rae - final submission1

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Department of Economics

University of Warwick

Research in Applied Economics EC331

2015 - 2016

1304164

Price or Preference? The economic relationship

between alcohol and marijuana

A study of American Universities from 2002-2014

Word Count: 4,833

Abstract

This article investigates the relationship between marijuana prices and the use of marijuana

and alcohol for American university students aged 18 - 29 in the National Survey on Drug

Use and Health data from 2002 to 2014. Previous studies yield mixed evidence regarding the

economic relationship and have preliminarily utilised proxies to account for pricing effects. I

implement an instrumental variable specification that identifies drug use with variables that

are empirically unrelated to consumption of marijuana and alcohol. Results indicate that

marijuana and alcohol are gross substitutes. Alterations to the model through legal drinking

age change this relationship. The analysis is enriched by observing changing desires to

consume marijuana and alcohol for different age groups and those in different years of

university. Exogeneity tests reveal that the standard Probit estimates for alcohol

consumption are severely biased towards one.

**Acknowledgements: I would like to thank Dr. Robert Akerlof for his immense support and guidance as a supervisor, Dr. Rocco d’Este, Dr. Jeremy Smith and Dr. Piotr Jelonek for their insight on empirical techniques, SAMHSA for the data and Ms. Anuria Singh, Mr. Hameem Raees Chowdhury, Ms. Sophia Karanicholas and Ms. Harshini Singh for their comments and encouragement. Any remaining errors and omission are my own.

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Table of Contents

ABSTRACT ........................................................................................................................................... 1

1. INTRODUCTION ........................................................................................................................ 3

2. LITERATURE AND THEORETICAL FRAMEWORK .......................................................... 4 2.1 RELATED LITERATURE ........................................................................................................................ 4 2.2 THEORETICAL FRAMEWORK ............................................................................................................... 5

3. DATA AND SUMMARY STATISTICS ..................................................................................... 6 3.1 DATASET ................................................................................................................................................ 6 3.2 VARIABLES ............................................................................................................................................. 7

3.2.1 Consumption likelihood measures .......................................................................................... 7 3.2.2 Pricing measure ............................................................................................................................... 7 3.2.3 Control Variables ............................................................................................................................ 8 3.2.4 Correlations among variables .................................................................................................. 8

4. RESEARCH METHODOLOGY .................................................................................................. 9 4.1 MAIN MODEL AND ESTIMATION METHOD ......................................................................................... 9 4.2 ENDOGENEITY ....................................................................................................................................... 9

5. RESULTS ....................................................................................................................................10 5.1 ARE MARIJUANA AND ALCOHOL ECONOMIC COMPLEMENTS OR SUBSTITUTES? .......................... 10 5.2 DO AGE AND UNIVERSITY YEAR AFFECT DESIRE TO CONSUME SUBSTANCES? .............................. 11 5.3 ARE UNIVERSITY STUDENTS OF LEGAL DRINKING AGE MORE LIKELY TO CONSUME ALCOHOL? 11

6. ROBUSTNESS CHECKS ...............................................................................................................15

7. DISCUSSION AND CONCLUDING REMARKS .......................................................................15

8. APPENDIX .....................................................................................................................................17 8.1 APPENDIX I: TABLES ............................................................................................................................. 17 8.2 APPENDIX II: FIGURES .......................................................................................................................... 24

9. REFERENCES ................................................................................................................................25

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1. Introduction

Substance abuse involving young adults has been an evolving phenomenon characterised by

growing affluent nations, requiring frequent and structured reassessment. Marijuana is

being seen as less and less of a 'problem drug' and more like a stress relaxant, advocated by

a rising number of young adults who have a strong impact on university students.1 There is a

common perception that marijuana (whose accessibility is prominent despite its illegal

status) is healthier and a less damaging option than alcohol. Due to the very nature of

marijuana, combining it with alcohol is hazardous which makes it interesting to note why

past research has led to viewing the pair as economic complements.2

Luthar (2003) provided an interesting outlook when she identified that inner-city students

showed more frequent usage of marijuana and alcohol. Material wealth, in her opinion, had

long lasting implications on the culture and psychological costs imparted onto children from

affluent backgrounds. Yet prior research has yet to look at how affluence affects preferences

towards the pair. There may be an underlying factor that promotes a psychological view that

marijuana is deemed a higher social class means of intoxication with alcohol being viewed as

the layman's substance (Thies and Register, 1993).

This study investigates the economic relationship between two commonly used substances

in American universities - alcohol and marijuana. By assessing own and cross-price effects, it

is possible to identify whether the goods are economic complements or substitutes.

Theoretical views present two alternating hypothesis. Marijuana and alcohol can be viewed

as either complements or substitutes, and in both cases, support standard consumer utility

maximisation.3 Empirical results from variations of a standard Probit model used to model

desire to consume either marijuana or alcohol are applied to test the theoretical hypothesis.

There is a particular focus as to whether an individual is of legal drinking age (LDA) affects

this relationship. There is reason to believe that the pair of substances are interrelated

(Cameron and Williams, 2001). Cannabis and alcohol consumption provide the similar initial

euphoric effects, albeit with differing end results.

