Unar-Munguía, Hernandez-Avila, & Rivera-Dommarco, 2015; Grace, Kivell, & Laugesen, 2014) or the marginal effect (Azagba & Sharaf, 2014; Todd & Ver Ploeg, 2014) that demonstrates the consumption of one good in relation to another. Since the pricing data of marijuana and alcohol are relatively hard to obtain, the present study uses different econometrics approaches to estimate

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Table 4 RDD heavy drinking and marijuana use (full sample). Variables

Linear trend Heavy drinking

Linear trend Marijuana use

Quadratic trend Heavy drinking

Quadratic trend Marijuana use

0.400*** (0.148)

Heavy drinking 0.084*** (0.016) 0.003*** (0.000)

0.027** (0.013) 0.000 (0.000)

−0.004*** (0.001)

−0.002*** (0.001)

Constant

0.273*** (0.010)

Observations R-squared

13,176 0.016

Post 21 Relative Age

0.162*** (0.009)

0.113*** (0.024) −0.003 (0.002) −0.000*** (0.000) 0.002 (0.003) 0.000 (0.000) 0.239*** (0.017)

0.045** (0.019) −0.003* (0.002) −0.000* (0.000) 0.001 (0.002) 0.000 (0.000) 0.143*** (0.014)

−0.002 (0.001) −0.000 (0.000) 0.000 (0.002) 0.000 (0.000) 0.048 (0.045)

13,176 0.001

13,176 0.016

13,176 0.002

13,176 0.236

Relative Age2 Post 21*Relative Age

IV Marijuana use

Post 21*Relative Age2

Robust standard errors in parentheses. * p < 0.1. ** p < 0.05. *** p < 0.01. Table 5 RDD moderate drinking and marijuana use (restricted sample). Variables

Linear trend Moderate drinking

Linear trend Marijuana use

Quadratic trend Moderate drinking

Quadratic trend Marijuana use

0.161*** (0.028)

Moderate drinking 0.647*** (0.009) 0.000 (0.000)

0.099*** (0.009) 0.000 (0.000)

0.001 (0.000)

−0.001 (0.000)

Constant

0.000 (0.000)

Observations R-squared

16,055 0.313

Post 21 Relative Age

0.044*** (0.007)

0.642*** (0.012) 0.000 (0.000) 0.000 (0.000) 0.001 (0.002) −0.000 (0.000) 0.000 (0.000)

0.103*** (0.014) −0.000 (0.001) −0.000 (0.000) −0.001 (0.002) 0.000 (0.000) 0.043*** (0.011)

−0.000 (0.002) −0.000 (0.000) −0.001 (0.002) 0.000 (0.000) 0.043*** (0.016)

16,055 0.016

16,055 0.313

16,055 0.016

16,055 0.049

Relative Age2 Post 21*Relative Age

IV Marijuana use

Post 21*Relative Age2

Robust standard errors in parentheses. *p < 0.1. ** p < 0.05. *** p < 0.01.

the marginal effect of moderate drinking and heavy drinking associated with marijuana use in college-aged youths.

3. Material and method The present study uses data from the National Longitudinal Survey of Youth 97 (NLSY97). Data in NLSY97 were collected from multiple rounds of interviews. There were 8,984 respondents aged 12–18 who first participated in the survey in 1997. After that, follow up interviews were conducted to those youths on a yearly basis. To focus on the college-aged youths, the present study restricts the sample to data from 1997 to 2009 and respondents aged 18–24. The present study first uses a logistic model to estimate the impact of both drinking habits on marijuana use

since the dependent variable marijuana use is a binary one. The log odds ratios from the logistic model are further converted into marginal effects using the command “margins” in STATA for better interpretation. This paper further uses a fixed effect model to estimate the long-term causal effect of moderate drinking and heavy drinking on marijuana use among college-aged youths. Prior research has shown that marijuana use and alcohol consumption are closely tied to a person’s unobservable characteristics such as family environment (Lee, Brook, Finch, & Brook, 2016), personality (Pearson et al., 2018), neighborhood environment (Furr-Holden et al., 2015), and peer norms (Schuler, Tucker, Pedersen, & D’Amico, 2019), suggesting that the action of using marijuana or consuming alcohol is not a random process. Therefore, in this context, the fixed effect

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Fig. 3. Mean probability of heavy drinking (full sample) in response to the relative age of respondents (0 for respondents aged 252 months). Derived from data NLSY97. Allowing different trends on both sides.

