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Universidad de San Andrés
Departamento de Economía
Licenciatura en Economía
Do Medical Marijuana Laws Reduce Crime?
An Empirical Approach to Drug Enforcement Policy and Its Effect on Criminal Activity
Autor: Marcos J. Mercado
Legajo: 22.163
Mentor: Martín Rossi
Buenos Aires, 25 May 2014
1
Do Medical Marijuana Laws Reduce Crime?
An Empirical Approach to Drug Enforcement Policy and Its Effect on Criminal Activity
Marcos J. Mercado1
Universidad de San Andrés
Abstract
This paper studies the effect of medical marijuana law on different types of crime reported. The effect
these laws have on society seems to be bigger than just through the community of medical marijuana users.
With data from the Uniform Crime Reports we are able to exploit the fact that states passed this law at
different points in time. This variation of the implementation of the law gives us the possibility of, by using a
Dif-in-Dif approach, analyze panel data. After controlling for state and year fixed effects, as well as other
variables, we find that reported index I crimes fall a 3.79% relative to states who were never treated. Index I
crime’s components are further analyzed and we find that property crimes seem to be responsible for this
drop. Property crimes suffer a 4% fall in crimes reported in states that enact medical marijuana relative to
those who do not. We argue that several mechanisms generate a change in law enforcement resource
allocation and try to approach econometrical proof that law enforcement is reallocating resources after
medical marijuana law has been enacted. After this we try to better assess the nature of this re-allocation. Our
study tries to anticipate impending research on marijuana policy and its effect on society, brought on by the
recent legalization of recreational marijuana in Colorado and Washington.
1 Marcos J. Mercado, Universidad de San Andrés, Vito Dumas 284, Victoria, Provincia de Buenos Aires, Argentina,
[email protected]. I would like to thank my mentor, Martin Rossi, for his valuable comments and guidance. I am also grateful to Nicolas Roig, Sofia Muñiz, Gerardo Munck, Corinne Munck and Mara Mercado for their never ending support and helpful discussions.
2
Index
Introduction ............................................................................................................................................................ 3
The Treatment ........................................................................................................................................................ 4
Marijuana law history.......................................................................................................................................... 4
Medical Marijuana Laws ..................................................................................................................................... 5
Possible Mechanisms linking MML and crime ................................................................................................... 7
The Data .................................................................................................................................................................. 9
The Empirical Model............................................................................................................................................ 9
The Identification Strategy ................................................................................................................................ 10
Main Results ...................................................................................................................................................... 14
Medical Marijuana Laws and law enforcement resource allocation ................................................................. 19
Arrests and resource allocation ....................................................................................................................... 19
Further Analysis ................................................................................................................................................ 22
Drug policy and age .......................................................................................................................................... 24
Enactment and effect – Final robustness check ............................................................................................... 28
Conclusion ............................................................................................................................................................ 30
References ............................................................................................................................................................ 32
Appendix ............................................................................................................................................................... 34
3
1. Introduction
In 2012 the states of Washington and Colorado enacted marijuana legalization laws. The controversy generated
by these laws leads us to believe that marijuana laws seem to be more of a political than a scientific matter. Goode
(1969) reinforces this affirmation by stating that this topic has turned into one of the bastions in the establishment of a
moral hegemony. The problem with the discussion is that it usually leaves out the effects of the drugs per se and appeals
to a less rational component.
The motivation for this paper, therefore, comes from the impending marijuana legalization in the US and the state
of discussion regarding the possible consequences of marijuana laws. This lack of a consensus across the literature might
very well be because of the influence that politics and emotion play over research. With this in mind, the study of how
marijuana laws might influence crime becomes a very appealing field to look into.
The potential impact that marijuana laws can have on marijuana use has received plenty of legislative attention.
The purpose of this paper is to assess the effect that medical marijuana laws (MMLs) have on crime rates. The
connection between drugs and crime is one of the main reasons used to block more permissive marijuana laws. Even
when the benefits of medical marijuana are well recorded, medical marijuana laws are not enacted in all US states.
Since the compassionate use act of 1996, which legalized marijuana for medical purposes in California, 21 states
and the federal district have legalized medical marijuana. Five of these states did so between 2012 and 2013, and more
have pending legislation regarding the matter2. With more permissive marijuana laws looming in the near future, the
analysis of medical marijuana laws and their effect on crime is a pressing matter for society as a whole.
The paper is organized as follows. The first section will be centered on the treatment. The first part is an analysis
of marijuana law; after this, we make an in depth analysis of medical marijuana laws, their history and different
mechanisms linking the treatment to crime. The second section will analyze the data available to carry out a study. We
will assess the possibility of considering medical marijuana law (MML) as a possible cause of a fall in crime. The third
section will explain the Dif-in-Dif approach to panel data, the possibility of identifying a causal effect, the model
specified, and the identification strategy. The fourth section will present the results and discuss the possibility that the
resource allocation mechanism is the main reason for a fall in crime after enacting MMLs. With this mechanism in mind,
we can further explain discernible effects of MML on crime. Finally we conclude summarizing our primary results,
highlighting the weaknesses of this study and generating questions for further analysis of the subject.
2Medicalmarijuana.procon.org,. 2014. '5 States With Pending Legislation To Legalize Medical Marijuana - Medical Marijuana - Procon.Org'.
Accessed May 25 2014. http://medicalmarijuana.procon.org/view.resource.php?resourceID=002481
4
2. The Treatment
A) Marijuana Law History
Prohibition is a law forbidding the manufacture and sale of something3. Past prohibitions include the unsuccessful
alcohol prohibition in the 1920s and early 1930s in the US. This prohibition brought upon the United States a crime wave
that has been unmatched since. The illegality of alcohol, and the voracious demand for it, generated a market where
opportunist criminals could greatly benefit, causing the surge of organized crime.
Marijuana laws date back to 1913, when California passed the first prohibition law aimed at recreational use of
marijuana (Gieringer 1999)4. In 1937, the Federal Bureau of Narcotics (FBN) led a campaign portraying marijuana users
as violent and criminal. With the support of the FBN and its assistant prohibition commissioner Harry Anslinger, the
marihuana tax act terminated legal use of marijuana. In order to posess and use marijuana a tax stamp had to be paid.
These tax stamps were difficult to obtain, making marijuana pretty much illegal. This referred to any kind of marijuana
even that used for treating medical impairments (Bilz 1992)5.
In 1944 Fiorello La Guardia, mayor of New York City at the time, commissioned a special committee of 31
scientists to study the effects of marijuana6. This report was referred to as “The marijuana problem in the city of New
York”. Fiorello’s idea was to abolish a law that can’t be enforced. The common perception that laws legislate morality is
interesting. A marijuana law, as gay laws, tries to legislate private morality. Though this report disproved all negative
effects attributed to marijuana, it did not have any impact on the federal government’s view on marijuana use.
In 1956, the Narcotic Control Act was passed as a result of a nationwide investigation on drug use and crime. This
act imposed some of the most extreme penalties on drug users to date. In Missouri, a 2nd possession charge could be
grounds for a life sentence. The Single Convention on Narcotics drugs (1961) was an international treaty that aimed to
prohibit the sale and production of certain drugs7. By 1970, the Controlled Substances Act classified marijuana as a
schedule 1 drug. A schedule 1 drug must have danger of abuse and no medical value for treatment, in order to be
classified as such89. If the Federal Drugs Administration (FDA) finds a drug to be addictive; then, under the authority of
the Controlled Substances Act, it petitions the Drug Enforcement Administration (DEA) to place the drug on the list of
controlled substances10. No significant change in federal law has happened since. Since 1973, 13 state legislatures have
enacted marijuana decriminalization11. Marijuana users do not face jail time for the possession or use of small amounts
of marijuana. This means that users are not prosecuted. Yet, this still means users might have to attend court or pay a
small fine.
3 As defined by the Oxford Dictionary, 13/04 http://www.oxforddictionaries.com/es/definicion/ingles_americano/prohibition
4 As seen in Anderson et. al (2013), page 335.
5 As seen in Anderson et. al (2013), page 335.
6 "The La Guardia Committee Report." La Guardia Committee Report. http://www.druglibrary.org/schaffer/library/studies/lag/lagmenu.htm
(accessed May 28, 2014). 7 Wikimedia Foundation. "Single Convention on Narcotic Drugs." Wikipedia. http://en.wikipedia.org/wiki/Single_Convention_on_Narcotic_Drugs
(accessed May 26, 2014). 821 U.S.C. §§ 812.(Cohen, 2009)
9 There is no consensus regarding the long term effects of marijuana use.
1021 U.S.C. §§ 801–971. 11 "NORML.org - Working to Reform Marijuana Laws." Marijuana Decriminalization & Its Impact on Use. http://norml.org/aboutmarijuana/item/marijuana-decriminalization-its-impact-on-use-2 (accessed May 28, 2014).
5
B) Medical Marijuana Laws
Contradictory to what federal law would lead us to believe, people have used marijuana historically for lots of
different medical reasons, ranging from treating malaria to relieving headaches12. From 1850 to 1942, marijuana had
been included in the “United States Pharmacopeia” with all recognized medicinal drugs (Bilz 1992)13
“Marijuana is widely considered to be an extremely effective medicine to assist with those common side
effects associated with cancer, chemotherapy, and AIDS. Marijuana also helps ease some of the suffering
associated with ailments such as glaucoma, epilepsy, multiple sclerosis, paraplegia, quadriplegia, and
chronic pain….There is a legitimate health interest in smoking marijuana that must be recognized for
persons suffering from AIDS, cancer, multiple sclerosis, glaucoma, and other serious illnesses, who need
to use the substance to help alleviate some of their ailments. Accordingly, there should be a distinction
made between those in need of the medication, and those solely using it for recreational purposes, as
there is for other controlled substances.” (Hussein, 2000)14
As we can derive from Cohen (2009), it is important to make a distinction between medical and recreational
marijuana. When the drug is used with recreational purposes, large doses might be taken for prolonged periods of time.
Medical marijuana is administered in doses that are enough to produce the desired effect. It is not appropriate to
compare the effects on users of medical versus recreational marijuana.
Because of the possible advantages of providing medical marijuana, several states have decided to enact medical
marijuana laws. The federal government can only prohibit marijuana from travelling to other states. The cultivation and
its use within a state fall outside the scope of federal law. Congress has tried to use the Commerce Clause, which would
cause this matter to fall within federal jurisdiction (Hussein 2001). In 2009 the Attorney General has adopted a policy of
not prosecuting users who are complying with state laws. This means individual states enjoy almost complete liberty in
this matter with very limited federal interference.
Medical marijuana laws remove penalties for using, possessing, and, in some cases, cultivating marijuana plants.
The patients are required to receive approval from a certified physician. The doctors and patients are not prosecuted as
long as the drug is intended for medical purposes. Medical marijuana laws might even allow a caregiver to obtain
marijuana for the aforementioned patient.
