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Medical Marijuana Laws and Illegal Marijuana Use Yu-Wei Chu * Michigan State University 110 Marshall Adams Hall East Lansing, MI 48823. [email protected] September 30, 2012 Abstract Seventeen states and the District of Columbia have passed laws that allow individuals to use marijuana for medical purposes. In this paper, I use marijuana possession arrests and treatment referrals by medical professionals to estimate the impact of medical marijuana laws on marijuana usage among non-patients. I find that these laws increase marijuana arrests among adult males by about 20%. The effect is strongest among young adults and decreases with age. I also find that marijuana treatment referrals increase by more than 10% after the passage of medical marijuana laws. In contrast to previous studies, my analysis also shows some evidence that these laws affect juveniles’ marijuana use. JEL Classification: I10 I18 H75 K42 Keywords: medical marijuana laws, marijuana use * The author is grateful to Jeff Biddle, Todd Elder, and Gary Solon for their guidance and suggestions. Thanks also go to Michael Conlin, Steven Haider, Sheila Royo Maxwell, Stacey Lynn Miller, and participants at the Empirical Micro Lunch Seminar at Michigan State University for helpful discussions and comments.

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Medical Marijuana Laws and Illegal Marijuana Use

Yu-Wei Chu* Michigan State University 110 Marshall Adams Hall East Lansing, MI 48823.

[email protected]

September 30, 2012

Abstract

Seventeen states and the District of Columbia have passed laws that allow individuals to use

marijuana for medical purposes. In this paper, I use marijuana possession arrests and treatment

referrals by medical professionals to estimate the impact of medical marijuana laws on marijuana

usage among non-patients. I find that these laws increase marijuana arrests among adult males by

about 20%. The effect is strongest among young adults and decreases with age. I also find that

marijuana treatment referrals increase by more than 10% after the passage of medical marijuana

laws. In contrast to previous studies, my analysis also shows some evidence that these laws

affect juveniles’ marijuana use.

JEL Classification: I10 I18 H75 K42

Keywords: medical marijuana laws, marijuana use

* The author is grateful to Jeff Biddle, Todd Elder, and Gary Solon for their guidance and suggestions. Thanks also go to Michael Conlin, Steven Haider, Sheila Royo Maxwell, Stacey Lynn Miller, and participants at the Empirical Micro Lunch Seminar at Michigan State University for helpful discussions and comments.

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"By characterizing the use of illegal drugs as quasi-legal, state-sanctioned, Saturday afternoon

fun, legalizers destabilize the societal norm that drug use is dangerous . . . Children entering

drug abuse treatment routinely report that they heard that 'pot is medicine' and, therefore,

believed it to be good for them.” Andrea Barthwell, M.D., Former Deputy Director of the White

House Office of National Drug Control Policy, in an editorial in The Chicago Tribune, Feb. 17,

2004

1. Introduction

Medical marijuana legislation represents a major change in U.S. policy towards

marijuana in recent years. Advocacy groups such as NORML consider such legislation the first

step towards full legalization. As of July 2012, seventeen states and the District of Columbia had

passed laws that allowed individuals with designated symptoms to use marijuana for medical

purposes (ProCon.Org, 2012a). Six more states have legislation pending, while twelve other

states introduced legislation that failed to pass. Although the Obama administration takes a

relatively liberal attitude towards medical marijuana, federal agencies such as the Drug

Enforcement Administration (DEA) and the Office of National Drug Control Policy (ONDCP)

remain firmly opposed, and marijuana is still listed as a Schedule I drug with no accepted

medical value. Because the number of legal patients was relatively small at least before 2009, the

direct impact of medical legalization should be limited. The main reason that the DEA and

ONDCP oppose such laws is based on the notion that they would increase marijuana use among

non-patients (Drug Enforcement Administration, 2011).

There is a strong correlation between medical marijuana legislation, the perceived risk of

marijuana and marijuana use. According to the 2008 National Survey on Drug Use and Health

(NSDUH), among the states with the highest rate of marijuana use and the lowest perceived risk,

ten out of fifteen have adopted medical marijuana legislation.1 Despite the strong correlation,

only a small number of studies have assessed the causal link between medical marijuana laws

and usage, with mixed results. Importantly, most of the studies only cover a short time period,

leading to imprecise estimates based on a small number of state-level law changes. Existing

1 The fifteen states with the highest use rate are Alaska, California, Colorado, Delaware, District of Columbia, Maine, Massachusetts, Michigan, Montana, New Hampshire, New York, Oregon, Rhode Island, Vermont and Washington; the states with the lowest perceived risk are the above states with California, Delaware and New York replaced by Minnesota, Virginia and Wisconsin.

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studies often focus on juveniles, while only adults are qualified patients under medical marijuana

laws, and the marijuana prevalence rate is actually higher among young adults than among

juveniles. In addition, these studies only examine the marijuana prevalence rate and ignore

potential changes at the intensive margin.

To examine whether medical marijuana laws increased illegal marijuana use among non-

patients, especially for adults, I use data on marijuana possession arrests in cities from the

Uniform Crime Reports (UCR) as a proxy for illegal use, for the years 1988 through 2008. As

shown in Table A1 in the Appendix, these data cover a period when 12 states legalized medical

marijuana. As arrest depends on not only the number of users but also their use or transaction

frequencies, it is a mix of measure for both the intensive and extensive margins. Another

advantage of arrest data is that it represents an objective measure. It does not suffer from the self-

reporting bias that is common in survey data (Golub et al., 2005; Harrison and Hughes, 1997).

Since medical marijuana laws are expected to change social acceptance and perception of

marijuana, changes in reporting behavior are of particular concern in the current context. For

example, Miller and Kuhns (2011) find that arrestees report use more honestly after the passage

of medical marijuana laws.

I estimate reduced-form models for the effect of medical marijuana laws on male arrests,

controlling for city and year fixed effects as well as city time trends. I find that these laws

increase illegal marijuana usage, particularly for young adults. For adult males, on average,

medical marijuana laws are associated with around a 20% increase in arrest rate, or an increase

of about 25 arrestees per 100,000 persons in cities. The effect is stronger among males aged 18-

29, while there is no obvious effect in age groups over 40. The estimates also show a 10%

increase in arrest rates for male juveniles, although the estimates are relatively imprecise in this

age group.

At first glance, a 20% increase might seem implausible large. Conceptually, arrest data

capture changes in both the intensive and extensive margins, and a significant part of the 20%

increase can be viewed as a change at the intensive margin. 2 Empirically, marijuana arrests are

2 We can model arrest as follows: 𝐴 = ∑ 𝑃(𝑋𝑖) ∗ 𝑁

𝑖=1 𝐹𝑖, where Fi is individual i's transaction or use frequencies, N is the number of marijuana users; P (X) is the probability of being arrested per transaction, a function of Xi, factors such as local law enforcement. Assume P(X) to be the same for everyone, then log(A) = log(P(X)) + log(𝐹�) + log(N), where 𝐹� is the average of Fi. So, arrest reflect effects on both the extensive and intensive margins. Empirically, P(X) may depend on Fi and possibly look like a step function, i.e., only users with Fi greater than some amount have a positive probability of being arrested.

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likely to be concentrated on heavy users, as the average probability of being arrested is low, and

a 20% increase in heavy users is not particularly large for policy changes regarding substance

use. For example, zero tolerance laws that lower the legally allowable BAC from 0.08 to 0.02 for

drivers reduce heavy drinking for targeted population by 13-18% (Carpenter, 2004).

Because heavy marijuana users are often associated with potential negative outcomes,

such as developing dependence and need for treatment, I use data on admissions to rehabilitation

facilities from the Treatment Episode Data Set (TEDS) to provide further evidence on the effects

of medical marijuana laws. The TEDS data allows me to obtain estimates that are not biased by

law enforcement from using treatments referred by professional medical providers. The results

are consistent with the findings from the arrest data: on average, medical marijuana laws increase

the professional referred treatments by around 10% for adult males. In contrast to previous

studies that do not find any effect of medical marijuana laws on juveniles, I find a positive effect

of 5-15% for juvenile treatments. I also use only first-time treatments to create a measure

representing individuals to exclude potential effects on recidivism. Somewhat surprisingly,

instead of a smaller effect, the estimates indicate a 20% increase in first-time treatments for adult

males.