Results from the analysis suggest that marijuana and alcohol are gross substitutes. However

variations to the model - including testing the individual's legal status suggest that for higher

marijuana prices the desire to consume alcohol, while positive, declines relative to previous

prices. Factors such as gender and geographic location are insignificant in contrast to prior

work. From a policymaker's perspective, this paper provides key insight into consumption

patterns at university and is a stepping-stone towards drug policy development. For instance

if marijuana and alcohol are in fact economic complements, a nation wide per-unit tax on

marijuana will dampen binge-drinking at universities, a high-priority problem to counter in

recent years (Hingson et al., 2009).

1 See Amonini (2005) for how youth perception influences substance use 2 There may be a time lag between consumption of alcohol and marijuana that would be driving this relationship. 3 See section 2.2 for Theoretical Framework on utility maximisation

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2. Literature and Theoretical Framework

2.1 Related Literature

The premise that alcohol and marijuana may satisfy similar consumption desires and as a

result, the restriction of one, leads to the increase in consumption of the other is not new.

Post the Volstead Act of 19204, the prohibition of alcohol led to the first signs of increased

recreational use of marijuana5. DiNardo and Lemieux (1992) were the first to research

whether youth substitute alcohol for marijuana6. They used utility maximisation constraints

to analyse the effects of an increase in minimum drinking age on alcohol and drug

consumption and whether decriminalising marijuana had any effect on the two. They

speculated how the presence of legal restraints at state level would deter marijuana or

alcohol consumption and amplify the substitution relationship. The results advocated that

an increase in minimum drinking age led to an increase in the use of marijuana among

youth, and decriminalising marijuana led to significant declines in alcohol consumption.7

From these two results, they concluded that alcohol and marijuana were in fact economic

substitutes.

Alternatively, Chaloupka and Laixuthai (1994) examined the effects of pricing on alcohol

substitutability with marijuana. They added information from the American Chamber of

Commerce Researchers Association (ACCRA) to examine full and part pricing effects of

alcohol in tandem with the legalisation of marijuana. They accounted for this relationship by

monitoring traffic related fatalities to proxy for substance abuse and induced policy changes8

to identify the substitution effect between the two goods. There was an inherent thought of

how the price effect would create a substitution-based relationship. However, the analysis

was lacking in areas with regards to money measurement problems, where it became

difficult to place an approximate value on marijuana. Later research done by Saffer &

Chaloupka (1999) negated this view.

Similarly, Pacula (1998), worked with pricing effects to examine whether rising beer taxes

would have an impact on marijuana. She used individual demand level equations and a static

(censoring) model to better test the economic relationship along with using data from the

National Longitudinal Survey of Youth. Her analysis revealed that an increase in the federal

tax on beer was found to have a larger unconditional decline in marijuana than alcohol,

making the pair complements.9 Her view was built upon how exogenous price effects would

lead to an alternative outcome to past results. The analysis extended to include a racial

4 First infringed in Chicago although implemented across the 50 states. 5 The evidence for this came from the sudden use of marijuana ‘tea bags’ in New York City. 6 Utilising large sample survey data across 43 US states from the Monitoring the Futures Survey 7 Although the latter had no effect on marijuana use 8 Moving from where marijuana is fully criminalised to where it is fully decriminalised 9 Taxes were used to proxy pricing effects as they were thought to better address policy questions and show fewer measurement errors.

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break up where Pacula identified the prominence of a racial bias.10 It was interesting to note

that the majority of individuals getting caught for possession were under the influence

themselves, which would possibly distort the results.

Supporting work by Williams et al (2001) looked at alcohol use amongst college students on

the basis of Congress's Drug-Free Schools and Communities Act 1986. They utilised the

Harvard School of Public Health College Alcohol Study (1993, 1997, 1999) along with the

Illegal Drug Price/Purity Report (IDPPR) published by U.S. Department of Justice as a way of

segmenting potential effects on the basis of marijuana quality. By examining own and cross

price effects across 30 day prevalence equations, the group identified that marijuana may

form a broader social trend of consumption that isn't inherently linked to alcohol but

validates it as being an economic complement. People’s perception of the ‘legal cost’ of

using marijuana and how the relative views could alter the cross-price elasticity was their

key concern. A gender break up suggested that females showed the most prevalent effects

on alcohol and marijuana consumption in regards to a full alcohol ban. The results, while

significant, only highlighted the effect under certain price specifications and still left room as

to whether unobservable factors (e.g. cigarette use) are driving relationship between the

two variables. Indeed, Crost and Guerrero (2012) found that, by IV estimation, common

influencers between alcohol and marijuana, the two goods return back to being economic

substitutes. Their RDD model11 worked well to monitor the causal effect of the minimum

legal drinking age on the substitutability between alcohol and marijuana with more

significant effects being identified for men, contradicting the previous study done by

Williams et al (2001).