12
Examples of other drugs derived from botanicals: “Digitalis leaf, derived from Digitalis purpurea (the foxglove plant), is the source of drugs commonly used to treat congestive heart failure.48 Papaversomniferum (the opium poppy) provides opium49 from which morphine used to treat pain is derived.50 Donnatal™, a medication used to treat irritable bowel syndrome, contains belladonna alkaloids—originally found in Atropa belladonna, the deadly nightshade plant—as one of its active ingredients…” (Cohen,2009), page 46. 13
As seen in Anderson et. al (2013), page 335. 14
Hussein (2001), page 2.
6
Table 1 - Medical Marijuana Law Treatment Specifics
State Law Enacted Law Took Effect
CALIFORNIA 1996 1996
ALASKA 1998 1999
OREGON 1998 1998
WASHINGTON 1998 1998
MAINE 1999 1999
COLORADO 2000 2001
HAWAII 2000 2000
NEVADA 2000 2001
MONTANA 2004 2004
VERMONT 2004 2004
RHODEISLAND 2006 2006
NEWMEXICO 2007 2007
MICHIGAN 2008 2008
ARIZONA 2010 2011
NEWJERSEY 2010 2010
DC 2010 2010
DELAWARE 2011 2011
CONNECTICUT 2012 2012
MASSACHUSETTS 2012 2013
ILLINOIS 2013 2014
MARYLAND 2013 2013
NEWHAMPSHIRE 2013 2015(est.) All information on medical marijuana taken from NORML "NORML - National Organization For The Reform Of Marijuana Laws (n.d.). Legal Issues - Medical Marijuana - State Laws. Retrieved May 19, 2014, from norml.org/legal/medical-marijuana-2"
In 1996 California passed the Compassionate Use Act to allow sick residents the use of marijuana for its medicinal
properties. A caretaker could even cultivate the plant for the patient. California Proposition 215 distinguishes between
recreational and medical uses of marijuana15. Nowadays, 21 states and Washington DC have followed California16, in the
shared concept that medical marijuana users should not be penalized, with some minor differences.
Some diversion will most likely occur to recreational users. The conditions necessary in order to classify for a
medical marijuana permit “include but are not limited to: arthritis; cachexia; cancer; chronic pain; HIV or AIDS; epilepsy;
migraine; and multiple sclerosis.” (NORML’s website)17. Chronic pains, migraine, are diseases that cannot be
methodically tested for (Anderson et al. 2013). Cochran (2010)18 describes medical marijuana recommendations being
handed out via brief Skype conferences. In Venice Beach, California, these cards are offered on the boardwalk for 40 U$S
to any bystander. Needless to say, medical marijuana does not seem to be difficult to access.
15
All this information is in California Health & Safety Code 11362.5 Hussein (2001). 16
"21 Legal Medical Marijuana States and DC - Medical Marijuana - ProCon.org."ProConorg Headlines. Procon.org, 25 Apr. 2014. Web. 26 Apr. 2014. 17
"NORML.org - Working to Reform Marijuana Laws." California Medical Marijuana. http://norml.org/legal/item/california-medical-marijuana (accessed May 26, 2014). 18
As seen in Anderson et. al (2013)
7
C) Possible Mechanisms linking MMLs and crime
The effect the treatment will have on the marijuana market is important in understanding the mechanisms that
might be working to connect medical marijuana legalization and crime. From Pacula and Kilmer (2003) we discern four
main mechanisms to associate crime and the legality of the marijuana. The cause could be psychopharmacological,
economic-compulsive behavior, systematic violence or common factors.
The psychopharmacological explanation argues that the person who smokes marijuana becomes an offender
because of the acute psychoactive effects of marijuana. The second explanation would relate to the crime as a means to
finance addiction. This would be an economic-compulsive behavior. The systemic violence has to do with the black
market behind marijuana. Cartels, dealers and gangs generate crime to resolve turf conflicts. Profits fuel competition,
which in turns generates a hostile environment that has no government regulation, because of its original standing
outside legality. Finally, the “common factor” hypothesis suggests that there are exogenous characteristics that make an
individual more prone to commit crime, and, at the same time, more likely to try drugs. This theory is supported with
evidence by Gottfredson and Hirschi (1990). This is an example of a spurious correlation, in that factors associated with
drug use are, at the same time, associated to criminal activity.
Interesting points listed by Benson et al. are taken from Kaplan (1983). It is the illegality of drug use itself that
causes criminal activity19. When drug use is made illegal, several consequences occur: Prices go up, forcing users to
acquire more resources in order to fuel their consumption. Steady employment is difficult because of the time spent
finding a safe supply source and because of the arrests and harassment by police forces. Finally, tagging them as
criminals makes them forcibly have to deal with criminals.
In Table 1 we can see the years in which different states and Washington DC legalized marijuana for medicinal
purposes. The legalizations taken into account in this study are those in states that legalized in or before 2012. The
treatment can have a relationship with crime through several different mechanisms.
From Benson et al (1992) we can say that drug crime and drug enforcement are linked to other types of crime in
two major ways. The first is explained by the large amount of predatory crime committed by people who are drug users.
Since a large percentage of the people arrested use drugs, this leads people to understand that drug use must cause
crime. It is a common argument that this is how they finance their habit. The notion that drug users have to finance their
addictions is not very appropriate in marijuana use where addiction ranges 10%20. This incidence is below that of alcohol
(15%), nicotine (32%) or Opioids (23%)21.
As Benson et al. (1992) question very diligently throughout their paper, the first relationship between drug crime
and other types of crime is certainly a correlation but does not necessarily imply causation. Property criminals might use
drugs but this does not mean that drug users are committing crimes because of their drug use/abuse. Implying this is as
stating that, since criminals drink water, consumption of water might be a cause of crime.
The second potential linkage these authors posit is through the allocation of resources. Since resources are scarce,
legislation decides how these will be distributed amongst all types of crime. An increase in resources allocated to deter
19
Not drug related. 20
Welch & Martin (1999). As seen in Cohen (2009). 21
Anthony et al., supra note 104, at 254–55. As seen in Cohen (2009)
8
drug crime will, undoubtedly, reduce the amount of resources available to stop other types of crime. In an economic
model of crime, an increased amount of resources destined to stop one type of crime turns other types of crime more
attractive.
Another key conclusion in Benson et al. (1992) shows that an increase of 1% in the percentage of drug arrests of
the total division I crimes increases property crime rate by a 0.164. Drug arrestees might be property criminals.
Technically the increase of 1% in drug arrests over the total number of crime will reduce the positive effect on crime
through the size of the drug market by a 0.030%. For a subset (majority) of the population, drugs do not cause crime.
Drug arrests are not efficient at reducing property crime.
Another mechanism taken from the conclusions of Benson et al. is that since drug arrests cause prison
overcrowding, property criminals face less severe penalties. This is because there are not enough resources to allocate
to them in prison. When drug arrests are reduced, the deterrence of property crime comes from more severe penalties.
Since property crime is a “logical”22 type of crime, the analytical structured approach to criminal decision making put
forward by these authors shows that the incentives can be shifted. This mechanism does not seem strong enough to
cause an immediate reduction in crime, since criminals probably cannot perceive perfectly that penalties are less severe.
This could be part of the explanation as to why property crime is reduced in the long run. Benson et al. (1992) conclude
that drug enforcement is clearly not a positive-sum crime control policy.
Additional mechanisms, such as the link between alcohol and marijuana use, are discussed, by Anderson, Hansen
and Rees (2013). This paper suggests that marijuana and alcohol are substitutes. Not only this, but medical marijuana
laws account for a 8-11 % decrease in traffic fatalities after the first year of coming into effect. This suggests that medical
marijuana laws might be responsible, through a drop in alcohol consumption, for a reduction in crime. Another point
made in Anderson et al. is that marijuana consumption typically takes place at home. Alcohol is usually consumed at a
bar, a restaurant or even a sports game. Following Munyo and Rossi,23 we can argue the idea that, taking people in a
euphoric or frustrated state fueled by alcohol off the street, might be one mechanisms functioning towards reducing
violent crime. The amount of people on the streets decreases, and the state of irrationality brought on by alcohol is
replaced by marijuana use.
Considering the different ways that medical marijuana laws (MMLs) can influence crime, we can now entertain
the notion that MMLs actually have a causal effect on crime; we can now go into the model specified.
22
Logical in the sense that it doesn’t include emotional components such as violent crime might. 23
Munyo, Ignacio, and Martín A. Rossi. "Expectations and Crime: One Hour of Irrational Behavior?."
9
3. The Data
A) The Empirical Model
The purpose of this paper is to assess the effect of MMLs on crime rates. The data consists of a panel of
observations from the 50 US states and Washington D.C.,24 with information relating to crime reported during 1960-
2012. The data on reported crime by state was obtained from the Uniform Crime Reports (UCR) program online data
tool25. The data obtained from UCR is composed of an index and two main groups of crime: property and violent crime.
Property crimes are larceny theft, vehicle theft and burglary. Violent crimes are robbery, aggravated assault, forcible
rape and murder26.
The treatment that will be studied is the passing of Medical Marijuana Laws (MML). The treatment has been
passed as a law in 19 states from 1996 through 2012. More policy changes over the studied period would increase the
statistical power to detect a causal effect, as suggested by Harper et al. (2012). An increase in the time period observed
with more pre- and post- treatment years available will also help isolate the causal effect. Information on medical
marijuana laws was taken from the “National Organization For The Reform Of Marijuana Laws” (NORML) webpage
(norml.org/)
Different state-related characteristics might potentially confound the identification; this is because states that
approve MMLs might be primarily different to those who do not, rendering comparisons amongst these states useless. A
cross-sectional analysis assesses the “between units” variability. This type of analysis would not be able to differentiate
these effects from the impact of MMLs. These are time-invariant characteristics and can be controlled by using a
differences-in-differences model.
The differences-in-differences estimator also includes year fixed effects. This means that shocks to crime rates
that would have affected all states in the same way are accounted for. Time series analysis would have to assume that
no shock has impacted the dependent variable aside from the treatment. The “within units” variability cannot
differentiate exogenous shocks. All possible shocks would have to be accounted for as controls, in order to “earn”
causality.
The treated group includes the states that have passed the law, while the control group includes the states that
have not passed the law. The main assumption we have to make is that, if the treatment never existed, the control
group’s trend would be a good estimate of the counterfactual trend that treated states would have undergone, if no
treatment had been administered. This is a more reasonable assumption than that of independent time-series or cross-
sectional analysis. Under this specification, we are comparing within-state changes in crime before and after MMLs with
the same information for states without MMLs.
The model we are going to estimate can be represented by the following equation:
24
Robustness was checked not including Washington D.C. and results do not change significantly. 25
CJIS - Criminal Justice Information Services. "Welcome to a new way to access UCR statistics." Uniform Crime Reporting Statistics. http://www.ucrdatatool.gov/ (accessed May 21, 2014). 26 Specific definitions for UCR offenses will be found in the appendix.