This research addresses the heated policy debate on medical marijuana laws by

presenting evidence for an increase in illegal use among non-patients. By using data reflecting

effects on heavy users, this research is more relevant to the design of policy because heavy usage

is associated with negative health and social outcomes. In particular, the results from the

marijuana treatment data provide empirical support for such concerns and suggest a direct

medical cost from these laws.

2. Medical Marijuana Laws

In the late 1980s and the early 1990s, smokable marijuana was discovered to have a

positive effect on patients suffering from nausea, a common symptom among cancer patients and

the increasing number of AIDS patients (Pacula et al., 2002). With growing evidence of positive

medical effects and lobbying by marijuana legalization advocacy groups such as NORML, many

states have joined in passing a new wave of medical marijuana legislation since 1996. Table A1

in the Appendix provides an overview of each state’s medical marijuana laws (For legal

documents, see ProCon.Org, 2012a).

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These laws permit patients with legally designated diseases and syndromes to use

marijuana as a treatment. Patients can legally possess marijuana up to a fixed amount. In many

states, they can cultivate marijuana on their own. These laws also allow “caregivers” (most of

whom are patients as well) to grow and provide marijuana to patients on a not-for-profit basis. In

most states, it is mandatory to register as a qualified medical marijuana patient or caregiver and

to renew this registration every year.3 The designated symptoms and conditions typically include

AIDS, anorexia, arthritis, cachexia, cancer, chronic pain, glaucoma, migraine, persistent muscle

spasms, severe nausea, seizures, and sclerosis. Some laws, such as those in California and

Colorado, even allow use for “any other illness for which marijuana provides relief” (Cohen,

2010).

Because even designated syndromes such as chronic pain can be defined subjectively,

such legislation actually provides a way for all marijuana users to become legal patients.

However, before 2009, the number of legal patients remained relatively small except in

California.4 A very imprecise estimate from ProCon.org (2012b) indicates that, as of January

2009, the total number of legal patients was about 0.27 million people, or 0.19% of the

population in medical marijuana states. One reason might have been that becoming a registered

patient did not greatly increase the ease of acquiring marijuana because the number of

dispensaries was limited. Some marijuana dispensaries with grey legal status did exist under the

name of caregiver, but how prevalent they were depended on the attitude of the local government

(often at the city level) and the actions of law enforcement, which could change from time to

time. This was because the state medical marijuana laws did not directly allow marijuana

dispensaries in order to conform to federal regulations in which marijuana remained a Schedule I

drug. The legal environment has changed since 2009. The Obama administration has stated that

the federal government will no longer seek to arrest medical marijuana users and suppliers so

long as they conform to state laws.5 This statement largely resolved the legal dispute between

state and federal governments, and it motivated several states to initiate and pass medical 3 California created a registration program in 2004 but registration was voluntary. Colorado allows patients who do not join the registry to use the "affirmative defense of medical necessity" if they are arrested on marijuana charges. Maine passed an amendment in November 2009 that created a registration program and required mandatory registration starting January 1, 2011. Washington does not have a registration program. 4 There is no official number of patients for states without registration. However, based on the large number of dispensaries, it is believed that California has many more patients than other medical marijuana states. 5 The Obama administration's medical marijuana policy is swinging recently. In 2012, there have been cases of DEA raids on medical marijuana providers which arguably conform to state/local laws.

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marijuana laws. The number of registered patients and dispensaries has increased significantly

since 2009.

3. Previous Literature

There is little doubt that medical marijuana legalization increases marijuana usage at the

intensive margin, because some existing users will become legal patients and they will be able to

increase their consumption safely and easily. On the other hand, since the number of legal

patients is limited, the major policy debate is whether an indirect effect of laws increases use

among non-patients. It is a popular belief among public media that legalization has increased

illegal marijuana use, especially among juveniles (O'Connor, 2011). Illegal marijuana use could

increase, in terms of both the intensive and extensive margins, because of the greater ease of

obtaining marijuana, either from a legal patient or from a dispensary. More significantly,

however, these laws could send a “wrong message” to the public that increases illegal use. If

marijuana were defined by law as a beneficial medicine rather than a harmful drug, this would

increase social acceptance for recreational use. People may also expect more lenient law

enforcement and broader legalization in the future. Unlike changes in legal penalty of marijuana

possession that ordinary people may not be aware of, the process of referendum and the setup of

medical marijuana program make the public much more aware of the legislation (MacCoun et al.,

2009; MacCoun, 2010; Pacula et al., 2005). As Becker and Murphy’s seminal paper (1988)

shows, lowering the perceived health and legal risk will lower the price and increase use.

Empirically, Johnston et al. (2011) and Pacula et al. (2001) show that the perception of risk is a

good predictor of marijuana use among high school students. Empirically, there is a strong

correlation between medical marijuana legislation, perceived risk and marijuana use;

nevertheless, the causal link is not well supported by existing studies.

Based on Drug Abuse Warning Network data for the years 1994–2002, Gorman and

Huber (2007) do not find any significant change of marijuana use among arrestees using a time

series framework. Their data are limited to a small portion of arrestees with available urine test

samples from only four cities in California, Colorado and Oregon in a short time span. Moreover,

criminals are probably a demographic group that does not respond to the change of laws due to

the existing high use rate and low perceived risk. On the other hand, based on the same dataset,

Pacula et al. (2010) find that medical marijuana laws increase marijuana price (as reported by

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arrestees), which they interpret as an increase in demand for marijuana along with an upward

sloping supply curve.

Drawing on public-use data from the NSDUH for the years 2002 through 2008, Wall et al.

(2011) find that legalization was associated with a higher prevalence rate of marijuana use

among 12- through 17-year-olds, while Harper et al. (2012) show that these results are quite

sensitive to including state fixed effects. Using a number of datasets that cover a longer period, a

recent working paper from Anderson et al. (2012a) finds no evidence of an increase in marijuana

use among teenagers. In fact, the estimates for juveniles from Anderson et al. (2012a) and Harper

et al. (2012) are often negative. The estimate for young adults aged 18-25 from Harper et al.

(2012) are positive but insignificant in their extended sample through 2009.

There are many limitations on the public-use data of NSDUH. First of all, it is only

available since 2002, and there were only five states that changes their laws within the sample

period. The problem of a shorter time period is further aggravated by the fact that the data is

smoothed across years. The NSDUH provides the state level measures as two-year moving

averages estimated from a logistic model. For example, the measure in the 2008 report is a

predicted probability using both 2008 and 2007 data. The standard fixed-effects estimates may

not be very reliable because there is little much within-state variation in these data. The two-year

moving averages also make coding for the first-year of legalization arbitrary, which could

present problems in a short sample.

As mentioned above, a direct effect of medical marijuana laws is to increase marijuana

use at the intensive margin for legal patients. Potential indirect effects, such as lowering

perceived risk or increasing availability, also suggest an effect on the intensive margin as well.

However, existing studies estimate the effect of laws exclusively in terms of the extensive

margin. Ignoring the intensive margin could seriously underestimate the effect especially for

adults due to their low initiation rate (Gfroerer et al., 2002).6 For policy concerns, the intensive

margin is at least as important as the extensive margin. It is common for a policy to affect only

the intensive margin. For example, Carpenter (2004) finds that zero tolerance laws only decrease

heavy drinking while having no effect on participating in drinking.

6 The measure of initiation rate in Gfroerer et al. (2002) is first-time use, which should be viewed only as a lower bound for the change at the extensive margin especially for adults. A current user could have tried marijuana early in his life, but starts to use regularly since recently.