To the author’s knowledge, this paper is the first of its kind to directly examine pricing

effects through a standardised form of marijuana, in contrast to prior results that utilised

taxation, prohibition or alternative proxies to determine the economic relationship. Williams

et al (2001) pioneered categorical pricing effects by observing how the economic

relationship changed for different qualities of marijuana. Building on, this study focuses on

how categorical pricing effects are altered by an individual's’ legal status and demographic

characteristics respectively.

2.2 Theoretical Framework Consumer Utility Maximisation

Standard individual utility maximisation theory purports increasing utility in respect to increased consumption. Rational consumers have monotonically increasing consumption functions, don't like high prices, and that this effect is amplified for university students who on average are on a restricted budget (usually on student loans).

10 African-Americans were more likely to be accosted and arrested for possession 11 Regression Discontinuity Design models were first introduced by Thistlethwaite and Campbell

(1960) as a way of identifying treatment effects on individuals in a non-experimental way. Treatment

was determined by whether the observable variable passed a 'cut-off' point.

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The following effects can be identified:

∂F(Consumption Function) = "Positive" ∂goodi

∂F(Consumption Function) = "Negative"

∂pricei

This study restricts utility maximisation to maximising consumer surplus and discounts for exogenous factors (such as altruism) that may affect individual utility. Purchasing behaviour is driven by prices, with consumer surplus shifting outwards for an increase in price by a substitute and inwards for a complement. Wealth Effects and Changing Preferences Individual responsiveness to price changes in relation to overall net wealth. The absolute elasticity of prices for relatively low value goods is higher for individuals in the lower income bracket. A simple example of this would be how a middle-class individual considers eating at a restaurant an occasional luxury whereas for someone wealthier it is more common. There exists an upper bound impact of price increases, after which the consideration of price as an influence on consumption behaviour automatically goes close to 0. This would primarily occur for Veblen goods12 where individuals wealthy enough to purchase it would not factor in price but rather alternative influencers such as celebrity promotion etc. Marijuana, being primarily an illegal substance with high standard prices, would possibly qualify for this wealth effect. Literature suggests that young adults from wealthy backgrounds tend to purchase marijuana with greater frequency, possibly due to the status associated with possession. Given their background, price is unlikely to significantly affect their purchasing behaviour. Furthermore, the illegality of the drug would push up its price, allowing mostly those wealthy young adults to purchase them at a regular frequency.

3. Data and Summary Statistics

3.1 Dataset

Data is obtained on individual consumption patterns and pricing response to illicit

substances from the National Survey on Drug Use and Health (NSDUH).13 The initial sample

contains all individuals sampled for the period 2002-2014. The analysis is restricted to

individuals at university, and within the age of 18-29 years to reduce anomaly effects. The

final sample consists of 6,308 observations over the 12-year period.

First launched in 1999, the NSDUH (formerly the National Household Survey on Drug Abuse)

primarily measures prevalence and correlates of drug use within the United States. Using

12 A luxury good whose price does not follow the usual laws of demand and supply 13 Courtesy of the Substance Abuse and Mental Health Services Administration (SAMHSA).

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the NSDUH, this study extracts a rich set of socio-demographic and consumption

characteristics at individual level. Notably this includes the price last paid for marijuana, the

main variable of interest.14 Responses are categorised into pricing brackets depending on

amount paid.15 Restrictions on personal data made it difficult to obtain state specific

geographic information that would have provided an interesting extension to the analysis.

Future studies could test pricing effects on States that have legalised marijuana.

3.2 Variables

3.2.1 Consumption likelihood measures

Our primary measures for consumption of alcohol or marijuana are MJOYR2 and ALCYR,

binary variables recording the likelihood of consuming marijuana or alcohol in the past year

respectively. Self reported values have a tendency to be either under or over-reported

(Fisher and Katz, 2000). However, the nature of the data accounts for this by providing

anonymity and financial motivation to individuals sampled to encourage truthful responses.

The model used is an analogous variation to the model outlined by Williams et al.

(2001), with the binary variables simplifying the existing model in literature. The

likelihood of consumption of the substance in the previous year was used as a proxy

for utility derived by consumption. An implicit assumption of this study is that consuming

marijuana or alcohol gives positive utility otherwise individuals would choose not to

consume it. Refer to Appendix16 for summary statistics of consumption likelihood measures.

The average likelihood an individual has consumed marijuana and alcohol in the past year

was 44.2% and 98.6%, respectively, over the whole sampling period. On average this means

that an individual selected at random is nearly always likely to have consumed alcohol in the

past year compared to marijuana, which has roughly 50-50 chances.

3.2.2 Pricing measure

The key independent variable in this study is marijuana prices. Prior studies have all

attempted to establish the economic relationship via proxy methods, either through indirect

pricing effects by taxation (Pacula, 1998) or via legal implications on consumption (DiNardo

and Lemieux, 1992). However due to the nature of the dataset, we're able to obtain

amounts last paid for marijuana. This provides a more robust pricing outlook and lets us

examine the direct effect of pricing on the economic relationship between the two. Panel B

of Table 1 presents summary statistics of the pricing measure. The average price paid last for

marijuana is between $11 to $20.99.