model is an appropriate method since it allows this paper to control the unobservable heterogeneity among individuals that may lead to different drinking habits and marijuana use. For the present study, the fixed effect model controls for the unobservable individual characteristics i , the regional difference j , and the year trend t . The model also clusters the standard error at the individual level. The fixed effect model is shown as follows: Yijt = ˇ0 + ˛1 Tijt + ı1 Xijt + t + j + i + εijt In the survey, respondents were asked: “On how many days have you used marijuana in the past 30 days?” The dependent variable Yijt is a binary variable that is equal 1 if the respondent i, living in region j, in year t, has used marijuana in the past 30 days from the survey, and 0 otherwise. Tijt is a set of treatment variables containing a moderate drinking dummy and a heavy drinking dummy that divide the drinking habit into three categories. According to the definition of moderate drinking and heavy drinking from the Centers for Disease Control and Prevention (CDC), moderate drinking is defined as a female who drinks between 1 to 7 drinks a week or a male who drinks between 1 to 14 drinks a week. Heavy drinking, on the other hand, is defined as a female who drinks equal or more than 8 drinks per week or a male who drinks equal or more than 15 drinks a week (Control & Prevention, 2016). Therefore, the present study first calculates the average drinking amount of a respondent in a given week based on data from the NLSY97, and then creates the moderate drinking dummy and the heavy drinking dummy according to the average drinking amount. After all, the moderate drinking dummy is equal to 1 if the average drinking amount of a female (or male) is equal or between 1 to 7 (or 1 to 14) drinks per week and 0 otherwise. The heavy drinking dummy is equal to 1 if

the average drinking amount of a female (or male) is equal or greater than 8 (or 15) drinks per week. Xijt is a set of control variables including a respondent’s age (in years), marital status (married/cohabitating or others), education level (high school/higher or lower than high school), income (in standardized scale), gender (male or female), race (White, Black, Asian, or others), and ethnicity (Hispanic or non-Hispanic). In the next step, the present study uses a parametric regression discontinuity design (RDD) to evaluate the short-term impact of both drinking habits on marijuana use among college-aged youths. The RDD uses respondents’ age in months as a forcing variable to moderate drinking and heavy drinking. The cutoff point in the RDD is set to the legal minimum drinking age 21 years old or equivalently 252 months. When evaluating the impact of moderate drinking, the present study restricts the sample to those respondents who demonstrate a moderate drinking habit after 21. Therefore, in the RDD, the moderate drinking dummy is equal to 1 for those who have a moderate drinking habit and 0 for those who do not drink alcohol. When estimating the impact of heavy drinking on marijuana use, this paper restricts the sample to those who show a heavy drinking habit after 21. The heavy drinking dummy is equal to 1 for the youths who have a heavy drinking habit and 0 for those who do not drink alcohol. The linear RDD model is shown as follows: Tijt = ˇ1 + ˛2 Post21ijt + 1 RelativeAgeijt + 1 Post21ijt · RelativeAgeijt + εijt

Yijt = ˇ2 + ˛3 Post21ijt + 2 RelativeAgeijt + 2 Post21ijt · RelativeAgeijt + εijt

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Table 6 RDD heavy drinking and marijuana use (restricted sample). Variables

Linear trend Heavy drinking

Linear trend Marijuana use

Quadratic trend Heavy drinking

Quadratic trend Marijuana use

0.413*** (0.051)

Heavy drinking 0.356*** (0.012) 0.000 (0.000)

0.145*** (0.012) 0.000 (0.000)

−0.002*** (0.001)

−0.002*** (0.001)

Constant

0.000 (0.000)

Observations R-squared

10,029 0.156

Post 21 Relative Age

0.044*** (0.007)

0.352*** (0.017) 0.000 (0.000) 0.000 (0.000) −0.001 (0.002) −0.000 (0.000) 0.000 (0.000)

0.146*** (0.017) −0.000 (0.001) −0.000 (0.000) −0.002 (0.002) 0.000 (0.000) 0.043*** (0.011)

−0.000 (0.002) −0.000 (0.000) −0.001 (0.002) 0.000 (0.000) 0.043*** (0.015)

10,029 0.033

10,029 0.156

10,029 0.033

10,029 0.192

Relative Age2 Post 21*Relative Age

IV Marijuana use

Post 21*Relative Age2

Robust standard errors in parentheses. *p < 0.1. ** p < 0.05. *** p < 0.01.

The variable Post21ijt is a dummy variable that is equal to 1 if the respondent i, living in region j, in year t, is equal or older than 252 months (21 years) old and 0 otherwise. The variable RelativeAgeijt is a continuous variable that is equal to a respondent’s age in months relative to the minimum legal drinking age 21 (equivalently 252 months). For instance, the variable RelativeAgeijt is equal to 0 if the respondent is 252 months old and is equal to 1 if the respondent is 253 months old at the time of interview. Yijt , and Tijt are the same dependent variable and treatment variables as in the fixed effect estimator. Alternatively, this study also shows results from a non-linear RDD by adding a quadratic 2 term RelativeAgeijt to the linear RDD model above. Finally, two instrumental variable (IV) models, which use moderate drinking and heavy drinking as endogenous variables and age in months as an instrumental variable, are also presented to show the magnitude of the short impact of both drinking habits on marijuana use. To test the robustness of the fixed effect model, the present study adds a linear and non-linear regional trends to the fixed effect model. Additionally, results from the fixed effect model are compared to results from a first difference estimator, which is a different method to yield fixed effect estimation, using the same variables. The RDD estimates the causal effect of the two drinking habits on marijuana use via a comparison between the treatment group (respondents with a moderate drinking habit or a heavy drinking habit) and the control group (respondents who do not drink alcohol). Thus, it is essential to check if the change in the probability of marijuana use before and after respondents reaching 21 is merely due to the change in drinking habits. This is done by conducting a robustness check which could show if there is a significant difference in the covariates other than the treatment variables (i.e., moderate drinking and heavy drinking) before and after the cutoff point in the RDD model within a selected bandwidth. However, prior research has suggested that there is no rule-