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4. Analysis of the Uniform Crime Reports

4.1. The data

The primary data for this study is arrest for marijuana possession from the FBI's Uniform

Crime Reports (UCR). 7 The UCR arrest data is an administrative series of monthly police

records from state and local police agencies across the U.S. It provides information on arrest

counts by age, gender and race in each crime category along with agency populations (estimated

from the Census). I use the yearly aggregated arrest data provided by the Inter-university

Consortium for Political and Social Research (ICPSR), as the FBI also reviews and checks the

data using annual arrest totals (Akiyama and Propheter, 2005). Previous studies like Conlin et al.

(2005) have used these data as measures for usage of illegal substances. The UCR arrest data has

a hierarchy rule which only records arrests according to the most serious offense. As a result,

arrestees classified under marijuana possession do not simultaneously commit other serious

crimes (such as cocaine possession or other violent or property crimes). Because a person may be

arrested many times, each arrest count does not necessarily represent a single individual.

My sample covers the period 1988 through 2008.8 I use data starting from 1988 to avoid

potential influences from decriminalization and the crack epidemic. Eleven states decriminalized

marijuana in the 1970s, though there are only minor differences across non-decriminalized and

decriminalized states in the late 1980s (Pacula et al., 2003; Pacula et al., 2010).9 There was also a

strong declining trend in marijuana use/arrest in the 1980s, which could be related to the crack

epidemic and President Reagan’s “war on drugs.”

I drop California and Colorado due to possible lower levels of enforcement which will

directly affect the likelihood of marijuana arrest. As Cohen (2010) points out, the loosely worded

medical marijuana legislation in those states have created a “regulatory vacuum.” In fact, the

7 There is also arrest for marijuana sale/manufacture, which is small and relatively constant across years. To be recorded as a sale arrest, the amount must exceed some minimum with intention to sell. Because marijuana transactions often involve small quantities, and sale intention is hard to prove, sale arrest is often due to large scale transactions. In fact, a proportion of marijuana possession arrestees are probably low level sellers. 8 2008 was the latest data available when I began this study. Although data through 2010 became available recently, looking at the period prior to 2009 has an advantage that the number of legal patients was relatively small, and the federal policy was fairly uniform prior to the Obama administration. In addition, strong economic recession may affect drug use. As of July 2012, most states that passed laws after 2008 have not yet accepted patient applications. (Only Arizona began to accept patient application since April 2011). 9 Decriminalization is better termed as depenalization, since marijuana possession is still legally a crime and it results in being arrested. Empirically, depenalization has either no or very small effect on marijuana use, and most citizens do not even know such legal change (MacCoun et al., 2009; MacCoun, 2010; Pacula et al., 2005).

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Colorado attorney general, John W. Suthers, has said, “In Colorado it’s not clear what state law

is.” (Johnson, 2009) It is even said, although perhaps with some exaggeration, that in Los

Angeles, San Francisco and Denver there are more marijuana dispensaries than Starbucks coffee

shops or CVS pharmacies (Coté et al., 2008; Osher, 2010). A large number of dispensaries could

reflect a lenient attitude on the part of law enforcement and local government, because most of

them are not strictly legal. For example, California law requires dispensaries to be non-profit and

to have a consistent care-giving relationship with their patients. This is not generally the case.

Moreover, both California and Colorado attempted to legalize marijuana; although these attempts

were unsuccessful, some cities like Denver did pass laws that legalized marijuana. Even such

laws are legally ineffective because they violate state laws, they can still influence local law

enforcement. In 2010 California became the second state to decriminalize marijuana possession,

making it a civil infraction rather than a crime, and the penalty for this infraction became one of

the lowest in the U.S.10

Since participation in the UCR program is generally voluntary, many agencies do not

report every month or every year; and even when an agency reports, it may not report data in all

categories. Empirically, most missing data is from agencies with small populations and those that

do not report for a whole year (Lynch and Jarvis, 2008). Since it is not possible to distinguish a

true zero from missing data in the UCR, I only use police agencies located in cities with

populations greater than 50,000, as the FBI also checks and communicates regularly with these

agencies to ensure data quality (Akiyama and Propheter, 2005). I restrict the sample to cities

because marijuana transactions are closely related to social networks and the probability of arrest

depends on population density. 11 Since population size is generally increasing over time, I

include earlier observations of the above cities to make the panel more balanced. (I exclude 215

city-year observations that have populations less than 25,000).

Similar to Carpenter (2007), and as is common in the criminology literature, I focus on

adult male arrest, and I use observations only if the agencies report arrests for marijuana

10 In September 2010 California enacted a new law that effectively decriminalizes marijuana possession (S.B. 1449) by making possession of 28.5 grams or less of marijuana only a civil infraction. Massachusetts passed a similar law making marijuana possession (less than 1 oz.) a civil offense in November 2008. Although marijuana possession (1 oz. or less) is still legally a crime (class 2 petty offense) in Colorado, it only results in maximum fine of $100 and no jail time. 11 For agencies in MSAs with more than 50,000, about 70% of populations live in cities, and about 70% of all MSA agencies are city agencies. On average, marijuana arrest rate in cities is about twice as large as in non-cities.

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possession for at least six months in that year.12 The final panel consists of 562 cities and 8722

city-year observations in which about 40% of the cities are observed in at least 20 years. The

sample covers 9 medical marijuana states that passed laws before July 2008, including Alaska,

Hawaii, Maine, Montana, Nevada, New Mexico, Oregon, Rhode Island, and Washington. In

addition to California and Colorado, Vermont is also not included in the sample due to sample

construction. (Michigan passed its law in November 2008 and is coded as a non-medical

marijuana state.)

Table 1 lists the means and standard deviations of marijuana possession arrest rates per

100,000 among each age group for states with and without medical marijuana. States with

medical marijuana laws have lower arrest rates in all age groups, likely due to a lower level of

law enforcement, but the distributions of arrests among age groups are similar. The arrest rate is

highest among those aged 18-24 and declines with age. In fact, the age distribution and the

trending over time of the UCR arrest data are consistent with underlying marijuana use from

survey data such as NSDUH and Monitoring the Future.

4.2. The Results

My primary empirical strategy involves estimating city- and year-specific marijuana

arrest rates as a function of whether the state has an effective medical marijuana law in place in

that year. I begin by estimating the following model by OLS:

(1) Yist = β Lawst + City fixed effectsi + Year fixed effectst + City linear time trendsit

+ City squared time trendsit + εist,

where Yist is the marijuana possession arrest rate among adult males or each male age group per

100,000 (or its log) for city i in state s and year t. Lawst is a dummy variable indicating whether a

12 I include 201 agency-year observations that report only in December since agencies may report annually; their mean and standard deviation are similar to observations that report for least six months. I drop 10 observations that have zero adult male marijuana arrest. I only consider males both to be consistent with the existing literature and because males are much more likely to be in the criminal justice system than are females. For example, the average arrest rate for adult males in my sample is seven times that for adult females.

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state s had a medical marijuana law during year t.13 In addition to city and year fixed effects, I

include city-specific time trends to capture the time-varying unobservables within a city such as

law enforcement. As a robustness check on functional form and a solution to zero-value

problems of logarithms, I also estimate a fixed-effect Poisson model with the same specifications.

In the main specification, I do not include any control variables because city-specific time trends

and fixed effects have already accounted for any smooth-trending variables.14 Throughout this

paper, the estimated standard errors are clustered at the state level and therefore are robust to

serial correlation, within-state spatial correlation, and heteroskedasticity.

Table 2 shows the estimates of β based on Equation (1). The dependent variable is the

adult male arrest rate in the upper panel and the logarithm of arrest rate in the middle panel; the

lower panel shows results from a fixed-effect Poisson model in which the dependent variable is

arrest rate. Columns (1) and (2) show the estimates from Equation (1), and they are positive and

highly significant in all specifications. For example, based on Column (2), medical marijuana

laws, on average, result in an annual increase of 26.94 adult male arrestees per 100,000 city

residents; if we interpret the log points as percentage change, this is about a 22.5% increase in

the adult male arrest rate. The estimates from the fixed-effect Poisson model are quantitatively

similar. The point estimate (the partial effect on the logarithm of the conditional mean), 0.198, is

very close to the estimate from the log specification (the partial effect on the conditional mean of

the log arrest rate). It also implies an average partial effect of an increase of about 31 arrestees

per 100,000 city residents (= 0.198×158.5, where 158.5 is the mean of adult arrest rate). One

possible concern is that city-specific time trends over-fit the data. In Columns (3) and (4), I use

state-specific time trends instead, and the results are nearly identical. The last two columns, (5)

and (6), show qualitatively similar results estimated from a specification with only a separate

group time trend for all medical marijuana states that passed laws before July 2008.