14 Derived from the question "How much did you pay for the marijuana you bought this last time?". 15 See Table 1 in Appendix for detailed variable descriptions. 16 Panel A of Table 2

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3.2.3 Control Variables

To provide a more robust analysis, key control variables are included that fall in line with

recent consumer preference literature (Caulkins & Pacula, 2006). These are broken up into

individual characteristics (represented by age and sex), locality (represented by university

year and county type) and purchasing capability (represented by whether the individual is of

legal drinking age or not). Panel C of Table 1 provides summary statistics for the control

variables used in this study. For instance, 62.2% of the sample was of legal drinking age

during the time of survey. The variables are designed to test variation in the pricing

relationship.

3.2.4 Correlations among variables

As a fundamental check for multi-collinearity, Table 3 in the appendix reports the

correlations amongst all the independent variables in the analysis. A general rule of thumb,

correlation values over 0.8 in absolute terms suggest possible multi-collinearity within

variables (Gujarati, 1995). Table 2 shows that the highest correlation coefficient of 0.84

between AGE2 and legal. Since legal is a proxy for an age-induced shock to an individual's

capability to purchase substances, this relationship is expected to occur and unlikely to

affect the overall analysis to the variables of interest.

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4. Research Methodology

4.1 Main model and estimation method

The following is the main model regression for how marijuana prices affect the likelihood of

consuming marijuana:

𝑝𝑟𝑜𝑏(𝑀𝐽𝑂𝑌𝑅2 = 1) = 𝛽0 + 𝛽1𝐴𝐺𝐸2𝑖 + 𝛽2𝐼𝑅𝑆𝐸𝑋𝑖 + 𝛽3𝐼𝑅𝐸𝐷𝑈𝐶2𝑖 + 𝛽4𝐶𝑂𝑈𝑁𝑇𝑌𝑃2𝑖 +

𝛽5𝑀𝐽𝐶𝐴𝑇𝐸𝐺𝑖 + 𝛽6𝑙𝑒𝑔𝑎𝑙𝑖 + 𝜀𝑖

The following is the main model regression for how marijuana prices affect the likelihood of

consuming alcohol:

𝑝𝑟𝑜𝑏(𝑀𝐽𝑂𝑌𝑅2 = 1) = 𝛽0 + 𝛽1𝐴𝐺𝐸2𝑖 + 𝛽2𝐼𝑅𝑆𝐸𝑋𝑖 + 𝛽3𝐼𝑅𝐸𝐷𝑈𝐶2𝑖 + 𝛽4𝐶𝑂𝑈𝑁𝑇𝑌𝑃2𝑖 +𝛽5𝑀𝐽𝐶𝐴𝑇𝐸𝐺𝑖 + 𝛽6𝑙𝑒𝑔𝑎𝑙𝑖 + 𝜀𝑖 MJCATEG is a categorical variable showing last price paid for marijuana. The price paid on average tends to be normally distributed with a slight skew to the right. AGE2, IRSEX, IREDUC2, COUTYP2 and legal are five sets of control variables related to age, gender, educational year (either freshman, sophomore or senior), county type and legal characteristics of the individual respectively. There are two estimation methods commonly used in addiction and pricing literature. One is a Probit model, controlling for environment effects and the other is a base latent variable regression adapted from the model developed by Williams et al (2001). Due to the nature of the data, individual level observations over time are unavailable and would restrict sampling size to the period at which the individual was at university. Therefore, the Probit regression on pooled cross sectional data is favoured as the main regression method. The linear probability model, analogous to the Probit model, is avoided due to inconsistency and bias in generated estimates as they are not bound to the unit interval (Horrace and Oaxaca, 2006). The reported standard errors are adjusted for potential heteroscedasticity.

4.2 Endogeneity There is concern between endogeneity of marijuana prices and the desire to consume either alcohol or marijuana, since marijuana prices can affect the decision to consume the two goods but also affect an individual's purchasing power (Galea et al., 2007). An alternative model specification uses instrument-variables ("IVs") to estimate the main regression model via the two-stage least squares ("2SLS") method. The IVs used need to meet the model exogeneity and instrument relevance conditions. Considering the price paid for marijuana can be affected by the price of loose marijuana (MMLSPCAT) and the quantity purchased in grams (MMLSGMS), the two alternative variables are utilised as IVs for MJCATEG.