of-thumb that could guarantee the selection of an optimal bandwidth, and therefore, multiple bandwidths should be used in the robustness check of an RDD (Imbens & Lemieux, 2008). The present study uses the commonly used method proposed by Imbens and Kalyanaraman (2009) to derive the optimal bandwidths and to conduct the discontinuity check. Since there are other methods and algorithms that could derive the optimal bandwidths, a future study may choose different methods and compare the results. Consequently, the optimal bandwidths in the robustness check range from 2 months to 2.5 months before and after the cutoff point in the four RDDs investigated in this paper. The present study also examines the possible difference in the covariates before and after the cutoff point within the 50% and the 200% of the optimal bandwidths. 4. Results Table 1 presents the descriptive statistics to the probability of marijuana use by different characteristics among college-aged youths between 18 to 24. There are 31,399 observations in the sample. Compared to those who do not drink alcohol or have a moderate drinking habit, respondents who have a heavy drinking habit show the highest average probability of marijuana use – 43%. Compared to the males who show a probability of marijuana use of 21%, females are less likely to use marijuana, showing an average probability of marijuana use of 15%. In contrast to those who are not married or cohabiting with a partner, which have a probability of marijuana use of 20%, married or cohabited individuals have a lower probability of marijuana use of 14%. Table 2 presents the results from the logistic model and the fixed effect model. Both models estimate the longterm impact of drinking habits on marijuana use. Results from the two models are accompanied by the standardized effects reported in column 2 and column 4 for the convenience of within and across studies comparison.

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Fig. 4. Mean probability of marijuana use associated with respondents having a heavy drinking habit (full sample). Derived from data NLSY97. Allowing different trends on both sides.

Fig. 5. Mean probability of moderate drinking in response to the relative age of respondents (0 for respondents aged 252 months). Sample restricted to those who never drink until 21 and then show a moderate drinking habit. Derived from data NLSY97. Allowing different trends on both sides.

The logistic model suggests that college-aged youths with a moderate drinking habit are 21% more likely to use marijuana than those who do not drink alcohol. Compared with those who do not drink alcohol, youths with a heavy drinking habit are 33% more likely to use marijuana. However, after controlling for the unobservable individual characteristics, which have the potential effects on an

individual’s drinking habit and marijuana use, college-aged youths with a moderate drinking habit or a heavy drinking habit are 8% and 18% more likely to use marijuana respectively compared to their counterparts who do not drink alcohol. The standardized effects have confirmed that heavy drinking has the strongest effect on marijuana use compared to moderate drinking and other covariates. For

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Table 7 First difference model and fixed effect model with linear and nonlinear trend results. Variables

Moderate drinking Heavy drinking Age Marital status Education level Standardized income

Fixed effect Linear regional trend Marijuana use ***

0.079 (0.005) 0.179*** (0.010) −0.010 (0.008) −0.022*** (0.006) 0.006 (0.011) 0.001 (0.004)

Fixed effect Quadratic regional trend Marijuana use

First difference D. Marijuana use

***

0.079 (0.005) 0.179*** (0.010) −0.010 (0.008) −0.021*** (0.006) 0.006 (0.011) 0.001 (0.004) 0.076*** (0.006) 0.156*** (0.011) −0.005 (0.009) −0.023*** (0.009) 0.017 (0.011) 0.000 (0.005) −0.011 (0.025) 21,039

D. Moderate drinking D. Heavy drinking D. Age D. Marital status D. Education level D. Standardized income

Observations

0.372 (0.245) 31,399

0.371 (0.244) 31,399

R-squared Number of PUBID

0.033 7,858

0.033 7,858

Constant

0.022

Robust standard errors in parentheses. Year dummies, regional dummies, and regional trend dummies are omitted from this output. Race, gender, and ethnicity are omitted from this output. * “D.” represents the coefficients in the first difference model. *p < 0.1. **p < 0.05. *** p < 0.01.

the fixed effect model, specifically, the standardized effects have shown that a 1 standard deviation increase in heavy drinking would yield a 16% standard deviation increase in the probability of marijuana use, while a 1 standard deviation increase in moderate drinking leads to an 11% standard deviation increase in the likelihood of marijuana use in the college-aged youths. Overall, both the logistic and the fixed effect models have shown a positive association between both drinking habits and marijuana use in the long run. Moreover, since the marginal effects of having a moderate or a heavy drinking habit on marijuana use vary after controlling for the unobservable individual characteristics, it is evident that there is a positive selection bias and engaging in marijuana use or alcohol consumption is not a random process among college-aged youths. Hence, the fixed effect estimator is an appropriate model for controlling the selection bias in this study. Table 3 shows the results of the short-term impact of moderate drinking on marijuana use using the RDD. For the linear trend model, compared to the time before respondents reach the minimum legal drinking age, the mean probability of moderate drinking increases by 7% after the youths reach 21. The quadratic trend model shows a 9% increase in the average probability of moderate drinking