Figures 1 provides graphical evidence of the effect of medical marijuana laws on arrests.

The graph shows the average adult male marijuana arrest rate (in logarithms) before and after

13 For the first year, Lawst equals 1 if the law is effective before July 1st, and equals 0 otherwise. I code the law based on the effective date rather than the passing date (it only matters for Nevada).Note that there could be a huge time lag between the law being effective and the marijuana program starting to accept application. 14 Potential non-smooth control variables include legal change in marijuana penalty, unemployment rate, and 0.08% blood alcohol concentration (BAC) laws. As mentioned in Note 10, most studies do not support that change in penalty affect marijuana use. Unemployment rate and 0.08% BAC laws are included along with other control variables in Table 5 as robustness check.

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the passage of medical marijuana laws, where the X-axis measures the year relative to the state's

law change, with 0 denoting the year of passing the law, 1 denoting the following year, and so on.

To create a synthetic control group, I compute an average of the log arrest rates in non-medical

marijuana states for each year, and then take a weighted average of these yearly averages, in

which the weights come from the relative composition of years in the treatment group (medical

marijuana states). For instance, in “Year 0,” 58% of observations in the treatment group are from

Oregon and Washington, which passed the laws in 1998 (coded as 1999); 2% of observations are

from Maine, which passed the law in 1999 (coded as 2000); and so forth. So the weight put on

the year 1999 average arrest rate in the control group is 0.58; the weight put on the year 2000

average arrest rate is 0.02, and so on. In other words, in “Year 0,” 58% of the observations in the

control group are selected from year 1999, 2% are from 2000, and etc. The treatment group

shows a persistent jump in the arrest rate from “Year -1” to “Year 0.” The magnitude of the jump

in the log arrest rate is about 0.15, which is similar to the regression results above.

Table 3 reports the effects of medical marijuana laws for each age group, in which I

control for city quadratic time trends along with city and year fixed effects.15 The upper panel

includes the estimates from the level specification. Because there are many zero values in each

age group, in the lower panel the results are estimated from a fixed-effect Poisson model. The

relative magnitudes of these estimates are consistent with the age distribution in Table 1. They

are larger among younger age groups and decrease with age, and as expected, the estimates for

the oldest age groups (age 40-44 and age 45+) are small and insignificant. In the upper panel, the

sum of the estimates in all age groups under 40 is about 27, which is nearly identical to the

estimate in Table 2, and the majority of increase comes from people under age 30. In the lower

panel, young adults also exhibit a greater effect than older age groups. The average partial effects

from the fixed-effect Poisson model are quantitatively similar to the results in the upper panel.

For example, the estimate implies 10.2 (= 0.215×47.54) additional male arrestees aged 18-20.

Table A2 reports descriptive statistics and the estimates of β for juveniles aged 12-17 and

15-17. Because there are many zeros, I only use a linear model with level specification and a

fixed-effect Poisson model. For all juveniles aged 12-17, the results are positive but never

significant. For ages 15-17, the estimates are often significant for the level specification, but 15 In the lower panel, age 45+ is estimated only with linear time trends. Because there are too many zeros (nearly 20%), there is not enough variation after controlling for linear trends and fixed effects. The result is qualitatively similar if I control for state quadratic time trends.

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somewhat noisy for the Poisson model. Because the data quality on juveniles is not as good as

adults, the interpretation on these estimates should be cautious.16 Although not reported here, the

estimates also indicate a positive and significant effect of around 17% on adult females.

In Table 4, I investigate the dynamic responses of the adult male arrest rate to adoption of

medical marijuana laws. The left panel is a level specification and the right panel is a log

specification. In the first two columns in each panel, I replace Lawst by a set of dummy variables,

Years 0-1 through Years 8-9 (the maximum lag), that indicate each two-year interval after the

medical marijuana laws are enacted. Note that the estimates for later years are driven mostly by

Oregon and Washington. The estimated standard errors become larger when squared city time

trends are included, but the magnitudes stay similar. Although the estimated effect on marijuana

arrest seems to be increasing over time, a Wald test cannot reject the null hypothesis that the

estimates for Years 0-1 through Years 6-7 are identical. (It is able to reject the null hypothesis

when the estimate for Year 8-9 is included.) Therefore, the restriction of a constant effect on

Lawst should be reasonable. The latter two columns in each panel include an additional dummy,

Years (neg.1-2), that indicates the two-year interval before the passage of the laws. The estimates

for this dummy are small and insignificantly different from zero, while the estimates remain

similar for post-law dummies, which indicates that policy endogeneity is not a serious concern in

this context. The results are similar if I include another dummy that indicates years three and

four before the passage of laws (not reported).

In Table 5, I check the robustness of the main results using different specifications and

samples. In Column (1), as in Carpenter (2007), I scale arrest counts by a factor that equals the

fraction reported of a year (12 divided by the number of months reported) using agencies that

report at least six months (agencies that only report in December are excluded). In Column (2), I

include city agencies that report any number of months without scaling. In Column (3) and (4), I

include city police officer rate (from the UCR) and other state-level controls such as black male

rate, unemployment rate, per capita local and state expenditures on police protection, per capita

local and state expenditures on health and hospital expenditures, and 0.08 BAC laws. The results

16 The data on juvenile crime and custody rates are much less complete than the associated data for adults (Carpenter, 2007; Levitt, 1998). Also, the juvenile justice system is very different from the adult system such as its procedures, incentives, and sanctions. Note that the adult arrest rate is much lower in medical marijuana states, while their juvenile arrest rate is similar to non-medical marijuana states. Actually, it is possible that, after the passage of medical marijuana laws, the law enforcement towards juvenile becomes stronger due to reallocation the resources from adults to juveniles.

13

are nearly identical to these in Table 2. The sample size is smaller because 2001 and 2003

government expenditures were not developed by the Census Bureau due to sample redesigning.

Because most state level controls are poorly estimated, it seems that fixed effects and city

specific time trends have accounted for most of the variations from these controls. I prefer the

specification without any controls as it includes more years in the sample. In Column (5) and (6),

the dependent variable is adult male arrest rate aggregating from all available agencies in the

UCR to the state level, including all cities and non-cities with positive populations. 17 The

estimates are similar to Table 2, though they are a little smaller and insignificant when squared

state time trends are included.

The estimates from Tables 2–5 will be biased if law enforcement responds to medical

marijuana laws. For example, if law enforcement becomes stricter, then the estimates are upward

biased. In Table 6, I indirectly test some implications for potential changes in law enforcement.

The upper panel is level specification and the lower panel is log specification (except for Column

(2)). There is one state, Arizona, which did pass a referendum to legalize medical marijuana in

1996 that did not lead to an effective law.18 If the increase in arrest in medical marijuana states

are driven by unobservables common to states that initiate the legalization process rather than by

the laws themselves, the arrest rate in Arizona would increase after passing the referendum.

However, Column (1) shows that the arrest rate in Arizona actually decreases after the

referendum, which is perhaps a result of that law enforcement becomes more lenient.

In Column (2), the dependent variable is the marijuana possession arrest ratio of adult

African Americans to all adults. 19 Because there are many zero values, I use a fixed-effect

Poisson model in the lower panel. It is well documented that African Americans are much more

likely (about 2.5 times) to be arrested for marijuana possession offences due to racial profiling,

even though their prevalence rate is similar to whites (Golub et al., 2007; Ramchand et al., 2006;

Reuter et al., 2001).20 Therefore, if there were an increase in the proportion of African American

arrestees, this would be a “smoking gun” that police are using racial profiling to increase 17 I exclude zero population agencies such as university or national park police departments. 18 Arizona passed a referendum in 1996 (Proposition 200), which legalized medical marijuana under doctors’ prescription. However, the state legislature dismantled it through the terminology — "prescribe." Because marijuana is a schedule I drug, the DEA prohibits physicians from "prescribing" it. This made Proposition 200 ineffective. 19 The ratio of black arrestees includes females since the UCR does not separate gender within races. 20 Most of the literature only accounts for racial differences at the extensive margin. On the other hand, based on the TEDS, blacks have a larger proportion of high-frequency users.