10
Where can be any of the UCR data available for state in year (these
variables are per 100.000 people); is a dummy variable that takes the value one if the state has legalized
medical marijuana in year and zero if not; in equation number two is similar to but this variable
captures the effect over time, the variable takes value one on the year that MML is passed and 1+k for every k years
after the MML was passed; is the coefficient of interest which represents the average treatment effect; is a set of
control variables that vary across state and time; represents a fixed effect for each year; is a fixed effect for each
state, and is a state, year specific error term which is assumed to be independent across time and space. Having this
in mind, as panel data is used, the possibility of error terms being correlated across time in the same state exists. When
a positive correlation exists, the standard errors would be smaller than they really are, which would cause an over
rejection of the null hypothesis. To avoid this type of bias in the estimation of coefficients, standard errors are clustered
by state; this allows for an arbitrary covariance structure within states over time (Bertrand et. al:2004). If state error
terms are highly correlated, clustering at the state level might reduce the statistical power of our regressions27.
We have used the natural logarithm of the dependent variable as several papers suggest. This lets us linearize the
trend for our dependent variable and avoid outliers’ effects. For dummy variables we can use the exponential of the β
coefficient and subtract one. Trend variables are trickier to interpret, in this case we need to 100*(exp(β-Var(β)/2)-1.
This will give us the estimated growth rate. As suggested in Morris et al. (2006), post law trend variables suggest an
individual impact of the treatment. The trend variable approach captures any change in the linear trend of our
dependent variable that might be observed over time. The argument used in Morris et al. is that, if gateway theory is a
mechanism connecting crime and marijuana use, then the treatment will have an impact over time and not right away.
Young adults who fall prey of drug use because of starting with marijuana will need to fuel their drug use by committing
crime; this effect will be gradual and not instantaneous.
B) The identification strategy
The purpose of this paper is to identify the average effect of medical marijuana legalization on crime. This relies
on comparing trends in states which have legalized medical marijuana and those who have not. Since MMLs are not
passed as an experiment we cannot assume that MMLs have been passed at random in the different states.
Treatment exogeneity would certainly give our study a more robust causal interpretation. Laws are dictated by
representatives who look to benefit their electorate. This means that laws have some relation with public opinion28.
Representatives who do not follow public opinion will be removed from their charges because of the nature of the
democratic game. There is significant literature regarding how public opinion and the policymaking process influences
policy29. Nonetheless, the democratic system will probably have a distorting effect on how public opinion is reflected in
policies. This distortion happens through paternalism and the self-interest of representatives.
If something unobservable by our model relates to MMLs then we can be confounding the effect of our
treatment. The most appropriate confounding effect can be that of marijuana use, and marijuana approval. For this we
27
In the case that no significant relation is found, nothing can be done because the amount of states can’t be increased and the amount of groups already matches the amount of states. A N=51 might not be enough to achieve causality. 28
Another argument to be made relates to “The nature of belief systems…” (Converse, 1964) the author states the irrationality of public opinion. 29
(Page and Shapiro 1983)
11
can refer to results in Cerda, Wall, Keyes, Galea and Hasin (2012). The authors find that states which pass marijuana laws
have a higher prevalence of past year marijuana use compared to states which never pass marijuana laws (7.13% vs
3.57%). A study by Harper et al. carried out a replication using information on drug use from 2002-2009 (Cerda et al.
only used information until 2004) found similar results. States with marijuana laws have a higher prevalence of
marijuana use (8.88%) than states which had not passed MMLs (6.94%). States that eventually pass marijuana laws by
2011 had a similar prevalence to states with marijuana laws (8.58%). The effects of MMLs might not be the same for
states with higher and lower prevalence of marijuana use. The critical assumption that the control groups’ post-
treatment trend would be a good counterfactual of the treated groups’ would not be true if prevalence of past year
marijuana use dictated MMLs enactment and crime.
This is certainly a weakness, but none of the studies cited above find a causal effect between marijuana use and
MMLs. Harper et al. (2012) find that MMLs have no effect on marijuana use once unmeasured state characteristics are
accounted for. We can therefore still trust our identification because we are analyzing trends. If the trend in marijuana
use continues to be the same, before and after the treatment, we can still attribute causality to our coefficient. This
point will be visited again in the course of this paper.
In order to make our model more robust we control for several variables that might influence the probability of a
state passing a MML and crime in order to gain robustness and a better specification. We are trying to make the control
group as similar as the treated group in order to assess causal effects in a better way, this is why we want to purge the
error term of any omitted variables in order to avoid biased estimators.
The controls, represented by are three main groups, controls related to socio-economic aspects, geography,
and criminal justice. The economic controls include: employment per 100.000 people (taken from the Bureau of Labor
Statistics website, http://www.bls.gov/) and gross domestic product per capita (taken from the Bureau of Economic
Analysis website, http://www.bea.gov/). These variables represent the macroeconomic conditions; states with high
poverty and low employment tend to suffer greater crime rates. At the same time, these variables might be related to
the passing, or not, of MMLs. States with better macroeconomic conditions might be more proficient institutionally,
meaning congress will be willing to pass riskier laws because their institutions can manage the change properly.
Macroeconomic conditions might relate to education, which might mean more open minded-ness, and a higher rate of
approval of MMLs or, terrible macroeconomic conditions might cause a desperate government to recur to extraordinary
measures, in order to combat crime. The possibility of a relationship forces us to create the control in order to expunge
any possible connection between our dependent and independent variables that would end up in the error term.
12
Table 2 - Descriptive Statistics Crime and Control Variables
Variable Obs Mean Std. Dev. Min Max
Index 2697 4080.925 1658.065 650.8 12173.5
Violent 2697 398.1431 301.2008 9.5 2921.8
Property 2696 3681.986 1435.25 573.1 9512.1
Murder 2698 6.671846 6.185083 0.2 80.6
Forciblerape 2698 28.22683 15.63089 0.8 102.2
Robbery 2698 128.2438 150.7685 1.9 1635.1
aggravated assault 2698 235.4009 165.1922 3.6 1557.6
Burglary 2698 929.2108 444.4106 182.6 2906.7
larceny theft 2690 2393.731 920.0663 293.3 5833.8
vehicle theft 2698 358.3314 228.9381 48.3 1839.9
Employment per 100.000 2193 111235.4 1761906 35831.89 6.24E+07
Gross Domestic Product Per Capita 2550 0.0222677 0.0181501 0.0019719 0.1736344
Adults On Parole Per Capita 1907 0.0015251 0.0016356 0.0000157 0.0136701
Adults On Probation Per Capita 1821 0.0095794 0.0065353 0.0002469 0.0482991
Average Persons In Custody Per Capita* 2672 0.0022102 0.0017385 0.0002034 0.0179842
Total Police Employees 917 18562.09 22349.88 1108 123506
Number Of Sworn Police Officers 917 13021.06 15166.1 814 81286
Yearly Population Growth 2652 50506.05 87677.08 -273963 753915
Population 2703 4791959 5353412 229000 3.80E+07
Population/Square Kilometers 2703 0.1446611 0.5137167 0.000198 6.453943
Beer consumption per capita 900 22.6152 3.74241 12.05611 34.23819 Information on medical marijuana legality was taken from NORML. Information on employment, population and GDP was taken from the Bureau of economic analysis. Information on adults on probation, parole and people prison taken from the Bureau of Justice Statistics. All data on police personnel was taken from the "Crime in the US publications" by the FBI CJIS.All regressions include year and state fixed effects. * Of State Or Federal Prison
Geographic controls include yearly population growth, population, and population density. Population was
extracted from the Bureau of Economic Analysis website (http://www.bea.gov/) and population growth was a
construction from this data. Information on state surface area was taken from About.com
(http://geography.about.com/od/usmaps/a/states-area.htm) and helped construct the population density. Several
papers link population, population growth and population density, to crime. An interesting idea comes from population
density: In a more densely populated area, there is a bigger chance that someone observes crimes being carried out, and
reports them. Population growth can also affect crime because this means that criminals are less likely to stand out
because a lot of people are new, this was argued at the county level. It would be difficult to translate this argument to
the state level growth, but we can see how population variables can affect crime.
Also, the idea that these variables might relate to MML passing comes from the relationship that might exist
between population density and institutional capabilities or public opinion. The notion that the amount of people in a
state, population density or population growth, might affect public opinion in that state, is not irrational. States with
13
higher population growth might feel that legislation regarding birth control is a must, in the same way as states with
higher population density might want legislation regarding public use of marijuana. This generates the need to control
these variables.
Criminal justice variables include number of adults on probation per capita, adults on parole per capita, and
average prisoners in custody of federal and state prisons. These variables were obtained from the Bureau of Justice
Statistics website (http://www.bjs.gov/) specifically from the Annual Probation survey, the Annual Parole survey and the
survey of Inmates in State and Federal Correctional Facilities. Combined with these variables are variables such as total
sworn police officers per capita and total law enforcement employees’ per capita constructed by using the FBI’s crime in
the US publications that are available annually from 1994 to 2012 and are part of the UCR program. All these variables
relate by construction to crime, states with higher crime rates tend to have more prisoners and more police. The
relationship with MML can be through resource availability, states with a high number of police officers per capita might
feel more confident to pass MML and deal with the possible outcomes. States with very high numbers of prisoners might
be more willing to pass MML in order to empty their prisons and leave some space for other kinds of criminals. This
might in turn affect crime through more severe penalties for criminals since prison overcrowding might reduce
penalties30 the reverse effect might occur.
Beer consumption per capita was taken from the Beer Institute website (www.beerinstitute.org) Brewers Almanac
of 2013 would be difficult to put in one of these groups. Authors who want to assess the impact of crime always tend to
control for alcohol consumption. The connection exists between MMLs and alcohol consumption as we can see in
Anderson et al. (2013).
A more exhaustive approach can be made towards the choice of control variables, but time pressures and
expertise limitations make these choices adequate and justified. The literature on the subject supports our decision.
Papers such as Anderson et al. and Morris et al. use similar controls. These are common in panel data analysis of law
impact in the US.
30
As suggested by Benson et al (1992).
14
C) Main Results
Table 3 - Enacting Medical Marijuana Law and its effect on main UCR crime categoriesⁱⁱⁱ
lnindex Lnviolent lnproperty
MML Enactment Post-Law Trendⁱⁱ MML Enactment Post-Law Trendⁱⁱ MML Enactment Post-Law Trendⁱⁱ
(1) (2) (1) (2) (1) (2) (1) (2) (1) (2) (1) (2)
MMLⁱ -0.2*** -0.04* -0.02*** -0.01* -0.08 0.03 -0.01 -0.0004 -0.2*** -0.04** -0.02*** -0.007**
{0.055} {0.02} {0.007} {0.004} {0.081} {0.037} {0.011} {0.009} {0.057} {0.02} {0.007} {0.004}
Impact -18.13% -3.79% -2.32% -0.67% -7.75% 3.09% -1.18% -0.05% -18.05% -3.96% -2.32% -0.75%
Obs. 2,696 853 2,696 853 2,696 852 2,696 852 2,695 851 2,695 851
R-Sq 0.875 0.891 0.874 0.892 0.921 0.932 0.921 0.932 0.878 0.944 0.877 0.945
Note.—All standard errors are in curly brackets clustered at the state level. * Statistically different from zero at the .1 level. ** Statistically
different from zero at the .05 level*** Statistically different from zero at the .01 level. Regression types: (1) Include no controls. (2)Include
controls for employment per 100.000 people, Gross Domestic Product per capita, number of police personnel per capita, number of adults on
parole per capita, number of adults on probation per capita, number of people in custody of federal or state prison per capita, population,
population density per square kilometer, yearly population growth and beer consumption per capita. Information on medical marijuana legality
was taken from NORML. All regressions include year and state fixed effects. ⁱ MML represents the effect of marijuana being legalized.ⁱⁱ Post law
trend is represented in the model by a variable that takes value 1 on the year that MML is enacted and the value 1+t for every t year after the
law was passed. ⁱⁱⁱ All crime variables are natural logarithms of the actual crime rate per 100.000 people. All analysis was conducted with Stata
(Version 11), and standard errors for the model were clustered at the state level. Results were tested for robustness by dropping Washington
DC, no significant change was found. Coefficients rounded to 1 significant figure. Standard errors rounded to 3 decimal places.