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5. Results

5.1 Are marijuana and alcohol economic complements or substitutes? Initial regressions look at the pricing effect of marijuana on the desire to consume either marijuana or alcohol17. Individual Probit regressions are used with results outlined in columns (1) and (4). Results modelling through instrument-variable estimation and for a Bivariate Probit analysis are outlined in columns (2) and (5), and (3) and (6) respectively.18 Constant sample size is used and robust standard errors are used throughout. The pricing effects are statistically significant and suggest an inverse relationship as expected (Pacula et al., 2014). For instance, a rise in price bracket from <$5 (default category in the analysis) to $5 - $10 reduces desire to consume marijuana by 20% and increases it for alcohol by 62%. This fits in with standard utility maximisation theory outlined in section 2.2. At gross level, marijuana and alcohol are observed to be economic substitutes. Specific to alcohol consumption, there is a maximum pricing effect before a reduction in impact of increased marijuana price, on the desire to consume alcohol (Glaeser et al., 2008). Lower priced marijuana categories showed a more significant effect in contrast to higher prices, which declined in impact as further control variables, were introduced (Tsuang et al., 2001). Concerning the IVs estimation method, the instruments are tested to meet the exogeneity and relevance conditions. Hansen's J instrument test19 is employed to examine if the IVs mention in section 4.2 meet the exogeneity requirement. The null hypothesis is that the IV's are not correlated with the error terms in each model. The regressions measured by ALCYR and MJOYR2 both produce insignificant J statistics, measured at the 10% significance level, failing to reject the null hypothesis. The relevance of each instrument is then examined, treating MJCATEG as a continuous variable and undertaking a standard linear regression. The F-test statistic is significant at the 1% level indicating that the instruments are jointly significant. However, MMLSGMS individual F-test statistic is insignificant, indicating a potentially weak instrument problem. The results based on IVs estimation indicate that marijuana prices continue to have a negative impact on desire to consume marijuana and a positive impact on desire to consume alcohol. Although, it is important to interpret the magnitude and significance level of MJCATEG's coefficient with caution since weak IVs may cause estimators to perform poorly. A bivariate Probit analysis is run to test whether the decision to consume alcohol or marijuana could potentially be related through the error term. The correlation coefficient between the bivariate outcomes is -0.28 and is significant at the 1% significance level. The decisions are therefore interdependent and should be estimated via bivariate analysis rather than independently. However, the coefficient effects are nearly identical and for simple analysis, independent Probit models would not alter the validity of the model. The robustness of this is further discussed in section 6. Noteworthy observations are that gender effects are insignificant although women are more likely to consume marijuana than their male counterparts (Booth & Nolen, 2012).20 Also

17 Outlined in Table 3 18 See table 4 in the Appendix for Instrument Relevance test 19 Also know as the over-identification test 20 Women are considered to be more experimental during university as compared to men.

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individual’s legal status is highly important in determining whether they consume marijuana or alcohol (Yörük & Yörük, 2011). This is further analysed in section 5.3.

5.2 Do age and university year affect desire to consume substances? To enrich the analysis, preferences changing across age and university year are analysed. Marginal effects for each standard model across AGE2 and IREDUC2 are plotted and can be seen in Figures 2 and 3. The graphs are created by considering age and university year as categorical variables and plotting marginal effects for each. General trends of Figure 1(a) and 1(b) suggests that there is an inverse and direct cohort effect induced by university year on desire to consume marijuana and alcohol respectively. Purchasing preferences may be subject to social norms invoked by the individuals you interact with on a regular basis (Perkins, 2002). Within university, this may be peer-led decisions, usually drawn from what is deemed socially acceptable at present time. This effect is amplified by the inherent social culture of drinking promoted through US fraternities and sororities (Wechsler et al., 2009). These social organisations usually offer selective post-degree networks that are often highly prized and sought after (Marmaros and Sacerdote, 2002). Due to this, college individuals may inherently self-select alcohol over marijuana as a required trait of these clubs (Phua, 2011). Figures 2(a) and 2(b) suggest that there is a direct and inverse age induced effect on desire to consume marijuana and alcohol respectively. The initial spike in the desire to consume alcohol once an individual turns 21 is further analysed in section 5.3. Older students on general are more likely to have consumed alcohol although their consumption trends move towards greater moderation with age (Engs and Hanson, 1986).

5.3 Are university students of legal drinking age more likely to consume alcohol? Perception of legal implications clearly plays an important role when deciding which substance to consume (Williams et al, 2001). The standard Probit model is broken up by individuals who are not of legal drinking age and by those who are.21 Figure 3(a) and 3(b) suggest that there is a straightforward substitution effect for those not of legal drinking age. Increased marijuana price decreases desire to consume marijuana and increases desire to consume alcohol. This effect is amplified for marijuana, for those who are not of legal drinking age. Figure 4(a) suggests that this effect is retained for marijuana for those of LDA. Theoretically, once an individual is given the freedom to undertake an action, there is a spike in desire to do so followed by a slow decline (Fromme et al., 2010). The action in this case is the ability to purchase either alcohol or marijuana. Being of LDA can have a significant impact on consumers switching preference away from marijuana to alcohol. Prior to being of LDA, purchasing alcohol and marijuana had the same legal implications if the individual were to get caught. However, the results suggest that alcohol is now the preferred choice. There is a legal bias towards the good where the individual's ability to purchase the substance legally, fuels his choice (Fromme et al., 2010). Figure 4(b) suggest that for those of legal drinking age, there seems to be a positive relationship between alcohol and marijuana initially, till a maximum is reached, after which a decline in

21 Statutory law in the US indicates that the general legal drinking age is 21.

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likelihood of consumption (complements). A possible “Wealth Effect” is identified. Pricing effects are dampened for wealthy individuals. (Glaeser et al, 2008). An alternative outlook is legal bias. Those who are of legal drinking age are more likely to consume alcohol and less

likely to be concerned with marijuana price (Yörük & Yörük, 2011). However the effect of

legal bias is only temporary. Once the individual gets accustomed to their newfound freedom, the desire to consistently purchase alcohol over marijuana solely due to legal ability dies out. Legal comfort is experienced and the individual returns back to his original consumption preferences.