14

marijuana arrests. However, the estimate is very close to zero and there is no evidence of change

in racial composition after the passage of medical marijuana laws.

In Column (3), I use the ratio of adult male marijuana possession arrests to total drug

possession arrests as a measure of prevalence of marijuana use, which could be approximately

interpreted as a change in relative demand for marijuana.21 In Column (4), I use the ratio of adult

male marijuana possession arrests to total crime arrests. One advantage of these measures is that

they partially control for the level of law enforcement and eliminates the measurement error from

estimated population (Fryer et al., 2005). If the increase in marijuana arrests is simply due to a

stricter level of law enforcement, then medical marijuana laws would not have an effect on these

ratios. The ratio in Column (3) is increased by 0.05 or a 16% increase, and the ratio in Column (4)

is increased by 0.005 or a 25% increase. As for percentage change, the results are very similar to

these in Table 2. A similar measure used in the literature for law enforcement toward illicit drug

is the ratio of total drug arrests (for both sale and possession) to total arrests (Pacula et al., 2003;

Resignato, 2000). For consistency, I only use adult male arrests to create this ratio. Column (5)

does not show evidence of change in law enforcement towards overall drug offenses.

Alternatively, I directly test whether medical marijuana laws increase arrests for non-

marijuana drug possession. In Columns (6), the dependent variables are adult male arrest rate for

possession of all other drugs. The estimates are small and negative, so there is no evidence of an

increase in arrests for non-marijuana drug possession. Cocaine is still the second most popular

drug in the U.S., and I estimate the effect of laws on cocaine possession arrests (adult male)

separately in Column (7). Contrary to the common belief that marijuana use will motivate hard

drug use, the estimates indicate a decrease in cocaine use (although it could be a result of lower

level of law enforcement). Interestingly, Anderson et al. (2012a) also find a huge reduction in

teen cocaine use.

As noted in the previous sections, the number of legal patients and dispensaries was

relatively small in the sample period, and it is more so because California and Colorado are not

in my sample. Therefore, the increase in marijuana use is most likely caused by changes in

perceived legal and health risks. Because of the different legal process, it is plausible that

referendum states are more liberal than lawmaker states, and a higher referendum passage rate 21 The UCR provides the category of total drug possession arrest as well as three subcategories other than marijuana: 1. Opium, cocaine, and their derivatives; 2. Truly addicting synthetic narcotics; 3. Other dangerous non-narcotic drugs.

15

may also imply a more liberal attitude. In Table 7, I compare states passing laws through

referenda to states in which legalization was enacted by lawmakers. If the existing public attitude

is very liberal, so the perceived risk of marijuana is already low, then the implied effect on

marijuana use will be smaller. Therefore, the arrest rate in referendum states with a high passage

rate would increase less than in states with a low passage rate or lawmaker states. (See Table A1

for passage rate in each state.) I create Law×Referendum, the interaction term of Lawst and a

dummy denoting referendum states, and Law×Pro rate, the interaction term of Lawst and passage

rate (%) of referenda (0 for lawmaker states). The left panel is level specification and the right

panel is log specification. The above prediction is supported in log specification: the estimates on

Law×Pro rate and Law×Referendum are negative and significant.22 In the level specification,

however, the estimates are quite noisy and show a positive sign.

Drug arrests often occur during or after transactions. However, unlike cocaine or heroin,

marijuana transactions are closely related to social networks. About 60-80 % of people acquire

marijuana from a friend and often for free; when there are monetary transactions, they are often

exchanged indoors (Caulkins and Pacula, 2006; Pacula et al., 2010; Substance Abuse and Mental

Health Services Administration, 2004; Taylor et al., 2001). The probability of being arrested is

actually low, and therefore most arrestees are probably heavy users who make regular

transactions.23 Because heavy users are more likely to be associated with dependence and need

for treatment; in the next section, I use marijuana treatment patients to provide direct evidence

that medical marijuana laws increase use among heavy users.

5. The Analysis of Treatment Episode Data Set

The treatment data is from the Substance Abuse and Mental Health Services

Administration's (SAMHSA) Treatment Episode Data Set (TEDS) for the years 1992 through

2008. The TEDS collects admission data from all substance-abuse treatment facilities that

receive public funding in each state. (Some states only collect data on public funded patients.)

For each admission, the data identifies the primary, secondary, and tertiary substance abuse

22 Because people in referendum states should be more aware of the laws, the larger effect from lawmaker states suggests that the effect of legalization is stronger than the potential effect coming from the publicity surrounding the laws. Because the publicity effect is likely to be decreasing over time, the dynamic in Table 4 also implies that the publicity effect is not a major cause for the increase in marijuana use. 23 Although heavy users might prefer to buy more in each transaction and therefore make less transactions, most marijuana transactions involve small quantities because legal penalty directly depends on the weight of possession.

16

problem of the patient, his/her demographics such as gender and age, referral sources, and the

number of prior treatment the patient ever received. 24 Similar to the UCR, each admission does

not represent an individual, but it is possible to create a measure representing individuals by

using only admissions without any prior treatment. This measure can avoid bias from recidivism

that is particularly a potential problem for using treatment data (Anderson, 2010). I focus on two

professional referral sources, alcohol or drug abuse care providers and health care providers that

reflect professional criteria of marijuana abuse. These medical professionals are unlikely to be

directly affected by general public's perception of risk and law enforcement. 25 Because the

number of admissions in the TEDS greatly fluctuates in some state-years (see also Note 26), as

commonly used by the SAMHSA, I create ratios of marijuana treatments to all substances

treatments within professional referrals for each state. To be consistent with the UCR arrest, I

only use adult (above age 18) male admissions and exclude California and Colorado. The sample

has all medical marijuana state that passed laws before July 2008; except for Alaska that is

missing for most of years, they have data in every year.26

Table 8 presents descriptive statistics on the marijuana-related and marijuana-primary

treatment ratios. I define marijuana-related treatment admissions if marijuana is identified as

either primary, secondary or tertiary abuse problem, and marijuana-primary treatment admissions

if marijuana is recorded as the primary abuse substance. The denominators of these ratios are

professional referred treatment admissions for all substances. In the upper panel, the numerators

of these ratios are marijuana treatment admissions with any number of previous treatment

episodes. About a third of patients have marijuana abuse problem, but only less than 8% of

patients have marijuana as their primary problem. To obtain a measure representing individuals,

in the lower panel, I construct the ratios using only first-time marijuana treatment admissions. 27

24 About half of the adult male marijuana-related treatment are referred by criminal justice system, a quarter are individual referrals, and around 15% are professional referrals. The rest 10% are referred by community or religious organizations, and self-help groups such as Alcoholics Anonymous. 25 However, changes in perceived risk and law enforcement could affect people's behavior of seeking medical professionals. In fact, as in the UCR data, the estimates for California and Colorado laws are still negative and significantly different from other medical marijuana states. 26 Alaska does not report referral source for years 1999-2003, and it does not report any data for years 2004-2007. (Because the data quality in Alaska is so low, I also drop its 2008 data.) Moreover, since year 1999, there was probably a change in available funding or reporting process in Alaska and Washington: the total numbers of treatment reported were only about half to their previous levels. 27 It is not possible to observe whether a patient has had prior treatment episodes for a particular substance; only the number of previous treatment episodes a patient has had for any drug or alcohol problem is available.