Table 8 RD manipulation test. RD manipulation test Method Robust

T 0.1685

P>|T| 0.8662

after respondents turn 21. Additionally, turning 21 is associated with a 1% decrease in the average probability of marijuana use. However, this decrease is not statistically significant in the quadratic trend model. The IV model presents the short impact of moderate drinking on marijuana use after the youths turn 21. In the short run, there is no significant impact of moderate drinking on marijuana use. Figs. 1 and 2 illustrate the results graphically. Table 4 presents the impact of heavy drinking on marijuana use using the RDD estimator. For the linear trend model, turning 21 is associated with an 8% increase in the mean probability of heavy drinking and a 3% increase in the mean probability of marijuana use for those who show heavy drinking habit after 21. For the quadratic trend model, results show that there is an 11% increase in the average probability of heavy drinking after youths reach 21; the average probability of marijuana use, on the other

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Fig. 6. Mean probability of marijuana use associated with respondents having a moderate drinking habit. Sample restricted to those who never drink until 21 and then show a moderate drinking habit. Derived from data NLSY97. Allowing different trends on both sides.

Fig. 7. Mean probability of heavy drinking in response to the relative age of respondents (0 for respondents aged 252 months). Sample restricted to those who never drink until 21 and then show a heavy drinking habit. Derived from data NLSY97. Allowing different trends on both sides.

hand, increases by 5%. The IV model suggests that having a heavy drinking habit increases the average probability of marijuana use by 40%. Figs. 3 and 4 show the results graphically. Reviewing Figs. 1–4, the present study finds that there is a substantial number of youths who have engaged in underage drinking. This might be the reason for the insignificant effect of moderate drinking

on marijuana use reported in Table 3 since underage drinking narrows the difference of the probability of moderate drinking before and after 21. Therefore, to better estimate the impact of drinking habits on marijuana use, the present study restricts the sample to those respondents who never drink before 21 and then demonstrate a moderate drinking habit when evaluating the impact of moderate drinking. Then, the present

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Fig. 8. Mean probability of marijuana use associated with respondents having a heavy drinking habit. Sample restricted to those who never drink until 21 and then show a heavy drinking habit. Derived from data NLSY97. Allowing different trends on both sides.

Fig. 9. RD density plot by the relative age in months of respondents, reported for respondents aged between age 18 to age 24 (216 months–288 months). Derived from data NLSY97.

study restricts the sample to those respondents who never drink before 21 and then demonstrate a heavy drinking habit when evaluating the impact of heavy drinking. Table 5 presents the impact of moderate drinking on marijuana use for those who never drink before 21 and then show a moderate drinking habit. Results from the linear trend model are consistent with those from the quadratic

trend model. For the linear trend model, turning 21 is associated with a 65% increase in the average probability of moderate drinking and a 10% increase in the average probability of marijuana use. For the quadratic trend model, being eligible to drink alcohol is associated with a 64% increase in the mean probability of moderate drinking and a 10% increase in the mean probability of marijuana use. Overall, having a moderate drinking habit increases the

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Table 9 Discontinuity Check for Control Variables (Shown in Wald Estimates). Variables

Full sample Moderate drinking Marijuana use

Full sample Heavy drinking Marijuana use

Restricted sample Moderate drinking Marijuana use

Full sample Heavy drinking Marijuana use

Optimal Bandwidth

2.127

2.486

2.029

2.33

Marital status

0.350 (0.614) 0.268 (0.551) 0.111 (0.700) −0.497 (0.777) 0.539 (0.710) 0.038 (0.584) −0.181 (0.231) −0.480 (0.618)

0.407 (1.096) 0.149 (1.023) −0.273 (1.272) 0.651 (1.325) 0.100 (1.146) 0.923 (1.576) −0.174 (0.358) −1.444 (1.821)

0.243 (0.159) 0.117 (0.412) 0.211 (0.187) −0.148 (0.192) 0.147 (0.523) −0.019 (0.475) −0.057 (0.142) −0.159 (0.449)

−0.229 (0.332) 0.013 (0.344) 0.473 (0.388) −0.005 (0.398) 0.177 (0.397) 0.120 (0.371) −0.077 (0.121) −0.679* (0.371)

0.810 (0.938) 0.723 (0.807) 0.252 (0.850) −0.518 (0.918) 0.639 (0.872) 0.107 (0.714) −0.268 (0.301) −0.487 (0.750)

−1.039 (6.297) 0.841 (6.115) −0.543 (6.104) 9.069 (42.864) −0.695 (6.968) 6.677 (33.296) −0.449 (2.499) −9.488 (45.251)