Table 3 shows how MML enactment affects index crime, which is an aggregate of the two major crime
groups, property and violent. MML affects index crime negatively and significantly for both models, violent
crime is reduced but not significantly and property crime falls significantly. The impact row shows the
percentage change pertaining to the coefficient of that regression. The post law trend coefficient shows the
percentage change in the dependent variable for every year where the treatment is active. When controls are
added these percentages significantly fall; this means that we have purged the error term of omitted variables
that were confounding our coefficient. We can observe in table 3 that MMLs decrease Index I crime, in average,
by a 3.79% in states that pass MMLs relative to those who do not. This percentage is very different to the
18.13% change when the model is estimated without controls. Index I falls at a 0.67% annual decline rate,
meaning that in ten years index I crime would experience a 6.5% fall in states who pass MML relative to those
who do not. When confounding variables are controlled for property crime falls at a 0.75% every year after
marijuana law has passed. This means that 10 years after MMLs are enacted we can expect a 7.2%31 drop in
property crime. This value should be taken cautiously because no information regarding the elasticity of the
change is available. The classical dummy approach shows that MMLs are responsible for a 3.96% fall in crime;
this dummy assumes that change occurs at one point in time while trend variables can detect gradual changes of
the treatment. Both effects are statistically different from zero at the .05 level.
31
We compound the annual growth rate by ((1+annual growth rate) ^ number of years)-1.
15
The first thing to see in this graph is how the trends of the groups before the treatment are very similar.
Eventually treated group’s Index Crime per 100.000 people changes its trend when the first MML passes in 1996
(represented by the blue line), the trend changes gradually, to finally match the group that never has been
treated. Of the 19 subjects passing the law in the time period studied, 8 did before 2000 and 11 from 2004 to
2012.
We can either argue that the treatment has a once and for all effect and the gradual impact is from the
gradual adoption of the law, that the impact of the law is gradual and strong enough to change the trend with
the amount of subjects in the study or both. The results in table 3 show a strong econometrical support for both
explanations. The graph (C1) does not need to have a sudden fall in crime in order to show how the once and for
all can be significant. This is because the graph is an average of states that legalized in different time periods.
Either one of the effects could be at place
.
1000
2000
3000
4000
5000
6000
7000
Index Crime - C1
Never Treated Eventually Treated
1000
2000
3000
4000
5000
6000
7000
Property Crime - C2
Never Treated Eventually Treated
16
From graphs C3 and C2 we can observe that property crime is causing the fall in index crime. Both types of
crime follow similar trends before the treatment, but violent crime fails to change its trend after the treatment.
Property crime, on the other hand, changes its trend to meet the never treated group’s trend after the
treatment. Our results are consistent with those presented by Benson et al. (1992). The authors analyze drug
enforcement policies and its effect on crime. Their results suggest that more severe drug enforcement policies
cause a surge in property crime. Benson et al. (1992) generate a model with different controls that would effect
a criminal decision to commit crime or increase the supply of offenses, as Benson et al. refer to it in their model.
In order to gain a better understanding of how property crime changes, we can observe the separate
types of crime within property crime.
Table 4 - Enacting Medical Marijuana Law and its effect on Property Crime componentsⁱⁱⁱ
lnburglary Lnlarcenytheft lnvehicletheft
MML Enactment Post-Law Trendⁱⁱ MML Enactment Post-Law Trendⁱⁱ MML Enactment Post-Law Trendⁱⁱ
(1) (2) (1) (2) (1) (2) (1) (2) (1) (2) (1) (2)
MMLⁱ -0.2*** -0.04 -0.03*** -0.01** -0.2*** -0.05** -0.03*** -0.008** -0.05 0.04 -0.0001 0.007
{0.064} {0.026} {0.008} {0.005} {0.066} {0.02} {0.008} {0.004} {0.099} {0.062} {0.012} {0.006}
Impact -21.02% -4.17% -2.82% -0.67% -22.40% -5.19% -2.67% -0.81% -4.88% -4.15% -0.02% 0.68%
Obs. 2,697 853 2,697 853 2,689 846 2,689 846 2,697 853 2,697 853
R-Sq 0.857 0.942 0.856 0.943 0.878 0.932 0.877 0.933 0.829 0.947 0.829 0.947
Note.—All standard errors are in curly brackets clustered at the state level. * Statistically different from zero at the .1 level. ** Statistically different from zero at the .05 level*** Statistically different from zero at the .01 level. Regression types: (1) Include no controls. (2)Include controls for employment per 100.000 people, Gross Domestic Product per capita, number of police personnel per capita, number of adults on parole per capita, number of adults on probation per capita, number of people in custody of federal or state prison per capita, population, population density per square kilometer, yearly population growth and beer consumption per capita. Information on medical marijuana legality was taken from NORMLⁱ MML represents the effect of marijuana being legalized.ⁱⁱ Post law trend is represented in the model by a variable that takes value 1 on the year that MML is enacted and the value 1+t for every t year after the law was passed. ⁱⁱⁱ All crime variables are natural logarithms of the actual crime rate per 100.000 people. All analysis was conducted with Stata (Version 11), and standard errors for the model were clustered at the state level. Results were tested for robustness by dropping Washington DC, no significant change was found. Coefficients rounded to 1 significant figure. Standard errors rounded to 3 decimal places.
0
100
200
300
400
500
600
700
800
Violent Crime - C3
Never Treated Eventually Treated
17
Table 4 shows a significant effect of MMLs on burglary. States that legalize medical marijuana enjoy a
significant, at the .05 level, 0.67% fall in crime with the post-law trend variable approach. By compounding this
average growth rate, we find that MMLs generate a 6.5% fall in burglary in the decade after MMLs have been
enacted relative to never treated states. The dummy variable model does not perceive a significant change.
Larceny theft, on the other hand, shows a relatively significant drop for both kinds of models (statistically
different from zero at the .05) level. A 5.19% drop with the dummy approach and a 0.81% decline rate in larceny
theft (-7.8% compounded in a decade). Vehicle theft is negative, but no significant effect can be found.
There is a precedent for the connection between property crime and drug enforcement policy. Benson et
al. (1992) make an interesting case for linking efforts to combat drug crime and the allocation of police
resources. The argument is that, by reallocating police resources towards pursuing drug crime, there is a
reduced deterrence towards property crime and, therefore, an increase in this type of crime. In Benson et al.,
auto theft crime is not taken into account. This is because vehicle theft does not behave in the same way as
other crimes. In Florida, a large portion of vehicle thefts are for “joy rides”. This means that the argument that
users commit crime in order to finance their activities will not be diligently evaluated though this type of crime.
Interestingly enough, this is the only type of property crime that does not show a significant and consistent
coefficient in the model specified in the current paper. Vehicle theft does not seem to follow the model of
economic decision making in crime.
To understand this approach towards crime, we can observe what Gary Becker, Nobel laureate, says in his
renowned paper, “Crime and Punishment: An Economic Approach”.
“The approach taken here follows the economists´ usual analysis of choice and
assumes that a person commits an offense if the expected utility to him exceeds
the utility he could get by using his time and other resources at other activities.
Some persons become “criminals,” therefore, not because their basic motivation
differs from that of other persons, but because their benefits and costs differ. I
cannot pause to discuss the many general implications of this approach, except
to remark that criminal behavior becomes part of much more general theory and
does not require ad hoc concepts of differential association, anomie, and the
like, nor does it assume perfect knowledge, lightning-fast calculation, or any of
the other caricatures of economic theory.
This approach implies that there is a function relating the number of offenses
by any person to his probability of conviction, to his punishment if convicted, and
to other variables, such as the income available to him in legal and other illegal
activities, the frequency of nuisance arrests, and his willingness to commit and
illegal act.” From Becker’s “Crime and Punishment…”32
The results Benson et al. obtain suggests that criminals, at least while perpetrating property crimes, act
rationally. They proxy the probability of arrest and the probability of conviction, and both have a negative
32
BECKER, Gary S. Crime and punishment: An economic approach. En Essays in the Economics of Crime and Punishment.UMI, 1974. p. 1-54.
18
impact on the supply of property offences. The probability of arrest is affected by community characteristics and
police resources. In order to model the probability of arrest, they use the number of police officers, reports of all
other types of crime, socio economic controls and a proxy that represents resource allocation. Their results
suggest that when more resources are directed to drug arrests, the probability of arrest for property crime
decreases and rational criminals commit more crimes. Taking their results into account, we could predict that a
more relaxed drug enforcement policy will cause resources to shift towards other types of crime and our results
would suggest this is what happens33.
Fortunately, we can construct the same proxy used in Benson et al. (1992), in order to assess if resources
are actually shifting away from drug criminals to property criminals. This will be developed further on in the next
section.
33
Another argument is made to link drug arrests and crime in Benson et. al (1992). If drug users are over proportionately criminals then drug arrests will very likely reduce the amount of criminals. When observing Florida, of the 45906 drug related arrests recorded during 1987 80% had never been arrested for burglary and 90% hadn’t been arrested for any other propertycrime. This leads us to understand that there is two subsets of drug users, those who commit other type of crimes and the overwhelming majority that are only convicted on drug related charges. This idea is reinforced by Kim et al (1990) who show that of the 50% of drug related arrestees that are re-incarcerated or put in probation only 31% are because of non-drug related crimes. Also, the recidivism rate for arrestees who only had drug arrests was significantly lower than that of arrestees with other types of crime as well as drug related crime. This means that arresting and convicting drug users will not have a large effect on the number of property offenders, at least a relevant one. What Benson et. al suggests is that the shifting of police resources to capture drug users is very inefficient at reducing property crime.
19
4. Medical Marijuana laws and law enforcement resource allocation
A) Arrests and resource allocation
We want to assess if the impact MMLs has is through a change in law enforcement resource
allocation. To do this we construct a variable that represents the amount of resources that are destined
to drug related policing activities. Arrests are accountable for a large amount of the use of law
enforcement resources. One arrest takes up to 9 hours of work, including paperwork. This approach
towards resource allocation modelling was taken from Benson et al. (1992). The proxy to represent
resource allocation in its most basic form will be
Since index I crimes are severe, the definitions of them do not vary across states and this gives us
a homogeneous proxy for our regressions. The other classes of arrests (curfew and loitering, runaways,
suspicion, etc.) often have different definitions across different states; this subjective component makes
them a less robust way of estimating resource allocation.