17

On average, only 10% of admissions lack the information on previous treatments, although it is

largely missing in some state-years. I restrict the sample to state-years which are missing less

than 50% of this information, and scale the treatment ratios by the proportion of reporting data in

each state-year.28 Except for Washington that does not report the number of prior treatment for

years 1992-1999, the information is very complete in medical marijuana states, with an average

missing rate of 1.7%. It shows that about half of the patients are first-time patients.

To examine the effect of medical marijuana laws on marijuana treatments, I estimate the

following model by OLS:

(2) Yst = β Lawst + State fixed effectss + Year fixed effectst + State time trendsst + εst ,

where Yst is the treatment rate or its log in state s and year t. As in the UCR analysis, I do not

include any controls to keep a larger sample size. The results in Table 9 below are nearly

identical when the same set of state-level controls is included (not reported).

Table 9 shows the estimates of β from Equation (2). The first two columns are from the

level specification, the second two columns are from the log specification, and the last two

columns are from a fixed-effect Poisson model. The upper panel shows the results for all

treatment ratios (with any number of previous treatment episodes). In terms of percentage change,

the estimated effects are a little larger but noisier for marijuana-primary ratio. Specifically, on

average, medical marijuana laws are associated with a 9-12% increase in marijuana-related

treatment ratio, or 10-17% for marijuana-primary treatment ratio. Because there may be capacity

constraints for treatment facilities, the smaller results are expected and consistent with the results

from the UCR.

Because a proportion of patients will repeatedly enter treatments due to addiction, we

would expect the estimates based on patients with previous treatment episodes to be smaller than

estimates from first-time patients. However, in the lower panel in Table 9, the estimates are

actually greater. The estimates are similar across marijuana-related or -primary treatment ratios.

Specifically, on average, medical marijuana laws are associated with a 17-25% increase in first-

28 I also exclude 14 state-years (including Rhode Island in 2003 and 2004) with zero first-time treatment. These observations are clearly missing data because they differ a lot from data in the previous or later years. The regression results in Table 8 are slightly greater without scaling or excluding zeros. (The estimated standard errors are 10-20% larger without scaling).

18

time treatments. In fact, the estimates in the upper panel are entirely driven by first-time

treatments as that first-time treatments account for around 40-50% of all marijuana treatments.

Although not reported, I estimate the effect of laws on patients with at least one previous

treatment episodes, and the results are nearly zero.29

It is straightforward to see graphically that the estimates in Table 9 are driven by first-

time treatments. Figure 2, constructed in the same way as Figure 1, shows the effect of laws on

marijuana-related treatment ratios (in logarithm). The upper graph is from all treatments, and the

lower graph is from first-time treatments. Both graphs show similar patterns of an increase in

marijuana-related treatment ratios after the passage of laws in medical marijuana states; however,

the magnitude from all treatments is much smaller than the magnitude from first-time treatments.

In the Appendix, Table A3 shows the descriptive statistics and regression results from all

referral sources. The data contain patients referred by criminal justice system and common

individuals (or self referrals) that are probably affected by law enforcement or perceived risk

among public. In general, the results are qualitatively similar for marijuana-primary treatments,

although they are more sensitive to time trends specification especially for marijuana-related

treatments. The estimates based on first-time patients are still larger than patients with previous

treatment episodes. Figure A1 is the graphical analysis, and the graphs look very similar to

Figure 2.

In Table A4, I estimate the effect on male juveniles (age 12-17) for all source referrals.

Because the number of juvenile patients is very small in some state-years, I drop 27 state-years

in which the total number of patients (for any substances and from any referral sources) is less

than 20. As 75% of juveniles are first-time patients, I do not separately estimate effects for first-

time treatments. Consistent with the popularity of marijuana among juveniles, 80% of treatment

juvenile patients report marijuana abuse, and it is the primary abuse problem for nearly 60% of

them. In contrast to previous studies that do not find any effect of laws on juvenile, the results

indicate around a 5-15% increase in marijuana-related or -primary treatments.30 I do not report

29 I also try to restrict the sample to be the same across the two panels, but the results are nearly identical. So the smaller estimates in the upper panel are not due to sample difference. 30 In Anderson et al. (2012a), they use state populations as the denominators to create treatment rates for teenagers aged 15-17 and 18-20, and their estimates are often negative. However, using population as denominator may be inappropriate as the TEDS data greatly fluctuates in some states. On the other hand, the estimates based on treatment rates using populations as the denominators are similar to results reported here if Washington are excluded (see also Note 26).

19

the results separately for professional referrals as the number of juvenile patients are quite small;

the estimates for professional referrals are a little greater (6-19% increase) but much more noisier.

6. Discussion of results and conclusion

In this paper, I estimate the effect of medical marijuana laws on illegal marijuana use

based on marijuana possession arrests. My estimates show a positive effect that is, as expected,

strongest among young adults. The results suggest that usage increased by roughly 20% among

adult males. There is no evidence that these results are driven by stronger law enforcement.

Based on the relatively small number of legal patients and dispensaries, and the smaller effect of

laws from potentially more liberal states (Table 7), an important potential channel through which

these laws increase marijuana use is through a lower perceived risk.

Those who are arrested for marijuana possession are likely to be heavy users, who may

be most prone to dependence and in need of professional treatment. I use marijuana treatment

referrals by medical professionals as a proxy for heavy usage, and I find that this treatment

increases by around 10% after the passage of laws. Somewhat surprisingly, the estimates on first-

time treatments indicate a greater effect of around 20%. In contrast to previous studies that use

measures for general use rate among juveniles and do not find any effect of these laws, the

estimates indicate a 5-15% increase in juvenile treatments.

A 10-20% increase is a large effect but it is plausible for heavy users. Based on existing

studies, MacCoun (2010) suggests that the non-price effect of marijuana decriminalization is

around 35% increase in general use rate (use in past month). 31 Although medical marijuana laws

represent a less dramatic change than decriminalization, a 10-20% increase is not particular large

for heavy users as previous research suggests that heavy users are disproportionately responsive

to legal changes (Becker and Murphy, 1988). This magnitude also seems reasonable in

comparison to policy changes regarding other substances. For example, Carpenter (2004) finds

that zero tolerance drunk driving laws are associated with a 13-18% reduction in heavy drinking.

Similarly, DiNardo and Lemieux (2001) find that marijuana use rate among high school seniors

increases by around 10% after the legal drinking age increased to 21.

31 Based on the 2002-2008 NSDUH, I find medical marijuana laws increase general use by around 5% for adults, although it is only significant for ages 18-25.

20

The difference between the estimated effects based on first-time treatments and all

treatments has interesting implications. First, it implies that, on average, marijuana is not

strongly addictive, which is consistent with existing medical evidence. Second, it suggests that

medical marijuana laws do not have a significant effect on strongly addictive patients who

repeatedly enter treatments. These patients could be "always-takers" who would be heavy

marijuana users regardless of marijuana's legal status. Finally, consistent with the negative

estimates on cocaine arrest from Table 6, it does not support the popular belief that use of

marijuana increases abuse of hard drugs. Strongly addictive patients who repeatedly enter

treatment facilities are often users of hard drugs such as cocaine and heroin. For first-time

marijuana-related treatment patients, 37% also report cocaine abuse, and 6% report heroin abuse.

On the other hand, for patients with at least one previous treatment episode, the proportion that

reports cocaine and heroin abuse increases to 49% and 11%, respectively. Moreover, this pattern

is monotonically increasing with the number of previous treatments.

Although the estimates in this paper are only appropriate for inference on heavy users,

they may be more relevant to policy concerns because heavy marijuana users are often associated

with negative health and social outcomes, such as developing dependence, the need for treatment

and future use of hard drugs (Chen et al., 1997; Fergusson et al., 2006; Gruber et al., 2003). A

20% increase in heavy users, as indicated by both arrest and first-time treatments, represents a

nontrivial cost to the society. On the other hand, based on the estimates from on all treatments,

the net effect on treatment is only 10%, which may be due to substitution between marijuana and

other substances. This substitution can be viewed as a benefit of medical marijuana laws, and

there could be additional benefits; for example, Anderson and Rees (2011) and Anderson et al.