0.079 (0.075) 0.141* (0.077) 0.111 (0.091) 0.005 (0.093) 0.180** (0.092) −0.108 (0.084) −0.029 (0.029) −0.102 (0.079)

−0.018 (0.182) 0.078 (0.196) 0.277 (0.224) 0.356 (0.228) 0.271 (0.224) −0.053 (0.210) −0.048 (0.071) −0.342* (0.189)

0.238 (0.566) 0.348 (0.543) 0.045 (0.666) −0.036 (0.663) 0.425 (0.661) 0.102 (0.575) −0.187 (0.223) −0.150 (0.536)

−1.080 (3.398) 1.398 (4.238) −1.834 (5.563) 5.247 (13.241) −0.467 (3.296) 3.391 (9.451) −0.188 (0.883) −5.099 (13.011)

0.017 (0.076) 0.156** (0.072) 0.050 (0.089) 0.034 (0.090) 0.276*** (0.088) −0.176** (0.079) −0.017 (0.024) −0.075 (0.074)

−0.088 (0.179) 0.059 (0.192) 0.203 (0.218) 0.407* (0.224) 0.146 (0.223) −0.004 (0.207) −0.039 (0.072) −0.285 (0.188)

23,239

13,176

16,055

10,029

Education level Standardized income Gender White Black Asian Ethnicity 50% Optimal Bandwidth Marital status Education level Standardized income Gender White Black Asian Ethnicity 200% Optimal Bandwidth Marital status Education level Standardized income Gender White Black Asian Ethnicity Observations Standard errors in parentheses. * p < 0.1. ** p < 0.05. *** p < 0.01.

average probability of marijuana use by 16%. Figs. 5 and 6 show the results graphically. Table 6 shows the association between heavy drinking and marijuana use for college-aged youths who never drink before 21 and then show a heavy drinking habit. For the linear trend and the quadratic models, turning 21 is associ-

ated with a 36% and a 35% increase in the mean probability of heavy drinking respectively. Both the linear trend model and the quadratic trend model suggest a 15% increase in the average probability of marijuana use when college-aged youths reach 21. After all, the IV model suggests that having a heavy drinking habit increases the average probability

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Table 10 RDD moderate drinking and marijuana use with control variables (full sample). Variables

Linear trend Moderate drinking

Linear trend Marijuana use

Quadratic trend Moderate drinking

Quadratic trend Marijuana use

−0.063 (0.176)

Moderate drinking Post 21 Relative Age

0.081 (0.013) 0.003*** (0.001)

−0.018 (0.010) 0.001* (0.000)

−0.003*** (0.001)

−0.001** (0.001)

−0.076*** (0.008) 0.083*** (0.008) 0.031*** (0.006) 0.039*** (0.006) 0.047*** (0.012) −0.157*** (0.014) −0.028 (0.026) −0.076*** (0.010) 0.395 (0.254) 23,239 0.078

***

−0.017*** (0.006) −0.052*** (0.006) −0.010** (0.004) 0.056*** (0.005) 0.016* (0.008) 0.005 (0.010) −0.031* (0.018) −0.047*** (0.007) 0.257 (0.207)

0.082 (0.019) 0.003 (0.002) −0.000 (0.000) −0.002 (0.002) 0.000 (0.000) −0.076*** (0.008) 0.083*** (0.008) 0.031*** (0.006) 0.039*** (0.006) 0.047*** (0.012) −0.157*** (0.014) −0.028 (0.026) −0.077*** (0.010) 0.395 (0.254)

−0.005 (0.014) −0.001 (0.001) −0.000 (0.000) 0.001 (0.002) 0.000 (0.000) −0.017*** (0.006) −0.053*** (0.006) −0.010** (0.004) 0.056*** (0.005) 0.016* (0.008) 0.005 (0.010) −0.031* (0.018) −0.047*** (0.007) 0.256 (0.208)

23,239 0.020

23,239 0.078

23,239 0.020

*

Relative Age2 Post 21*Relative Age Post 21*Relative Age2 Marital status Education level Standardized income Gender White Black Asian Ethnicity Constant Observations R-squared

IV Marijuana use

***

−0.001 (0.002) −0.000 (0.000) 0.001 (0.002) 0.000 (0.000) −0.022 (0.015) −0.048*** (0.016) −0.008 (0.007) 0.058*** (0.008) 0.019 (0.012) −0.005 (0.030) −0.032 (0.020) −0.052*** (0.015) 0.281 (0.196) 23,239

Robust standard errors in parentheses. Year dummies and regional dummies are omitted from this output. * p < 0.1. ** p < 0.05. *** p < 0.01.

of marijuana use by 41%. Figs. 7 and 8 present the results graphically. By restricting the sample to the college-aged youths who never drink until they are eligible to purchase alcohol, the present study shows that the impact of both drinking habits on marijuana use is significant and positive in the short run. However, the moderate drinking effect on marijuana use is insignificant when including college-aged youths who have drunk alcohol before their 21. The difference between those results can be attributed to the effect of underage drinking in college-aged youths, which narrows down the probability difference of engaging in moderate drinking before and after 21, resulting in an insignificant association between moderate drinking and marijuana use in the RDD for the full sample. Further evidence can be seen when comparing Figs. 2 and 6, which shows that the probability of marijuana use before 21 when using the full sample is significantly higher than that when using the restricted sample. Therefore, underage drinking might be a reason for marijuana use among college-aged youths younger than 21. Moreover, results for heavy drinking are consistent in both the full sample and the restricted sample, showing that heavy drinking increases the probability of marijuana use regardless of a respondent’s drinking status before 21.