Information on drug arrests was taken from table 69 of the different “Crime in the US” annual
publications. This table gives us arrest data, disaggregated by type, state and two age groups (under 18
and total). This information is available for the 1995-2012 years. This limits our analysis of long run
pretreatment trends, but we can still appreciate the years where all MMLs were enacted. This reduces
the power of our analysis; a connection is going to be harder to demonstrate.
The relationship between MMLs and our proxy can be because of several reasons. Firstly,
marijuana arrests ceteris paribus should decrease, the crime definition is now less strict34. Secondly, we
do not know how MMLs would affect recreational marijuana use and what this effect might cause on
drug arrests. Since laws generally reflect public opinion, a passing of marijuana law might mean police
are less disapproving of marijuana users, and therefore prosecute all marijuana related crimes less. If
recreational marijuana use increases arrests more than the amount that these have gone down because
of the legality of medical marijuana use, then we would observe more drug arrests. Lastly, any change in
drug arrests will, ceteris paribus, affect Index I arrests because of the primal notion that resources are
limited. If this is the case, we can then argue that the effect MMLs have on crime is, at least partly,
through this mechanism.
There are several mechanisms working but all of them become changes in resource allocation. An
increased amount of recreational marijuana users would generate more arrests; this can be offset by a
growing approval of marijuana use which might cause police to willingly prosecute these users less. Also,
the decision not to prosecute could be to maximize efficiency since now marijuana users might be very
likely to carry medical marijuana licenses and searching them could be considered a waste of time.
Another effect could be that actually recreational users are going down because they are all falling into
34
This is actually partly confirmed in table 9.
20
legality through medical marijuana licenses. These effects are very likely to interact, it would be very
difficult to measure each one separately and, alternatively, we can measure the effect they all have on
resource allocation.
To estimate a trustworthy model we need to control for variables that might affect both the
dependent variable and our treatment, in order to isolate the effect of MMLs
on .
First, we used the natural logarithm of index crime reported. This control will avoid the effect of
arrests because of changes in crime levels. MMLs, theoretically, affect crime. If the mechanism at work
is a reallocation of resources, then we want to observe the direct impact of MMLs on our resource
allocation proxy; not through the actual crime changes, but through the change in politics, public
opinion, legal marijuana users, etc. We want to isolate the cause for a decrease in drug abuse violation
arrests relative to Index I arrests.
We also generate a control for all other arrests. Resources are limited, if more prostitution arrests
are occurring, the amount of policing resources available to deter drug and Index I criminals will
decrease. Since we cannot assume the effect is the same on both types of arrests that compose
we have to control for this variable. We construct this control variable by
simply subtracting Index I crime arrests and drug abuse violations arrest from the total of all classes
arrest. We should consider the importance of this variable since, on average, Index I and drug arrests
account for only about 30% of total arrests.
We also control for the classic control variables that might relate to crime; these are specified in
the different tables’ notes.
Table 5 - Enacting Medical Marijuana Law and its effect on law enforcement resource allocationⁱⁱⁱ
Drug Arrests / Index I Arrests
MML Enactment Post-Law Trendⁱⁱ
(1) (2) (1) (2)
MMLⁱ -0.06** -0.05* -0.005 -0.007*
{0.025} {0.029} {0.003} {0.004}
Impact -5.38% -5.17% -0.49% -0.65%
Obs. 883 823 883 823 R-Sq 0.827 0.845 0.825 0.844 Note.—All standard errors are in curly brackets clustered at the state level. * Statistically different from zero at the .1 level. ** Statistically different from zero at the .05 level.*** Statistically different from zero at the .01 level. (1) Include no controls. (2) Include controls for employment per 100.000 people, Gross Domestic Product per capita, Number of police personnel per capita, Number of people in custody of federal or state prisons, Population, Population Density per square kilometer, Yearly population growth, beer consumption per capita and the natural logarithm of Index per 100.000 people. Information on medical marijuana legality was taken from NORML. Another variable represents all other arrests, total arrests subtracting index I arrests and drug abuse violations. All regressions include year and state fixed effects. ⁱ MML represents the effect of marijuana being legalized.ⁱⁱ Post law trend is represented in the model by a variable that takes value 1 on the year that MML is enacted and the value 1+t for every t year after the law was passed. ⁱⁱⁱ Index I crimes are those deemed serious enough (murder, forcible rape, vehicle theft, larceny theft, arson, aggravated assault, robbery). Results were tested for robustness by dropping Washington DC and Alabama (2011-2012), no significant change was found. Coefficients rounded to 1 significant figure. Standard errors rounded to 3 decimal places. Coefficients rounded to 1 significant figure. Standard errors rounded to 3 decimal places.
21
As Benson et. al predict a more relaxed drug policy shifts resources towards Index I crimes. This
effect is robust to our controls (statistically different from zero at the .1 level) and is discernible in the
long run. The immediate effect on allocation of resources could be the legislative effort and its impact
on how police departments manage their resources. This could mean that after medical marijuana law is
passed, police departments make a conscious decision to stop pursuing marijuana users and therefore,
drug arrests are reduced.
This effect is carried on in time, not only strictly medical marijuana users will access medical
marijuana licenses, which means that by definition a percentage of recreational marijuana users will
now be in compliance with state laws. This effect is probably discernible in the long run; it would mean a
-0.65% annual fall35 the relationship between drug arrests and index I arrests in states that pass MMLs
relative to those who do not pass MMLs. State registries take time to set up and then start running, at
the same time citizens who actually need medical marijuana might doubt the system at first, because of
the controversy of the matter. Doctors will certainly take time to grow used to prescribing medical
marijuana. These cultural changes aren´t as clear cut as maybe police decisions to change resource
allocation might be, while reacting to a new law. Changes in approval and public opinion will affect
resource allocation in the short run and in the longer run. Therefore, we can assess that criminals are
taking a conscious rational decision to stop committing crimes, at least partly, because police presence is
greater, and more concentrated on non-drug related crime.
35
-6.3% if compounded for 10 years.
22
B) Further Analysis
We have argued that a change in resources could be a part of the fall in crime brought on by
MML. To generate further analysis on the matter we could look at the different components of crime
through resource allocation. Though this approach has its weaknesses, since we are interpreting that
drug arrests are the opportunity cost of both property crime and violent arrest in the separate
regressions, by controlling for all other types of arrests we can try to observe, less rigorously, the effect
of MML on resource allocation towards separate crimes. Resources are probably deviated from drug
arrests to all other types of crime and not just a certain type of crime. But, since different types of
crimes have different properties, we could find that resources allocate themselves towards the more
notable crimes first.
Table 6 - Enacting Medical Marijuana Law and its effect on law enforcement resource allocationⁱⁱⁱ
Drug Arrests / Property Crime Arrests Drug Arrests/ Violent Crime Arrests
MML Enactment Post-Law Trendⁱⁱ MML Enactment Post-Law Trendⁱⁱ
(1) (2) (1) (2) (1) (2) (1) (2)
MMLⁱ -0.07** -0.07* -0.001 -0.005 -0.5 -0.5 -0.08** -0.1*
{0.029} {0.038} {0.004} {0.006} {0.279} {0.300} {0.039} {0.052}
Impact -6.67% -6.89% -0.69% -0.46% -36.11% -37.19% -7.98% -9.56%
Obs. 883 820 883 820 880 821 880 821
R-Sq 0.829 0.856 0.827 0.854 0.173 0.179 0.174 0.179
Note.—All standard errors are in curly brackets clustered at the state level. * Statistically different from zero at the .1 level. ** Statistically different from zero at the .05 level.*** Statistically different from zero at the .01 level. (1) Include no controls. (2) Include controls for employment per 100.000 people, Gross Domestic Product per capita, Number of police personnel per capita, Number of people in custody of federal or state prisons, Population, Population Density per square kilometer, Yearly population growth, beer consumption per capita and the natural logarithm of Index per 100.000 people. Information on medical marijuana legality was taken from NORML. Another variable represents all other arrests, total arrests subtracting index I arrests and drug abuse violations. Within Index I another variable represents the other categories arrests (violent when assessing property and so on) Drug arrests/ property crime arrests is controlled additionally with the natural logarithm of property crimes reported per 100000 and violent crime arrests. Drug arrests/violent crime arrests are controlled with the natural logarithm of violent crimes reported per 100000 people and property crime arrests. All regressions include year and state fixed effects. ⁱ MML represents the effect of marijuana being legalized.ⁱⁱ Post law trend is represented in the model by a variable that takes value 1 on the year that MML is enacted and the value 1+t for every t year after the law was passed. ⁱⁱⁱ Index 1 crimes are those deemed serious enough (murder, forcible rape, vehicle theft, larceny theft, arson, aggravated assault, robbery). Results were tested for robustness by dropping Washington DC and Alabama (2011-2012), no significant change was found. Coefficients rounded to 1 significant figure. Standard errors rounded to 3 decimal places.
In Table 6 the coefficient of the dummy variable, which captures the once and for all effect, is
significant when tested with property arrests. It is hard to assess the results in table 6 quantitatively
since we are controlling for so many of the other possible allocations for resources. The relationship
between drug arrests and property arrests falls a 7%, we are seeing either an increase in property
arrests, a fall of drug arrests or both. This significance is lost in the trend variable. This suggests that,
when considering property arrests, resources shift all at once. The possibility of police deterrence of
crime suffering from the law of diminishing marginal utility might explain the non-significance of the
longer run analysis as well.
23
When considering allocation from violent crimes, the effect of drug arrests is better discerned
with the trend variable. The r squared that shows the amount of the variability represented by the
model suggests that a lot of the variability is not accounted for. One possible explanation is that R-Sq is
smaller in violent crime because it is harder to model the irrational component in violent crime (in
average, 0.84 R-sq vs. 0.18 R-sq).
Logically, the change in resources is felt in property crime first. Property crimes are four times
more frequent than violent crimes, and they are mostly committed outdoors. Violent crimes are less
common and very often committed indoors (rape, domestic violence, murder). This is a possible
explanation for the delayed effect of MMLs on resources allocated towards violent crime. Resources
simply shift to property crime first because these types of crime are the easiest to detect and the police,
as the criminals, seem to follow an economic model of decision making, they want to maximize their
utility.
When assessing our main results we can take into consideration that property crime follows a
clear economic thinking (financial gain is the main motivator). Violent crime, on the other hand, has an
irrational component fueled by the emotional nature of this kind of crime. This means that property
crime is more easily deterred by police presence. If resources shift in a longer period of time and violent
crime is more irrational than property crime when responding to police presence, it is logical to find a
lack of significant effect of MMLs on violent crimes.
The resource shift in violent crime should be interpreted with caution. This is because the R
squared in the model for allocation of resources in violent crimes is too small. We are only accounting
for 0.174 of the variation. This means that even though we specified a model with all the usual variables,
no conclusive intuition can be made. The irrational components of violent crime might explain the
difficulty of modeling the entire variation with traditional control variables.
24
C) Drug policy and age
We have analyzed the different components of reported property crime, but we have failed to
analyze the effects of MMLs on the various components of violent crime. We can observe the effect of
MMLs on certain components of violent crime in the following table.