Table 3

Pricing Effects of Marijuana on desire to consume Marijuana or Alcohol

(MJOYR2) Desire to Consume Marijuana (ALCYR) Desire to Consume Alcohol

Standard Model (1)

IVs Model (2)

Bivariate Model (3)

Standard Model (4)

IVs Model (5)

Bivariate Model (6)

MJCATEG

$5 - $10.99

$11 - $20.99

$21 - $50.99

$51 - $100.99

>$101

-0.20*

(0.12) -0.57***

(0.11) -0.70***

(0.11) -0.82***

(0.12) -0.96***

(0.12)

-0.17***

(0.02)

-0.20*

(0.12) -0.57***

(0.11) -0.70***

(0.11) -0.82***

(0.12) -0.96***

(0.12)

0.62***

(0.19) 0.81***

(0.19) 0.76***

(0.18) 0.64***

(0.20) 0.62***

(0.21)

0.02

(0.04) 0.63***

(0.19) 0.81***

(0.19) 0.75***

(0.18) 0.63***

(0.20) 0.63***

(0.21)

AGE2 0.11***

(0.02) 0.12***

(0.02) 0.11***

(0.02) -0.19***

(0.06) -0.19***

(0.06) -0.20***

(0.06)

IRSEX 0.03

(0.03) 0.04

(0.03) 0.03

(0.02) -0.09

(0.09) -0.08

(0.09) -0.10

(0.09)

IREDUC2 -0.08***

(0.03) -0.9***

(0.03) -0.08***

(0.03) 0.32***

(0.07) 0.34***

(0.07) 0.33***

(0.07)

COUTYP2 -0.07***

(0.02) -0.07***

(0.02) -0.07***

(0.02) 0.10

(0.07) 0.10

(0.07) 0.10

(0.07)

legal -0.17***

(0.06) -0.17***

(0.06) -0.17***

(0.06) 0.48***

(0.17) 0.47***

(0.17) 0.48***

(0.17)

Rho -0.03 (0.02)

-0.28***

(0.05) 0.07

(0.05) -0.28***

(0.05)

Observations 6308 6308 6308 6308 6308 6308

Pseudo R2 0.03 0.06

The left panel reports the pricing effects of marijuana on the desire to consume marijuana. The right panel reports the pricing effects of marijuana on the desire to consume alcohol.

*p<0.1; ** p<0.05; *** p<0.01

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Figure 1(a) University year impact on desire to consume marijuana Figure 2(a) Age impact on desire to consume marijuana

Figure 1(b) University year impact on desire to consume alcohol Figure 2(b) Age impact on desire to consume alcohol

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Figure 3(a) Pricing effects on marijuana consumption for those not of LDA Figure 3(b) Pricing effects on alcohol consumption for those not of LDA

Figure 4(a) Pricing effects on marijuana consumption for those of LDA Figure 4(b) Pricing effects on alcohol consumption for those of LDA

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6. Robustness Checks This section covers checking the model's validity in contrast to using a naive estimator to simply gauge consumption preferences. The 'Hit-and-run' results for this can be see in Figure 5 (a) and 5(b) in the Appendix. 98.6% all individuals at university in the study have consumed alcohol at some point during the past year. This is not surprising (Johnston et al., 2008) and would mean that, in general, regardless of how well specified your model is, simply assuming all individuals consume alcohol would be the best way to predict consumption patterns. However, since the empirical analysis is targeted at pricing effects on standard consumption, the model described still has valid implications. What's interesting to note is that in comparison to the naive estimator of marijuana consumption22, the model acts as a better predictor for individual behaviour. Robust standard errors are used to account for heteroscedasticity. A bivariate Probit analysis is undertaken to test whether the decision to consume marijuana and alcohol can be related through the error term (Greene, 1984). This can be seen in table 1. The results do suggest this and under normal scenarios it would make sense to use a bivariate analysis as the main model. However, as mentioned earlier, the purpose of this study is not to create the best-fit model but to explicitly look at how pricing affects purchasing decisions (Yamada et al., 1993). As a simple direct tool, independent Probit equations can be used to undertake the analysis (Bray et al., 2000).

7. Discussion and Concluding Remarks

The relationship between alcohol and marijuana is interesting. Empirical results suggest that the goods could be, both, economic complements or substitutes under specific scenarios. The legal drinking age is an important factor that affects the pricing effect of marijuana on alcohol. Generally, an individual who is of legal drinking age is more likely to consume alcohol rather than marijuana. Social factors such as university year, age and sex provide a more robust outlook at the economic relationship identified.