5. Robustness check Table 7 presents results from the robustness check of the fixed effect model. Results from the fixed effect model with a linear or a non-linear regional trend are consistent with those in the original fixed effect estimator – college-aged youths who show a moderate drinking or a heavy drinking habit are 8% and 18% more likely to use marijuana respectively compared to those who do not drink alcohol. The first difference model suggests that in contrast to those who do not drink alcohol, youths with a moderate drinking habit or a heavy drinking habit is 8% and 16% more likely to use marijuana on average. Therefore, the present study considers that results from the fixed effect model are significant and robust. The robustness check to the RDD is conducted from two perspectives – the possible discontinuity of the forcing variable (relative age measured in months) and the potential discontinuity of other control variables that may affect the change of the probability of marijuana use at age 21. From a theoretical perspective, people do not have the capability to manipulate their age. As expected, Fig. 9 shows that there is no significant discontinuity in the forcing vari-

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Table 11 RDD heavy drinking and marijuana use with control variables (full sample). Variables

Linear trend Heavy drinking

Linear trend Marijuana use

Quadratic trend Heavy drinking

Quadratic trend Marijuana use

0.415*** (0.142)

Heavy drinking Post 21 Relative Age

0.088*** (0.015) 0.003*** (0.001)

0.025* (0.013) 0.001** (0.000)

−0.068*** (0.008) −0.039*** (0.008) −0.013** (0.006) 0.047*** (0.007) 0.018 (0.011) −0.104*** (0.012) −0.120*** (0.023) −0.078*** (0.010) 0.316 (0.210)

0.118*** (0.022) −0.002 (0.002) −0.000*** (0.000) 0.002 (0.003) 0.000* (0.000) −0.162*** (0.009) 0.006 (0.009) 0.044*** (0.008) 0.053*** (0.008) 0.046*** (0.014) −0.232*** (0.015) −0.193*** (0.029) −0.094*** (0.012) 0.387* (0.210)

0.049*** (0.019) −0.004** (0.002) −0.000*** (0.000) 0.003 (0.002) 0.000 (0.000) −0.069*** (0.008) −0.043*** (0.008) −0.013** (0.006) 0.046*** (0.007) 0.018 (0.011) −0.104*** (0.012) −0.120*** (0.023) −0.078*** (0.010) 0.312 (0.211)

−0.003* (0.001) −0.000* (0.000) 0.002 (0.002) 0.000 (0.000) −0.002 (0.024) −0.045*** (0.007) −0.031*** (0.008) 0.024** (0.010) −0.001 (0.012) −0.008 (0.035) −0.040 (0.037) −0.039** (0.016) 0.151 (0.171)

−0.004*** (0.001)

−0.003*** (0.001)

−0.161*** (0.009) 0.011 (0.009) 0.043*** (0.008) 0.054*** (0.008) 0.046*** (0.014) −0.232*** (0.015) −0.194*** (0.029) −0.094*** (0.012) 0.392* (0.209) 13,176 0.119

13,176 0.043

13,176 0.120

13,176 0.044

13,176 0.245

Relative Age2 Post 21*Relative Age Post 21*Relative Age2 Marital status Education level Standardized income Gender White Black Asian Ethnicity Constant Observations R-squared

IV Marijuana use

Robust standard errors in parentheses. Year dummies and regional dummies are omitted from this output. * p < 0.1. ** p < 0.05. *** p < 0.01.

able at age 21. The RD manipulation test is also insignificant as shown in Table 8. Table 9 presents the discontinuity check for the control variables in the RDD using either the full sample or the restricted sample. Overall, within the optimal bandwidth, only ethnicity is statistically significant at a 0.1 significance level. However, there are some control variables that are statistically significant within the 50% and the 200% optimal bandwidths. Hence, the present study repeats the RDD (both full sample and restricted sample) analysis with all the control variables examined in the discontinuity check. Tables 10 and 11 show the results of the RDD using the full sample with control variables. In the short run, the impact of moderate drinking on marijuana use is not significant while heavy drinking is associated with a 42% (40% in the RDD without the control variables) increase in the average probability of marijuana use. This result is consistent with those from the RDD using the full sample but without the control variables. Tables 12 and 13 present the results of the RDD using the restricted sample with control variables. Overall, moderate drinking and heavy drinking increase the average probability of marijuana use by 17% (16% in the RDD without the control variables) and 42% (41% in the RDD without the