Table 7 - Enacting Medical Marijuana Law and its effect on certain Violent Crime componentsⁱⁱⁱ
Lnrobbery Lnmurder
MML Enactment Post-Law Trendⁱⁱ MML Enactment Post-Law Trendⁱⁱ
(1) (2) (1) (2) (1) (2) (1) (2)
MMLⁱ -0.2* -0.03 -0.02 -0.001 -0.1* -0.07* -0.02** -0.02**
{0.08} {0.047} {0.011} {0.008} {0.063} {0.043} {0.008} {0.007}
Impact -13.58% -2.93% -1.53% -0.12% -11.13% -6.98% -1.97% -1.85%
Obs. 2,697 853 2,697 853 2,697 853 2,697 853 R-Sq 0.923 0.980 0.923 0.980 0.874 0.920 0.875 0.921
Note.—All standard errors are in curly brackets clustered at the state level. * Statistically different from zero at the .1 level. ** Statistically different from zero at the .05 level.*** Statistically different from zero at the .01 level. Regression types: (1) Include no controls. (2)Include controls for employment per 100.000 people, Gross Domestic Product per capita, number of police personnel per capita, number of adults on parole per capita, number of adults on probation per capita, number of people in custody of federal or state prison per capita, population, population density per square kilometer, yearly population growth and beer consumption per capita. Information on medical marijuana legality was taken from NORML. Information on employment, population and GDP was taken from the Bureau of economic analysis. Information on adults on probation, parole and people prison taken from the Bureau of Justice Statistics. All data on police personnel was taken from the "Crime in the US publications" by the FBI CJIS. All regressions include year and state fixed effects. Beer consumption information was taken from the Brewers Almanac from 2013. ⁱ MML represents the effect of marijuana being legalized.ⁱⁱ Post law trend is represented in the model by a variable that takes value 1 on the year that MML is enacted and the value 1+t for every t year after the law was passed. ⁱⁱⁱ All crime variables are natural logarithms of the actual crime rate per 100.000 people. All analysis were conducted with Stata (Version 11), and standard errors for the model were clustered at the state level. Results were tested for robustness by dropping Washington DC, no significant change was found. Coefficients rounded to 1 significant figure. Standard errors rounded to 3 decimal places.
Table 7 includes robbery and murder. Aggravated assault and forcible rape show a negative, but
not significant, correlation. Therefore, they were omitted from the table, but not from the discussion.
The amount of robbery offenses reported also shows a negative coefficient once the controlled model is
applied but no significant effect. Murder, on the other hand, shows significant results, especially with
the trend variable. The different allocation of resources might be affecting murder and no other violent
crimes, but why is this? How does murder differ from other violent crimes?
In order to reason this question, we can take a look at murders reported summary statistics in
Table 2. While violent crimes have a mean of 398 per 100.000 people, murders account for 6.67 of this
mean. This means that, on average, murders account for 1.6% of violent crime. This gives us an idea of
why this effect might not be noted. Murder arrest data is available, and even disaggregated by two age
groups, under and over 18 years of age.
25
Table 8ⁱ - Arrest data used to suggest resource allocation change.
Age Group Variable Obs Mean Std. Dev. Min Max
Under 18
Violent Crime 880 1617.255 2769.974 0 21266
Murder 856 24.52453 43.47523 0 520
Forcible Rape 879 65.89534 83.04401 0 490
Robbery 877 526.6978 1056.785 0 10719
Aggravated Assault 883 1006.169 1678.803 0 12132
Property Crime 883 7914.648 10175.72 28 78205
Burglary 883 1498.521 2584.39 0 22111
Larceny Theft 883 5617.67 6473.269 7 42822
Motor Vehicle Theft 883 662.6512 1191.855 0 13462
Drug Arrests per 100.000 884 389.0457 177.2131 6.2452 1054.75
Over 18
Violent Crime 880 8570.575 16811.46 15 129966
Murder 856 208.5643 300.1831 0 2276
Forcible 879 352.6348 450.4924 0 2664
Robbery 877 1490.649 2441.421 1 17910
Aggravated Assault 883 6525.864 13863.14 4 109857
Property Crime 883 20146.13 24818.79 29 171889
Burglary 883 3566.651 6018.512 0 42798
Larceny Theft 883 14910.12 16588.34 23 97308
Motor Vehicle Theft 883 1459.335 2820.919 2 24987
Drug arrests per 100.000 883 339.271 159.7773 5.68 942.78 ⁱAll data was obtained from the FBI "Crime in the US" publications, starting in 1995 to 2001. ⁱ Over 18 data was constructed by subtracting under 18 data provided by the publications and the totals, this data was not readily available and therefore is a construction. Note: We need to address the fact that an alarmingly high number of arrests for the age group under 18 have a minimum value of 0. This is because of a problem of reporting with Alabama in 2011 and 2012 and DC. Tests to check for the robustness of the results dropping these two subjects were made and no significant change in results was seen.
Of the total 10.187 violent crime arrests, murder represents a 2.2% of these arrests36. While of
the total violent crimes, 18.8% are committed by people under the age of 18, in average, only 11.7% of
the murder crimes are committed by underage people. With this information we can now start to
suspect that MMLs do not affect all age groups in the same way. The first test we can do is to see how
drug arrests per 100.000 people change once MMLs are enacted.
36
These numbers are means across states and across time, which would represent the average of all states for the time period studied.
26
Table 9 - Enacting Medical Marijuana Law and its effect on drug arrests for different age groupsⁱⁱⁱ
Drug Arrests per 100.000 people (under 18) Drug Arrests per 100.000 people (over 18)
MML Enactment Post-Law Trendⁱⁱ MML Enactment Post-Law Trendⁱⁱ
(1) (2) (1) (2) (1) (2) (1) (2)
MMLⁱ 0.4 3.9 -0.02 0.7* -12.1 -23.1* -4.1 -3.2
{3.83} {2.64} {0.459} {0.431} {18.30} {12.65} {3.238} {2.517}
Obs. 882 822 882 822 882 822 882 822
R-Sq 0.723 0.897 0.723 0.898 0.699 0.920 0.700 0.920
Note.—All standard errors are in curly brackets clustered at the state level. * Statistically different from zero at the .1 level. ** Statistically different from zero at the .05 level.*** Statistically different from zero at the .01 level. (1) Include no controls. (2) Include controls for employment per 100.000 people, Gross Domestic Product per capita, Number of police personnel per capita, Number of people in custody of federal or state prisons, Population, Population Density per square kilometer, Yearly population growth, beer consumption per capita and the natural logarithm of Index per 100.000 people. Includes the arrest data for "all other arrests" except drug arrests and Index I arrests. Over 18 include Index I arrests for people under 18 as a control and under 18 includes Index I arrests for people over 18 as a control. Information on medical marijuana legality was taken from NORML. Another variable represents all other arrests, total arrests subtracting index I arrests and drug abuse violations. Drug arrests/ property crime arrests is controlled additionally with natural logarithm of property crimes reported per 100000 and violent crime arrests. Drug arrests/violent crime arrests are controlled with the natural logarithm of violent crimes reported per 100000 people and property crime arrests. Controls also include all other kinds of arrests beside the arrest that is part of the dependent variable. All regressions include year and state fixed effects. ⁱ MML represents the effect of marijuana being legalized.ⁱⁱ Post law trend is represented in the model by a variable that takes value 1 on the year that MML is enacted and the value 1+t for every t year after the law was passed. ⁱⁱⁱ Index 1 crimes are those deemed serious enough (murder, forcible rape, vehicle theft, larceny theft, arson, aggravated assault, robbery). Results were tested for robustness by dropping Washington DC, no significant change was found. Coefficients rounded to 1 significant figure. Standard errors rounded to 3 decimal places.
MML enactment generates a positive and significant effect on drug arrests for people under 18.
The trend variable suggests that, of the 390 mean drug arrests per 100.000, MMLs cause an increase of
0.7 arrests per year when the law has been passed. This is a very small effect, and can help us conclude
that MMLs have almost no effect on drug arrests for people under 18. Drug arrests for people over 18
works differently, and a fall of 23 arrests per 100.000 is significant accounting for a 7% of the mean
number of drug arrests for people over 18. All different specifications maintain the negative sign on our
coefficient.
Our results so far suggest that MMLs work differently for adults than minors. This could be
because of several reasons. Primarily, older people are more likely to suffer from chronic pains and
discomforts that might be cured by the use of medical marijuana. This means that its more probable for
medical marijuana users to be adults. These users used to commit a crime in order to treat their ailment
but not after MMLs have passed and they have a license to use medical marijuana.
Minors, on the other hand, would have to act through their parents, even if health was equal
having to receive parent approval would reduce the amount of underage medical marijuana users. This
means that the primal effect of MML on the amount of illegal marijuana users will be greater in the over
age group. Physicians might also be reticent to provide minors of a means to use such a controversial
drug. Anderson et Al.37 (2013) provides proof of this, taking into account 128.698 registered patients of
an average age of 40 years. In Montana, there are 30.036 patients. The average age is 41 years old and
37
Specifically table A1 in the appendix. Data from 2011.
27
48% of the registry is in between 18-40 years old. This suggests that a large portion of the remaining
50% is 40 years or more.
The other effect could be that though approval of marijuana use has gone up, this has not been
the case for underage use. Police might willingly prosecute under age people more concerning drug use.
An adult that looks as a suspect of marijuana possession might not even be searched, in the case of a
suspicious minor, police would still search this person.
Di Tella and Schargrodsky (2004) estimate the deterrence effect of police by exploiting the
random allocation of police forces after a terrorist attack. Their results show a fall in crime in the vicinity
of new police locations. This leads us to think that police deterrence might also work at a state level
considering that police presence, in average, will be greater everywhere. Police no longer spend time
arresting drug criminals and now spend this time roaming the streets. An interesting analysis would
observe how MMLs affect drug arrests. In order to control for the crime level, we will add the opposite
age group arrests as a control for general rises in arrest levels.