(2012b) show evidence for a decrease in drunk driving and suicide. Therefore, evaluating the net

effect of medical marijuana laws requires a more careful benefit and cost analysis that is beyond

the scope of this study.

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25

Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.All adult (age 18+) 158.46 130.99 118.12 84.64 162.06 133.77

Age 18 - 20 47.54 38.50 34.70 29.71 48.69 38.99

Age 21 - 24 39.41 34.66 27.64 21.13 40.47 35.43

Age 25 - 29 28.29 25.90 20.15 15.41 29.01 26.52

Age 30 - 34 17.51 16.39 13.26 10.37 17.89 16.77

Age 35 - 39 11.62 11.73 9.53 7.69 11.81 12.01

Age 40 - 44 7.29 8.00 6.31 6.09 7.38 8.15

Age 45 + 6.79 8.61 6.54 7.56 6.81 8.70

City-year obs.

8,722 (562 cities) 715 (48 cities) 8,007 (514 cities)

Note.― Medical marijuana states include only states that passed laws before July 2008; states that passed laws afterward are in "other states." California, Colorado and Vermont are not in the sample.

Table 1: UCR Descriptive Statistics in Each Male Age Group (1988-2008)

Medical marijuana states Other states All states

26

(1) (2) (3) (4) (5) (6)

28.22*** 26.94*** 29.83*** 25.59*** 24.06** 19.71*(6.23) (6.58) (5.09) (5.16) (10.83) (11.62)

0.282*** 0.225*** 0.288*** 0.187*** 0.303*** 0.253***(0.095) (0.076) (0.090) (0.061) (0.084) (0.061)

0.168*** 0.198*** 0.177*** 0.168*** 0.150** 0.129*(0.060) (0.042) (0.040) (0.036 (0.074) (0.078)

Note.― All specifications include city and year fixed effects. Robust standard errors are reported in parentheses, and they are clustered at the state level. *** p<0.01, ** p<0.05, * p<0.1.

Quadratic (Group)

Linear (Group)

Quadratic (State)

Linear (State)

Quadratic (City)Linear (City)

Table 2: Effects of Medical Marijuana Laws on Adult Male Arrests

FE Poisson

Linear Model

Log-Linear Model

Obs. 8722

Time trend specification

27

18 - 20 21 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45+

8.560*** 6.150*** 6.575*** 3.132** 2.222*** 0.244 0.055(2.148) (1.436) (1.520) (1.450) (0.804) (0.666) (0.603)

0.215*** 0.226*** 0.271*** 0.180** 0.177*** 0.052 -0.015(0.0335) (0.0485) (0.0678) (0.0854) (0.0578) (0.058) (0.074)

Table 3: Effect of Medical Marijuana Laws in Each Male Age Group

Note.― All specifications include city and year fixed effects. The upper panel includes city quadratic time trends, and the lower panel includes only city linear time trends. Robust standard errors are reported in parentheses, and they are clustered at the state level. *** p<0.01, ** p<0.05, * p<0.1.

Male arrest rate for each age group (Linear Model)

Male arrest rate for each age group (FE Poisson)

28

-0.836 -1.249 0.0364 0.0122(9.799) (11.19) (0.108) (0.0947)

30.12*** 30.36*** 29.55** 29.14* 0.322*** 0.298*** 0.347** 0.310**(6.18) (6.95) (11.79) (16.39) (0.074) (0.086) (0.131) (0.149)

37.67*** 35.76** 36.97* 34.10 0.373** 0.301 0.403** 0.318(12.86) (16.48) (18.53) (27.06) (0.150) (0.216) (0.192) (0.274)

39.08*** 33.48* 38.25** 31.29 0.401* 0.298 0.437* 0.319(12.80) (17.82) (17.48) (28.85) (0.202) (0.299) (0.231) (0.354)

44.23*** 36.24 43.27* 33.46 0.507** 0.384 0.549** 0.411(15.73) (24.09) (21.64) (38.13) (0.211) (0.368) (0.251) (0.442)

64.07*** 58.26* 62.99** 54.79 0.723*** 0.669 0.770*** 0.703(19.69) (33.62) (27.02) (50.06) (0.228) (0.505) (0.278) (0.594)

City time trends Linear Quadratic Linear Quadratic Linear Quadratic Linear Quadratic

Obs. 8,722 Note.― All specifications include city and year fixed effects. Robust standard errors are reported in parentheses, and they are clustered at the state level. *** p<0.01, ** p<0.05, * p<0.1.

Table 4: Dynamic Responses of Adult Male Arrests to Medical Marijuana Adoption

Years 8 - 9

Adult male log arrest rateAdult male arrest rate

Years 4 - 5

Years 6 - 7

Years (neg. 1 - 2)

Years 0 - 1

Years 2 - 3

29

(1) (2) (3) (4) (5) (6)

27.35*** 22.34*** 29.68*** 21.08*** 23.60** 11.32(6.77) (8.29) (5.86) (5.21) (9.39) (9.93)

0.202*** 0.199* 0.323*** 0.227*** 0.284** 0.152(0.063) (0.104) (0.083) (0.073) (0.129) (0.151)

Quadratic (city)

Quadratic (city) Linear (city) Quadratic

(city) Linear (state ) Quadratic (state)

8,762 9,053 7,880 7,880 964 964

Table 5: Robustness Checks

Obs.

Time trend specification

Adult male arrest rate

Note.― . Column (1) includes cities that report at least 6 months (agencies that report only in December are excluded), and the arrest counts are scaled by a factor that equals the fraction reported of a year. Column (2) includes cities reporting less than 6 months. Coulumn (3) and (4) include city police officer rate, and state level control variables: black male rate, unemployment rate, state and local government police expenditures, state and local government health and hospital expenditure, and 0.08 BAC laws. Column (5) and (6) include all agencies to create state-level arrest rate (per 100,000 state residents). All specifications include city (state) and year fixed effects. Robust standard errors are reported in parentheses, and they are clustered at the state level. *** p<0.01, ** p<0.05, * p<0.1.

City and state controls included

All agencies aggregated to state level

Scaled by reporting months

Reporting any months

Adult male log arrest rate

30

(1) (2) (3) (4) (5) (6) (7)

Arizona Law Black arrest ratio

Marijuana to total drug

arrest ratio

Marijuana to total arrest

ratio

Total drug to total arrest

ratio

Other drug arrest rate

Cocaine arrest rate

-5.963* 0.0017 0.053*** 0.0046** 0.0011 -2.633 -19.24**(3.495) (0.006) (0.018) (0.002) (0.002) (9.007) (8.92)

-0.127*** -0.00087 0.161*** 0.247*** 0.025 -0.071 -0.288***(0.025) (0.044) (0.055) (0.071) (0.045) (0.073) (0.056)

8,722 8,711 8,722 8,722 8,722 8,619 8,235

Table 6: Tests for Change in Law Enforcement

Note.― In Column (1), the estimate in the lower panel is from a fixed -effect Poisson model. All specifications include city and year fixed effects and city quadratic time trends. Robust standard errors are reported in parentheses, and they are clustered at the state level. *** p<0.01, ** p<0.05, * p<0.1.

Adult male arrest rate

Adult male log arrest rate

Obs.

31

15.52 15.54 0.473*** 0.446***(15.85) (15.70) (0.078) (0.086)13.91 -0.301***

(17.05) (0.109)0.24 -0.005**

(0.29) (0.002)

Note.― All specifications include city and year fixed effects and city quadratic time trends. Robust standard errors are reported in parentheses, and they are clustered at the state level. *** p<0.01, ** p<0.05, * p<0.1.

Table 7: Implications of Referendum

Law

Law* Referendum

Law* Pro rate

Adult male arrest rate Adult male log arrest rate

Obs. 8,722

32

Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.

Marijuana-related 0.323 0.090 0.344 0.090 0.318 0.090

Marijuana-primary 0.077 0.038 0.075 0.035 0.078 0.038

Obs.

Marijuana-related 0.131 0.072 0.143 0.073 0.128 0.072

Marijuana-primary 0.039 0.023 0.038 0.024 0.039 0.023

Obs.