control variables) respectively, which are consistent with the results from the RDD using the restricted sample but without control variables. To sum up, the present study considers that results from the RDD are robust. 6. Discussion Due to the still significant prevalence of substance use in college-aged youths, this study aims at investigating the long-term and the short-term impact of two drinking habits (i.e., moderate drinking and heavy drinking) on marijuana use using a fixed effect model and an RDD. This paper has made several contributions to the literature studying the consumption relationship between alcohol and marijuana. First, the present study has found that, in the long run, both moderate drinking and heavy drinking could lead to an increase in the probability of marijuana use among college-aged youths. The relationship between alcohol and marijuana does not vary based on the two drinking habits examined in this paper when looking at the long-term effect. This confirms prior research that alcohol and marijuana are complements (Gunn et al., 2018; O’Hara et al., 2016; Pape, Rossow, & Storvoll, 2009; Williams, Liccardo Pacula, Chaloupka, & Wechsler, 2004) and those two sub-

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Table 12 RDD moderate drinking and marijuana use with control variables (restricted sample). Variables

Linear trend Moderate drinking

Linear trend Marijuana use

Quadratic trend Moderate drinking

Quadratic trend Marijuana use

0.174*** (0.030)

Moderate drinking Post 21 Relative Age

0.628*** (0.009) −0.001** (0.000)

0.095*** (0.010) 0.001** (0.000)

−0.021*** (0.006) −0.039*** (0.007) −0.007 (0.005) 0.048*** (0.005) 0.015* (0.009) 0.011 (0.010) −0.027 (0.019) −0.032*** (0.007) 0.031 (0.032)

0.609*** (0.013) 0.002*** (0.001) 0.000*** (0.000) −0.001 (0.002) −0.000* (0.000) −0.074*** (0.008) 0.077*** (0.008) 0.031*** (0.006) 0.041*** (0.007) 0.045*** (0.013) −0.115*** (0.014) −0.013 (0.027) −0.061*** (0.011) −0.021 (0.049)

0.106*** (0.014) −0.001 (0.001) −0.000 (0.000) 0.000 (0.002) 0.000 (0.000) −0.021*** (0.006) −0.040*** (0.007) −0.007 (0.005) 0.048*** (0.005) 0.015* (0.009) 0.011 (0.010) −0.027 (0.019) −0.032*** (0.007) 0.028 (0.031)

−0.001 (0.002) −0.000 (0.000) 0.000 (0.002) 0.000 (0.000) −0.008 (0.006) −0.053*** (0.007) −0.012*** (0.005) 0.040*** (0.005) 0.007 (0.009) 0.031*** (0.011) −0.024 (0.020) −0.021*** (0.008) 0.031 (0.218)

0.001 (0.000)

−0.001*** (0.000)

−0.074*** (0.008) 0.076*** (0.008) 0.031*** (0.006) 0.041*** (0.007) 0.045*** (0.013) −0.115*** (0.014) −0.013 (0.027) −0.061*** (0.011) −0.026 (0.048) 16,055 0.350

16,055 0.031

16,055 0.350

16,055 0.031

16,055 0.062

Relative Age2 Post 21*Relative Age Post 21*Relative Age2 Marital status Education level Standardized income Gender White Black Asian Ethnicity Constant Observations R-squared

IV Marijuana use

Robust standard errors in parentheses. Year dummies and regional dummies are omitted from this output. * p < 0.1. ** p < 0.05. *** p < 0.01.

stances are often consumed together among college-aged youths. This is not hard to explain since college students are often found to drink in a social context where both alcohol and marijuana are accessible (Hall & Lynskey, 2005) and college youths usually drink and use marijuana for social facilitation (Beck et al., 2008). Therefore, the present study supports the gateway theory that alcohol could serve as a potential “gateway” substance leading to marijuana use due to the positive consumption relationship in collegeaged youths. A notable finding in the present study is that, based on the standardized effect, college-aged youths with a heavy drinking habit are more likely to use marijuana than those with a moderate drinking habit or do not drink alcohol. Thus, adding to the literature, the potential gateway effect of alcohol leading to marijuana use could be much stronger in the context of heavy drinking than moderate drinking. This is not surprising given the findings in the past studies that the behavior of an individual after drinking alcohol differs from the drinking amount and the deleterious outcomes (e.g., hypertension, risky sexual behavior, and impulsivity) of alcohol consumption are more pronounced in a heavy drinking than in a moderate drinking context (Briasoulis, Agarwal, & Messerli, 2012; Metrik et al., 2016; White et al., 2011). Second, distinct from

prior research, the present study has found that the association between alcohol and marijuana could vary based on the drinking habit a person has. Specifically, in the short run, heavy drinking is significantly and positively correlated with marijuana use regardless of the drinking status of the college-aged respondents before 21, showing that alcohol has a strong positive spillover effect on marijuana use for those with a heavy drinking habit. For the youths with a moderate drinking habit, however, the present study finds no significant association between alcohol consumption and marijuana use for the full sample. Further examination reveals that this insignificant result could be attributed to the effect of underage drinking, which narrows the difference in the probability of moderate drinking before and after 21. Looking at the restricted sample, for those who never drink until 21 and then show a moderate drinking habit, the short-term impact of moderate drinking on marijuana use is positive and significant. Hence, as expected, underage drinking might be an important reason that leads to marijuana use for college-aged youths under 21. This finding ties well with prior studies that underage drinking could lead to illicit drug use among adolescents (Komro & Toomey, 2002; Miller, Naimi, Brewer, & Jones, 2007) and this result also substantiates the gateway effect