Table 10 - Enacting Medical Marijuana Law and its effect law enforcement resource allocation in different age groupsⁱⁱⁱ
Drug Arrests /Index I Crime Arrests (Under 18) Drug Arrests/ Index Crime I Arrests (Over 18)
MML Enactment Post-Law Trendⁱⁱ MML Enactment Post-Law Trendⁱⁱ
(1) (2) (1) (2) (1) (2) (1) (2)
MMLⁱ -0.001 0.001 0.005* 0.004 -0.07* -0.08* -0.008* -0.01**
{0.013} {0.012} {0.002} {0.002} {0.03} {0.04} {0.004} {0.004}
Impact -0.05% 0.97% 0.47% 0.34% 6.63% 7.46% -0.77% -1.06%
Obs. 882 822 882 822 882 822 882 822
R-Sq 0.777 0.823 0.781 0.824 0.800 0.818 0.800 0.818
Note.—All standard errors are in curly brackets clustered at the state level. * Statistically different from zero at the .1 level. ** Statistically different from zero at the .05 level.*** Statistically different from zero at the .01 level. (1) Include no controls. (2) Include controls for employment per 100.000 people, Gross Domestic Product per capita, Number of police personnel per capita, Number of people in custody of federal or state prisons, Population, Population Density per square kilometer, Yearly population growth, beer consumption per capita and the natural logarithm of Index per 100.000 people. Includes the arrest data for "all other arrests" except drug arrests and Index I arrests. Over 18 include Index I arrests for people under 18 as a control and under 18 includes Index I arrests for people over 18 as a control. Information on medical marijuana legality was taken from NORML. Another variable represents all other arrests, total arrests subtracting index I arrests and drug abuse violations. Drug arrests/ property crime arrests is controlled additionally with natural logarithm of property crimes reported per 100000 and violent crime arrests. Drug arrests/violent crime arrests are controlled with the natural logarithm of violent crimes reported per 100000 people and property crime arrests. All regressions include year and state fixed effects. ⁱ MML represents the effect of marijuana being legalized.ⁱⁱ Post law trend is represented in the model by a variable that takes value 1 on the year that MML is enacted and the value 1+t for every t year after the law was passed. ⁱⁱⁱ Index I crimes are those deemed serious enough (murder, forcible rape, vehicle theft, larceny theft, arson, aggravated assault, robbery). Results were tested for robustness by dropping Washington DC and Alabama (2011-2012), no significant change was found. Coefficients rounded to 1 significant figure. Standard errors rounded to 3 decimal places.
When controlled for, the under 18 resource allocation proxy does not change significantly and has
a positive sign in both controlled specifications. The over 18 resource allocation proxy does change
significantly (statistically different from zero at the .1 and .05 levels respectively) when controlled and
maintains a negative sign. The impact that MMLs has on our resource allocation shows that, in the once
28
and for all approach, legalization of medical marijuana generates a 7.46% change in allocation of
resources towards Index I arrests. In the trend variable approach, this effect is of a -1.06% annual
change38. We see that arrests have gone down but not crime Index I arrests.
Summing up, we have seen that violent crime does not vary significantly with MML enactment,
but that murder does vary significantly. The reason this effect is not when testing violent crime is that
murder represents an insignificant part of the total violent crime, close to a 2%. Murder does not have
the same characteristics as the other types of violent crimes. Though an 18.68% of violent crimes are
committed by people under the age of 18, in the case of murder this number goes down to 11.7%. At
the same time resources seem to be allocated towards deterring crime amongst people over 18 years of
age, but the same effect is not present with people under the age of 1839.
If this reasoning is correct, then the crimes with most proportions of people over the age of 18
committing them will be more likely to be reduced significantly.
If we go back, we can return to the argument that motor vehicle theft works irrationally because
of the “joy ride” component. When observing the mean of arrests for vehicle theft, 45% of these arrests
are of people under the age of 18. This is the highest proportion of crime arrests of underage people.
This might very well be a reason for explaining the lack of significance of MML on motor vehicle theft.
D) Enactment and effect – Final robustness check
Even after all our results, we could argue that states with higher approval rates of marijuana
eventually legalize because of this fact; the change in resource allocation is only because of public
opinion, policy is not playing a role. This approach would mean that the drop in crime is unrelated to
MMLs and has to do with social and cultural factors that are represented by the MMLs. If marijuana
approval didn’t affect crime noticeably before our treatment, we wouldn’t be controlling for this effect.
By observing MML enactment, we would not be interpreting the effect of MMLs but changes in
community approval brought on by more permissive laws. This variable could be a main cause for both
the changes in resource allocation and marijuana law.
The time a law takes to go into effect varies from state to state. Different organizational and
bureaucratic changes have to be made. In the case of MMLs, this change refers mainly to the setting up
of the registry system and legal dispensaries. These results suggest that two mechanisms make
resources shift towards non drug related crime. In the case of our study, 4 states have different years for
law enactment and when the law actually took effect. This gives us the opportunity to assess the two
separate components in policy making, the first one is in the short run. This could be a shift in resources
because of legislation, any impact that would clearly cause change in resource allocation. The other
change is more gradual. This change is captured by trend variables. This could be the same short run
38
-14.9% compounded for 10 years. 39
Another effect that might make police deterrence rates different for different age groups, as noted in Benson et al (1992) is that younger criminals face lighter sentences because of their appeal, also the opportunity cost of time lost can is less for minors who have a longer period of time to live.
29
causes functioning in the long run or the mechanical part of the law where fewer users are committing a
crime. Table A1 in the appendix shows the results of estimating the same model but with MML effective
date instead of enactment. When assessing the impact of effective MMLs, most components remain
negative but lose their significance when using the once and for all dummy approach. Interestingly
enough, trend variables that capture the longer run trend changes remain significant. The results can be
seen in Table A1.
Property crime falls in a 0.75% annual decline in treated states compared to never treated states.
Burglary comprises a -0.81% relative decline in treated states and larceny theft suffers a -1.07% every
year after MML is passed. Both effects are statistically different from 0 at the .05 level.
There are only 4 states with different years for when the law takes effect and when the law was
enacted. This leads us to believe there is a once and for all mechanism related to law enactment and
another mechanism which works in a more gradual way. One possible explanation for our results is that
the first effect of MMLs is on public opinion and approval of marijuana users. Police stop prosecuting
marijuana users because of how MML enactment modifies a state’s general view on marijuana use. This
is supported by our main findings in Table 3. A logical approach is to suggest that when the law is
enacted, public opinion shifts in an almost discontinuous way. The endorsement of marijuana use by the
medical community might be one of the main reasons for a change in the general view on marijuana.
This, in turn, will cause the shift in resources.
In the longer run, there is an effect on the systemic violence brought forth by black markets. Drug
dealers lose an important source of income; this has an effect on gang activity. There is also a
mechanical component; marijuana users leave the state of illegality. Logically, this effect would be
better appreciated as a gradual change. Marijuana users will take time to obtain medical marijuana
licenses. A very possible explanation is that because of the fear of social condemnation or distrust
towards the authorities, state residents avoid obtaining licenses. This means that they are still subject to
prosecution. When, with time, these users start trusting the system they obtain marijuana licenses and
this decreases the resources spent on controlling drug users40. Another, very plausible explanation is
that MMLs exacerbate an already existing trend. Marijuana approval is increasing, once marijuana law is
enacted this approval suffers a discontinuous jump and a steeper trend upward in approval is
generated. Both explanations suggest that MMLs are related to crime through resource allocation; that
is, our mechanism seems to be a plausible explanation to explain the fall in crime in medical marijuana
legalizing states
40
This would delay the effect of MMLs on crime, our results show several components of crime which happen to suffer a change in trend as well as a more sudden fall in crime.
30
5. Conclusion
Summing up our results, we have shown that a viable causal link may exist between MMLs and
crime. These results are robust to interpretation with several control variables. After this, we have
analyzed one of the possible mechanisms that could be working to generate this relationship. Our
results suggest that this mechanism is a feasible option to explain how MMLs can affect crime. This
mechanism not only explains the drop in crime, but, by separating the effect of MMLs on resource
allocated by age groups, it also explains why certain components in crime behave differently to others.
Finally, we have separated the different impacts MMLs have because of their effect on public opinion
and their mechanical impact on marijuana legality.
We have shown that MML, when several control variables are specified, has an effect on certain
types of crime. Index I crime, which is the aggregate of all crimes, suffers a 3.79% fall in crime in states
that legalize medical marijuana against those who do not. When observing trend variables, the effect of
MML is a -0.67% relative decline in index I crime. The main culprits of this change are property crimes.
Larceny experiences a 5.19% fall in occurrences in treated states relative to never treated states. If we
observe the trend variable, a -0.81% would translate to a -7.8% change after 10 years of medical
marijuana legality. Burglary would experience a 6.5% fall in a 10 year period, in states that legalize
relative to those who do not. Murder, on the other hand, experiences a 7% fall in those states that enact
MML ceteris paribus. This effect can also be seen with a trend variable where a 17% fall in murders
reported is at place, in a 10 year period, relative to states that never pass MML. Our results suggest that
any increase in other crimes caused by less drug criminals arrested, is offset by an increased
concentration of efforts to reduce non drug related crime.
We recognize the limitations in expertise and time to further pursue this subject, but we are still
willing to accept that challenges to irrational and emotional interpretation of drug policies effects on
society have been made. We need to recognize the weaknesses in this study. The specification of the
model can never be perfect; the probability of omitted variables confounding our coefficients exists. The
underreporting of crimes might play a role when no relation is found. The limited post treatment period
studied will also definitely affect the power and the reliability of our coefficients. Our study assumes
that the implementation of Medical Marijuana Laws is similar across states. This is not necessarily true,
appropriate medical conditions for treatment could be different in different states. Definitions in law
can be very subjective. At the same time law enforcement may vary from state to state. Federal
intervention might interfere with MML effects, but we have decided to oversee this component,
because federal intervention is not significant in comparison to the effect state laws have, especially
because regular police follow state laws.
There is lots of different ways to achieve further robustness. A more in depth study of marijuana
approval rates could explain qualitatively the change in resource allocation we have observed. The
analysis of state registries and the medical marijuana license holder’s characteristics could also be of
use. Once the time period is expanded enough, the complete legalization could be added as another
treatment to be taken into account. This will theoretically help prove which mechanism is working. A
similar analysis in the not so distant future might provide more power to interpret results as our
31
timeline of legalizations is fairly limited, and some of our legalizers fall in the last few years. It would also
be interesting to observe how different kinds of arrests change, possession, sale etc. when the
treatment is administered. This would paint a clearer picture of what is happening.
With the recent legalizations of recreational marijuana, and the growing rates of approval of
medical marijuana,41 further work on the subject is likely to appear. This study intended to further the
analysis of the existing literature. A mechanism has been soundly supported by the data; we can say we
have approached causality but in no way proved it. If anything, this study proves that the notion that
drug enforcement policy is the best way to combat crime has several flaws.
41
legalization including recreational marijuana is a reality in Colorado and Washington, and 70 percent of Americans are in
favor of making marijuana available for doctors to prescribe in order to reduce patient suffering Mendes (2010) as seen in
Anderson et al (2013).