Standard policy implications stand towards stricter enforcement on alcohol sale around university areas. Empirically a large proportion of students were observed to be engaging in underage drinking. Building upon what Pacula et al. (2014) suggested, it would be beneficial to look towards legalising marijuana and then heavily taxing consumption to deter substance abuse. Limitations to the analysis include the lack of personal data, which distorts the model's accuracy. Reverse causality is another concerning issue. It is still unclear as to whether individual motivation to consume substances affects price or vice versa. Due to the nature of the dataset this paper is unable to account for changing consumption patterns over time.23 Extensions to this paper would include testing individual specific motivation over time in tandem with additional demographic variables such as family wealth. It would also be

22 Assuming all individuals have not consumed marijuana. 23 This could be ignored considering that alcohol consumption at university has remained at nearly 100% over the years.

16

worthwhile to check how consumption patterns differ over states where purchasing marijuana is legal. The paper finds significant evidence supporting the hypothesis that marijuana and alcohol are at gross, substitutes. However legal implications clearly play an important role an induce variation into this economic relationship. Future tax policy changes should allow for flexibility, adapting to suit the situational economic relationship at hand.

17

8. Appendix

8.1 Appendix I: Tables Table 1: Description of the Variables Used

Variable Description

Panel A: Consumption Likelihood Measures

MJOYR2 Binary Variable; equals to 1 if marijuana was consumed in the last 12 months and 0 otherwise

ALCYR Binary Variable; equals to 1 if alcohol was consumed in the last 12 months and 0 otherwise

Panel B: Pricing Measure

MJCATEG The average categorical price paid for last using

marijuana. This is broken up into 6 categories of

under $5, $5 - $10.99, $11 - $20.99, $21 - $50.99,

$51 - $100.99, >=$101 with numerical assignment

of 1, 2, 3, 4, 5 and 6 respectively. Under $5 is the

assumed default price for our analysis.

Panel C: Control Variables

AGE2 Categorical variable for individuals aged between

19 -29. This is broken down into 6 categories of

19, 20, 21, 22 or 23, 24 or 25 and 26 to 29 with

numerical assignment of 8, 9, 10, 11, 12, 13

respectively.

IRSEX Gender Variable; assumes a value of 1 for male

and 2 for female.

IREDUC2 Describes individual current education year at

university. Broken up into freshmen, sophomores

and seniors with numerical assignment as 9, 10

and 11 respectively

COUTYP2 Geographic indicator. Individual can be in either a

large metropolitan area, small metropolitan area,

or a non metropolitan area with numerical

assignment 1, 2 and 3 respectively.

legal Dummy Variable; equals to 1 if individual is of

legal drinking age and 0 otherwise

18

Table 2: Summary Statistics

Variable Obs Mean Std. Dev. Min Max

Panel A: Consumption Likelihood Measures

MJOYR2 6308 44.2% .5 0 1

ALCYR 6308 98.6% .12 0 1

Panel B: Pricing Measure

MJCATEG 6308 3.72 1.2 1 6

Panel C: Control Variables

AGE2 6308 10.1 1.43 8 13

IRSEX 6308 1.40 0.49 1 2

IREDUC2 6308 9.83 0.67 9 11

COUTYP2 6308 1.66 0.71 1 3

legal 6308 62.2% 0.49 0 1

Table 3: Correlation Matrix

1 2 3 4 5 6 7 8

1 MJOYR2 1.00

2 ALCYR -0.07 1.00

3 MJCATEG -0.17 0.03 1.00

4 AGE2 0.03 0.01 0.15 1.00

5 IRSEX 0.04 -0.01 -0.14 -0.01 1.00

6 IREDUC2 -0.02 0.06 0.12 0.52 0.00 1.00

7 COUTYP2 -0.05 0.02 0.07 -0.02 -0.04 -0.02 1.00

8 legal 0.01 0.03 0.11 0.84 -0.01 0.49 -0.013 1.00

This table presents the correlation matrix for all the independent variables employed in this study.

19

Table 4: Instrument Relevance Testing

MJCATEG (Categorical Pricing Variable) Standard Model

MMLSGMS -0.0001

(0.00)

MMLSPCAT 0.041***

(0.00)

Observations 6308

R2 0.8328

*p<0.1; ** p<0.05; *** p<0.01

Table 5: Age effects on desire to consume alcohol or marijuana


Standard Model Standard Model

MJCATEG

$5 - $10.99

$11 - $20.99

$21 - $50.99

$51 - $100.99

>$101

-0.19

(0.12) -0.56***

(0.11) -0.69***

(0.11) -0.82***

(0.12) -0.96***

(0.12)

0.62***

(0.19) 0.81***

(0.19) 0.76***

(0.18) 0.64***

(0.20) 0.61***

(0.21)

AGE2

20

21

22/23

24/25

26-29

0.01

(0.05) 0.01

(0.06) 0.07

(0.06) 0.20***

(0.07) 0.47***

(0.09)

-0.10

(0.13) 0.14

(0.16) 0.02

(0.16) -0.17

(0.17) -0.49***

(0.18)

IRSEX 0.03

(0.03) -0.09

(0.09)

IREDUC2 -0.06**

(0.03) 0.31***

(0.08)

COUTYP2 -0.07***

(0.02) 0.10

(0.07)