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Table 13 RDD heavy drinking and marijuana use with control variables (restricted sample). Variables

Linear trend Heavy drinking

Linear trend Marijuana use

Quadratic trend Heavy drinking

Quadratic trend Marijuana use

0.421*** (0.055)

Heavy drinking Post 21 Relative Age

0.332*** (0.012) 0.001*** (0.000)

0.132*** (0.012) 0.001*** (0.000)

−0.058*** (0.008) −0.043*** (0.008) −0.014** (0.006) 0.039*** (0.006) 0.025** (0.011) −0.047*** (0.012) −0.071*** (0.021) −0.045*** (0.009) 0.056 (0.045)

0.334*** (0.017) 0.000 (0.001) −0.000 (0.000) −0.001 (0.002) 0.000 (0.000) −0.141*** (0.010) −0.005 (0.008) 0.034*** (0.008) 0.047*** (0.007) 0.036*** (0.013) −0.152*** (0.015) −0.109*** (0.028) −0.063*** (0.011) 0.075 (0.076)

0.140*** (0.017) −0.001 (0.001) −0.000 (0.000) −0.000 (0.002) 0.000 (0.000) −0.059*** (0.008) −0.045*** (0.008) −0.014** (0.006) 0.039*** (0.006) 0.025** (0.011) −0.047*** (0.012) −0.071*** (0.021) −0.045*** (0.009) 0.052 (0.045)

−0.001 (0.002) −0.000 (0.000) 0.000 (0.002) 0.000 (0.000) 0.001 (0.010) −0.043*** (0.007) −0.028*** (0.006) 0.019*** (0.006) 0.010 (0.011) 0.017 (0.015) −0.025 (0.025) −0.018* (0.010) 0.020 (0.202)

−0.002*** (0.001)

−0.003*** (0.001)

−0.141*** (0.010) −0.004 (0.008) 0.034*** (0.008) 0.047*** (0.007) 0.036*** (0.013) −0.152*** (0.015) −0.110*** (0.028) −0.063*** (0.011) 0.077 (0.076) 10,029 0.229

10,029 0.063

10,029 0.229

10,029 0.063

10,029 0.199

Relative Age2 Post 21*Relative Age Post 21*Relative Age2 Marital status Education level Standardized income Gender White Black Asian Ethnicity Constant Observations R-squared

IV Marijuana use

Robust standard errors in parentheses. Year dummies and regional dummies are omitted from this output. * p < 0.1. ** p < 0.05. *** p < 0.01.

of alcohol connecting to other drug use advocated by the gateway theory (Kirby & Barry, 2012). Findings in this paper have important implications for controlling alcohol and marijuana use since college is a crucial phase wherein the youths grow up to the age of being eligible to drink alcohol. The present study has documented alcohol and marijuana as complements, highlighting that alcohol has a potential gateway effect leading to marijuana use among college-aged youths. Thus, policy and intervention program that reduce alcohol consumption could also bring the benefit of controlling marijuana use as well as the public health issues such as impaired driving (Volkow et al., 2014), risky sexual behavior (Bryan, Schmiege, & Magnan, 2012), and intimate partner violence (Nabors, 2010) associated with marijuana use on campus. Additionally, the present study has shown that underage drinking could lead to marijuana use in adolescents younger than 21. This finding is particularly important given that approximately one third of the high school students in the U.S. had engaged in underage drinking within 30 days prior to the Youth Risk Behavior Surveillance survey in 2017 (Kann et al., 2018) and it is evident that underage drinking is strongly correlated with other deleterious effects such as violent

offending (Healey, Rahman, Faizal, & Kinderman, 2014) and poor academic performance (Hingson & White, 2014). Therefore, precautions such as the legal minimum drinking age law and other early-age intervention programs that prevent college-aged youths younger than 21 from accessing alcohol can also help reduce marijuana use (Krauss, Cavazos-Rehg, Agrawal, Bierut, & Grucza, 2015). Despite the contributions, it is also worth to mention the limitations in the present study. Benefiting from the NLSY97, the present study uses a panel dataset that captures the individual-level variations of drinking habits and marijuana use over time. However, it is also important to note that data from the NLSY97 are self-reported. The present study uses a parametric RDD due to the availability of data. Hence, a linear trend model and a quadratic trend model are fitted to estimate the impact of both drinking habits on marijuana use. Although results are consistent in those two estimators, a future study could conduct a nonparametric RDD to free the analysis from the predetermined trend assumption. Additionally, apart from the drinking habits, other individual characteristics that have the potential to affect the association between alcohol consumption and marijuana use such as drinking culture

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