Table 8: TEDS Descriptive Statistics 1992-2008 (Professional Referrals)

All states Medical marijuana states Other states

786 159 627

Note.― Medical marijuana states include only states that passed laws before July 2008; states that passed laws afterward are in "other states." California and Colorado are not in the sample. The sample for first-time treaments only includes state-years that the information on prior treament is missing less than 50%.

688 149 539

All Treatment

First Time Treatment

33

0.033** 0.039*** 0.090** 0.121*** 0.103*** 0.108***(0.013) (0.011) (0.034) (0.027) (0.035) (0.026)

0.007 0.011* 0.095 0.173*** 0.108 0.142**(0.006) (0.006 ) (0.057) (0.065) (0.068) (0.064)

0.033** 0.032*** 0.215** 0.218*** 0.223*** 0.207***(0.013) (0.009) (0.086) (0.061) (0.079) (0.050)

0.007 0.009** 0.172 0.227* 0.157 0.211**(0.006) (0.005) (0.122) (0.114) (0.103) (0.088)

Linear Quadratic Linear Quadratic Linear Quadratic Note.― All specifications include state and year fixed effects. Robust standard errors are reported in parentheses, and they are clustered at the state level. *** p<0.01, ** p<0.05, * p<0.1.

First-time Treatment (Primary)

All Treatment

All Treatment (Primary)

Table 9: Effect of Medical Marijuana Laws on Professional Referred Marijuana Treatments

Time trend specification

Treatment ratio (Linear Model) Log treatment ratio (Linear Model) Treatment ratio (FE Poisson)

First-time Treatment

34

Appendix Table A1

State Medical Marijuana Laws

State Pass/Effective date Pass rate Registration

Alaska Nov. 3, 1998 /Mar. 4, 1999

58% (Measure 8) Yes

Arizona Nov. 2, 2010 50.13% (Proposition 203) Yes

California Nov. 5, 1996 /Nov. 6, 1996

56% (Proposition 215)

Yes (Voluntary since Jan. 1, 2004)

Colorado Nov. 7, 2000 /Jun. 1, 2001

54% (Amendment 20) Yes

Connecticut May 31, 2012 96-51 House; 21-13 Senate (HB 5389)

Yes Program not yet established

D.C May 21, 2010 /Jul. 27, 2010

13-0 vote (Amendment Act B18-622)

Yes Program not yet established

Delaware May 13, 2011 /Jul. 1, 2011

27-14 House; 17-4 Senate (Senate Bill 17)

Yes Program not yet established

Hawaii Jun. 14, 2000 /Dec. 28, 2000

32-18 House; 13-12 Senate (Senate Bill 862) Yes

Maine Nov. 2, 1999 /Dec. 22, 1999

61% (Question 2)

Yes (Mandatory after Dec. 31,

2010)

Michigan Nov. 4, 2008 /Dec. 4, 2008

63% (Proposal 1) Yes

Montana Nov. 2, 2004 62% (Initiative 148) Yes

Nevada Nov. 7, 2000 /Oct. 1, 2001

65% (Question 9) Yes

New Jersey Jan. 18, 2010 48-14 House; 25-13 Senate (Senate Bill 119)

Yes Program not yet established

35

New Mexico Mar. 13, 200 /Jul. 1, 2007

36-31 House; 32-3 Senate (Senate Bill 523) Yes

Oregon Nov. 3, 1998 /Dec. 3, 1998

55% (Measure 67) Yes

Rhode Island Jan. 3, 2006 52-10 House; 33-1 Senate (Senate Bill 0710) Yes

Vermont May 26, 2004 /Jul. 1, 2004

82-59 House; 22-7 Senate (Senate Bill 76) Yes

Washington Nov. 3, 1998 59% (Initiative 692) None

36

Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.Age 12 - 17 34.88 30.22 33.98 32.47 34.96 30.01Age 15 - 17 29.88 25.67 26.97 25.70 30.14 25.65

4.08 3.62 4.20 3.84 1.40 1.26(4.26) (3.84) (3.79) (3.49) (5.61) (5.29)

0.071 0.099 0.072 0.075 0.049 0.053(0.101) (0.100) (0.087) (0.091) (0.093) (0.086)

5.28* 4.94** 5.12* 5.04** 2.62 2.03(2.86) (1.94) (2.58) (2.00) (4.62) (4.43)

0.103 0.177** 0.105 0.125* 0.079 0.082(0.090) (0.077) (0.077) (0.069) (0.095) (0.087)

Obs. 8722 Note.― All specifications include city and year fixed effects. Robust standard errors are reported in parentheses, and they are clustered at the state level. *** p<0.01, ** p<0.05, * p<0.1.

Time trend specification

Linear (City) Quadratic (City) Linear (State) Quadratic

(State) Linear (Group) Quadratic (Group)

Linear Model

Age 12 - 17 FE Poisson

Linear Model

Age 15 - 17 FE Poisson

Regression Results

Table A2: Effects of Medical Marijuana Laws on Male Juveniles (Ages 12-17)

Descriptive StatisticsAll states Medical marijuana states Other states

37

Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.

Marijuana-related 0.351 0.096 0.357 0.088 0.349 0.097Marijuana-primary 0.116 0.051 0.093 0.033 0.121 0.053

Obs.

Marijuana-related 0.168 0.069 0.163 0.062 0.170 0.071Marijuana-primary 0.065 0.033 0.050 0.020 0.070 0.034

Obs.

0.033*** 0.011 0.094*** 0.025 0.094*** 0.033(0.008) (0.009) (0.025) (0.027) (0.021) (0.022)

0.017*** 0.012** 0.188*** 0.103* 0.182*** 0.111**(0.003) (0.005) (0.028) (0.058) (0.026) (0.050)

0.028** 0.018 0.200** 0.120* 0.168** 0.110*(0.011) (0.011) (0.083) (0.061) (0.069) (0.062)

0.011** 0.007* 0.252*** 0.150* 0.207*** 0.136*(0.004) (0.004) (0.091) (0.074) (0.080) (0.071)

Linear Quadratic Linear Quadratic Linear Quadratic

Table A3: Effect of Medical Marijuana Laws on All Referral Source Marijuana Treatments

628

All Treatment

All Treatment

First-time Treatment

All states Medical marijuana states Other states

First-time Treatment788 160

690 150 540

Note.― All specifications include state and year fixed effects. Robust standard errors are reported in parentheses, and they are clustered at the state level. *** p<0.01, ** p<0.05, * p<0.1.

Treatment ratio (Linear Model)

Log treatment ratio (Linear Model)

Treatment ratio (FE Poisson)

Descriptive Statistics

First-Time (Primary)

Regression Results

All Treatment (Primary)

Time trend specification

38

Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.

Marijuana-related 0.792 0.148 0.838 0.119 0.781 0.152Marijuana-primary 0.584 0.173 0.577 0.147 0.586 0.180

Obs.

0.0367*** 0.0322** 0.0805*** 0.0746* 0.0528*** 0.0469**(0.00834) (0.0155) (0.0209) (0.0387) (0.0113) (0.0211)

0.0383** 0.0247* 0.146*** 0.0933** 0.0834*** 0.0528*(0.0163) (0.0125) (0.0472) (0.0452) (0.0258) (0.0311)

Linear Quadratic Linear Quadratic Linear Quadratic

Table A4: Effect of Medical Marijuana Laws on Male Juvenile TreatmentsDescriptive Statistics

All states Medical marijuana states Other states

All Referrals

759 155 604

Regression ResultsTreatment ratio (Linear Model)

Log treatment ratio (Linear Model)

Treatment ratio (FE Poisson)

Time trend specification

Note.― All specifications include state and year fixed effects. Robust standard errors are reported in parentheses, and they are clustered at the state level. *** p<0.01, ** p<0.05, * p<0.1.

All Referrals

All Referrals (Primary)

39

Figure 1: Adult male log arrest rate

40

Figure 2: Adult male log treatment ratio from professional sources

41

Figure A1: Adult male log treatment ratio from all referral sources