Observations 6308 6308

Pseudo R2 0.03 0.06

*p<0.1; ** p<0.05; *** p<0.01

20

Table 6: Testing Age Effect at Margins


At Margins At Margins

AGE2

19

20

21

22/23

24/25

26-29

0.41***

(0.02) 0.42***

(0.02) 0.42***

(0.02) 0.44***

(0.01) 0.49***

(0.02) 0.60***

(0.03)

0.99***

(0.00) 0.99***

(0.00) 0.99***

(0.00) 0.99***

(0.00) 0.98***

(0.00) 0.97***

(0.01)

*p<0.1; ** p<0.05; *** p<0.01

Table 7: University Year Effect on desire to consume alcohol or marijuana



MJCATEG

$5 - $10.99

$11 - $20.99

$21 - $50.99

$51 - $100.99

>$101

-0.20*

(0.12) -0.57***

(0.11) -0.70***

(0.11) -0.82***

(0.12) -0.96***

(0.12)

0.62**

(0.19) 0.81***

(0.19) 0.76***

(0.18) 0.64***

(0.20) 0.62***

(0.21)

AGE2 0.11***

(0.02) -0.19***

(0.06)

IRSEX 0.03

(0.03) -0.09

(0.09)

IREDUC2

Sophomore

Senior

-0.13***

(0.04) -0.13**

(0.06)

0.31***

(0.09) 0.67***

(0.18)

COUTYP2 -0.07***

(0.02) 0.10

(0.07)

legal -0.15** 0.49***

21

(0.06) (0.17)


Pseudo R2 0.03 0.06

*p<0.1; ** p<0.05; *** p<0.01

Table 8: Testing University Year Effect at Margins



IREDUC2

Freshman

Sophomore

Senior

0.47***

(0.01) 0.42***

(0.01) 0.42***

(0.02)

0.98***

(0.00) 0.99***

(0.00) 1.00***

(0.00)

*p<0.1; ** p<0.05; *** p<0.01

Table 9: Price Effect on desire to consume alcohol or marijuana for those not of LDA



MJCATEG

$5 - $10.99

$11 - $20.99

$21 - $50.99

$51 - $100.99

>$101

-0.29

(0.18) -0.72***

(0.18) -0.89***

(0.18) -1.10***

(0.19) -1.1***

(0.20)

0.68***

(0.24) 1.00***

(0.25) 0.89***

(0.25) 1.20***

(0.34) 1.30***

(0.42)

AGE2 0.04

(0.06) -0.17

(0.16)

IRSEX 0.01

(0.05) -0.10

(0.13)

IREDUC2 -0.10*

(0.06) 0.43**

(0.18)

COUTYP2 -0.11***

(0.04) 0.26**

(0.12)


Pseudo R2 0.04 0.09

*p<0.1; ** p<0.05; *** p<0.01

22

Table 10: Testing Pricing Effect for those not of LDA at Margins



MJCATEG

<$5

$5 - $10.99

$11 - $20.99

$21 - $50.99

$51 - $100.99

>$101

0.72***

(0.06) 0.62***

(0.02) 0.45***

(0.02) 0.39***

(0.02) 0.30***

(0.03) 0.29***

(0.03)

0.90***

(0.04) 0.98***

(0.01) 0.99***

(0.00) 0.99***

(0.00) 0.99***

(0.00) 1.00***

(0.01)

*p<0.1; ** p<0.05; *** p<0.01

Table 11: Price Effect on desire to consume alcohol or marijuana for those of LDA



MJCATEG

$5 - $10.99

$11 - $20.99

$21 - $50.99

$51 - $100.99

>$101

-0.13

(0.16 ) -0.46***

(0.15 ) -0.57***

(0.15 ) -0.65***

(0.15 ) -0.83***

(0.16 )

0.68** (0.34) 0.59**

(0.30) 0.57** (0.29) 0.27

(0.30) 0.24

(0.31)

AGE2 0.13***

(0.02) -0.19***

(0.06)

IRSEX 0.04

(0.04) -0.09

(0.12)

IREDUC2 -0.06*

(0.03) 0.28***

(0.08)

COUTYP2 -0.04

(0.03) -0.01

(0.09)


Pseudo R2 0.02 0.06

*p<0.1; ** p<0.05; *** p<0.01

23

Table 12: Testing Pricing Effect for those of LDA at Margins



MJCATEG

<$5

$5 - $10.99

$11 - $20.99

$21 - $50.99

$51 - $100.99

>$101

0.65***

(0.05) 0.59***

(0.02) 0.47***

(0.02) 0.42***

(0.01) 0.39***

(0.02) 0.32***

(0.02)

0.97***

(0.02) 0.99***

(0.00) 0.99***

(0.00) 0.99***

(0.00) 0.98***

(0.00) 0.98***

(0.01)

*p<0.1; ** p<0.05; *** p<0.01

24

8.2 Appendix II: Figures Figure 5(a): Testing model validity - Probit Model Table 5(b): Testing model validity - Naive Estimator

Marijuana Use Frequency Percent

No 3523 55.85

Yes 2785 44.15

Total 6308 100

Alcohol Use Frequency Percent

No 88 1.4

Yes 6220 98.60

Total 6308 100

25

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