32
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34
Appendix
Table A1 - Effective Medical Marijuana Law and its effect on Property Crime componentsⁱⁱⁱ
lnproperty lnburglary Lnlarcenytheft
MML Enactment Post-Law Trendⁱⁱ MML Enactment Post-Law Trendⁱⁱ MML Enactment Post-Law Trendⁱⁱ
(1) (2) (1) (2) (1) (2) (1) (2) (1) (2) (1) (2)
MMLⁱ -0.2*** -0.03 -0.023*** -0.008* -0.2*** -0.03 -0.03*** -0.01** -0.2*** -0.03 -0.03*** -0.01**
{0.058} {0.019} {0.007} {0.004} {0.065} {0.026} {0.008} {0.01} {0.065} {0.026} {0.008} {0.005}
Impact -17.55% -3.05% -2.32% -0.75% -20.63% -3.08% -2.84% -0.81% -20.63% -3.08% -2.84% -1.07%
Obs. 2,695 851 2,695 851 2,697 853 2,697 853 2,697 853 2,697 853
R-Sq 0.878 0.943 0.877 0.945 0.857 0.942 0.856 0.944 0.857 0.942 0.856 0.944
Note.—All standard errors are in curly brackets clustered at the state level. * Statistically different from zero at the .1 level. ** Statistically different from zero at the .05 level.*** Statistically different from zero at the .01 level. Regression types: (1) Include no controls. (2)Include controls for employment per 100.000 people, Gross Domestic Product per capita, number of police personnel per capita, number of adults on parole per capita, number of adults on probation per capita, number of people in custody of federal or state prison per capita, population, population density per square kilometer, yearly population growth and beer consumption per capita. Information on medical marijuana legality was taken from NORML. All regressions include year and state fixed effects. ⁱ MML represents the effect of marijuana being legalized.ⁱⁱ Post law trend is represented in the model by a variable that takes value 1 on the year that MML is enacted and the value 1+t for every t year after the law was passed. ⁱⁱⁱ All crime variables are natural logarithms of the actual crime rate per 100.000 people. All analysis were conducted with Stata (Version 11), and standard errors for the model were clustered at the state level. Results were tested for robustness by dropping Washington DC, no significant change was found. Coefficients rounded to 1 significant figure. Standard errors rounded to 3 decimal places.
Table A2ⁱ - Arrest data used to suggest resource allocation change.
Variable Obs Mean Std. Dev. Min Max
Total Arrests All Classes 882 223695 274196 1918 1594841
Crime Index I Arrests 884 38180 52597 140 392717
Property Crime Arrests 884 28037 34431 59 242565
Violent Crime Arrests 881 10177 19467 15 129966
Drug Related Arrests 884 26838 42971 68 305745 ⁱAll data was obtained from the FBI "Crime in the US" publications, starting in 1995 to 2001. Allocation proxies are constructions from the aforementioned data, these proxies intend to represent resource allocation but are in no way published or acknowledged by any legal authorities. ⁱ Over 18 data was constructed by substracting under 18 data provided by the publications and the totals, this data was not readily available and therefore is a construction.
35
0
2
4
6
8
10
12
Murder Crime - C7
Never Treated Eventually Treated
800
1300
1800
2300
2800
3300
3800
Larceny Theft Crime - C5
Never Treated Eventually Treated
300
500
700
900
1100
1300
1500
1700
1900
Burglary Crime - C4
Never Treated Eventually Treated
0
125
250
375
500
625
750
Vehicle Theft Crime - C6
Never Treated Eventually Treated
36
Table B(1) - Summary Statistics per 100.000 habitants
Propertyⁱ Burglaryⁱ Larceny Theftⁱ Vehicle Theftⁱ
State Mean
Std. Dev. Min Max
Mean
Std. Dev. Min Max
Mean
Std. Dev. Min Max
Mean
Std. Dev. Min Max
ALASKA 4034
1234.506
1540.6
5979.1 812
309.2458
332.1
1383.3
2746
844.3387 950
3958.7 471
158.4601
185.8
919.9
ALABAMA 3287
1097.219
985.5
4521.4 951
277.2645
339.3
1526.7
2077
802.1738
569.4
2954.8 255
80.48646
76.8
365.8
ARKANSAS 3135
1063.651
926.4
4581.7 898
276.7074
302.2
1226.5
2039
739.1732
568.2
3013.8 198
78.77261
50.3
341.4
ARIZONA 5596
1479.591 2806
7793.7
1391
510.384
685.5 2534
3616
957.1108
1782.2
5106.1 594
256.8628
287.3
1157.7
CALIFORNIA 4737
1371.965
2583.8
6939.5
1327 562.08
610.4
2316.5
2755
728.5883
1583.8 3880 655
189.3036
332.7
1038.9
COLORADO 4531
1459.001
2035.1
6821.4
1112
504.8953
503.6
2031.6
3006
951.8416
1251.4
4429.3 411
102.186
213.8
589.3
CONNECTICUT
3408
1212.349
1119.9
5469.2 883
401.4399
333.4
1700.6
2095
701.823
656.9
3113.9 430
179.3302
129.7
795.7
DELAWARE 4067
1165.865 2040
6301.8 988
334.3336
591.9
1826.3
2722
814.7735
1284.9
4216.2 360
113.6095
156.6
584.8
FLORIDA 5295
1645.363
2243.3
7819.9
1512
540.5801
720.5
2506.8
3316
991.8146 1353
4760.6 468
208.5132
168.3
911.5
GEORGIA 3906
1386.932
1249.1
6337.3
1090
376.1058
392.1
1712.5
2424
976.3649
709.6
3971.1 392
142.3777
147.4
673.6
HAWAII 4935
1302.963
2276.5
7182.8
1156
394.6504
525.9
1937.7
3326
966.0386
1480.6
5046.9 463
140.076
241.2
798.8
IOWA 2961
933.2923
1057.6
4546.4 655
216.6297
228.7
1079.5
2137
689.3417
759.1
3221.7 169
44.29322
69.8
269.7
IDAHO 3079
830.3934
1732.8
4468.8 695
254.5539
301.4
1238.9
2218
559.2586
1318.6
3057.6 166
47.57792
84.1
271.8
ILLINOIS 3802
1050.274
1976.8
5461.1 900
306.4953
541.3
1559.6
2435
671.1495
1045.7
3317.5 470
132.9922
199.5 676
INDIANA 3423
774.4436
1469.1
4578.6 849
214.9577
460.8
1376.4
2230
552.2582
840.7
2870.6 344
80.14571
167.6
465.7
KANSAS 3685
1011.52
1336.6
5034.9 919
323.2407
331.6
1544.2
2526
683.4951
895.8
3377.1 240
64.37897
85.7 351
KENTUCKY 2488
560.4612
1105.4
3253.4 707
167.2194
366.4
1062.4
1564
408.4211
619.8
1992.4 217
54.76757
108.3
346.3
LOUISIANA 3957
1361.861
1120.7
5909.7
1052
327.0583
327.1
1523.9
2515
962.5704
614.4
3982.3 380
132.643
169.6
631.6
MASSACHUSETTS
3413
1217.896
1170.3
5635.3 915
416.6821
309.2
1740.4
1817
548.9168
650.1
2685.8 681
356.6105
138.9
1571.1
MARYLAND 4093
1095.115
1518.8
5777.7 983
337.0545
366.4 1698
2612
715.2149
968.3
3629.2 501
153.6293
184.1 763
MAINE 2756
867.6939
1081.1
4174.2 723
293.6401
278.8
1361.5
1890
602.3303
705.8
2772.3 143
46.93344
74.5
253.3
MICHIGAN 4299
1208.804
2441.5
6212.2
1136
411.5431
635.7
1903.9
2637
700.4436
1536.9
3714.9 526
177.231
176.3
865.9
MINNESOTA 3338
859.2542
1408.2
4571.7 778
261.4917
370.4
1277.6
2278
613.9898
892.5
3029.9 281
76.80255
134.6
384.8
MISSOURI 3826
744.646
1799.7 4904 999
281.0817
595.2
1668.9
2424
521.7232
1019.7
3151.1 404
102.7523
167.9
578.5
MISSISSIPPI 2640
1171.209
573.1
4343.3 856
356.9614 186
1331.9
1604
760.3789 338
2864.8 188
100.9429
48.3 405
All crime data was obtained from the UCR Data tool online.
37
Table B(2) - Summary Statistics per 100.000 habitants
Propertyⁱ Burglaryⁱ Larceny Theftⁱ Vehicle Theftⁱ
State Mean
Std. Dev. Min Max
Mean
Std. Dev. Min Max
Mean
Std. Dev. Min Max
Mean
Std. Dev. Min Max
MONTANA
3418
896.9101
1928.8
4841.7 598
193.4801
310.7 966
2579
744.8299
1289.6
3833.8 232
42.59137
146.1
321.7
NORTHCAROLINA
3487
1273.976 956
5230.3
1085
375.9922
279.4
1692.3
2179
845.9102
589.6
3264.9 223
76.15383 86.9
340.2
NORTHDAKOTA
2133
592.0156
868.3
2923.6 353
85.53433
189.6
539.2
1648
499.3322
597.2
2288.8 132
33.11088 68
190.1
NEBRASKA 3137
868.5785
1177.8
4162.5 600
163.8656
231.5
915.4
2295
700.5873
820.9
3179.2 242
75.25493
115.6
384.2
NEWHAMPSHIRE
2443
969.9598
676.4
4499.8 569 278.96
182.6
1312.9
1707
678.9253 436 2915 167
69.96069 57.8
324.9
NEWJERSEY 3453
1230.047
1376.5 5797 897
394.3165
423.6
1878.2
2052
717.6831 755
3189.3 505
215.2916
176.8
939.5
NEWMEXICO 4552
1170.765
2062.4
6053.2
1271
371.5823
443.2
1844.7
2898
761.7548
1383.2
3929.5 364
100.5705 221
717.2
NEVADA 5049
1470.467
2560.5 7996
1455
617.9098
742.3
2906.7
2976
886.0356 1470
4717.4 619
170.1466
348.3
1116.4
NEWYORK 3784
1277.87
1912.4 5882
1030
520.5245
321.2
2061.6
2225
587.9748
1401.8
3133.8 530
269.8853 88.6
1042.7
OHIO 3544
906.8537
1475.2
4950.8 903
257.8761
383.9
1493.6
2291
606.656
950.2
3040.1 350
105.3808 122
500.4
OKLAHOMA 3844
1107.801
1847.1 5608
1131
359.7512
513.2
1786.8
2328
674.9056 1122
3317.4 372
124.9249
190.4
649.2
OREGON 4657
1375.056
1907.5
6558.2
1122
503.165
404.4 1967
3186
884.0333
1371.2
4250.8 387
136.8383
130.7
705.2
PENNSYLVANIA
2434
644.6919
949.6
3372.4 612
200.9154
297.8
1038.5
1516
456.2976
512.1
2079.9 306
105.1886
117.7
505.5
RHODEISLAND
3735
1093.402
1833.3
5524.1 945
371.9113
454.1
1716.3
2202 539.67
1128.1
3051.3 589
248.2701
217.5
1016.8
SOUTHCAROLINA
3847
1167.233
1356.5
5217.2
1125
335.0944
423.1
1714.2
2431
842.3985
824.1 3505 291
88.87717
109.2
428.5
SOUTHDAKOTA
2165
568.4579
1065.2
3116.3 457
122.5538
239.9
692.5
1596
446.6538
715.7
2377.4 113
23.44776 73.7 174
TENNESSEE 3435
1090.976
1149.4
4722.1
1040
276.9638
461.5
1501.5
2017
766.5485
559.7
2921.8 378
147.399
121.5
648.5
TEXAS 4552
1417.036
1970.5
7365.1
1242
455.7824
573.5
2157.2
2854
848.5257
1233.2
4407.9 457
196.8701
156.8
944.3
UTAH 4256
1030.873
2047.9 5762 802
240.5027
388.4
1321.9
3175
833.2201 1482
4572.1 279
62.87199
158.8
444.1
VIRGINIA 3109
798.0365
1469.2
4349.1 686
252.4057
360.5
1202.6
2192
590.1689
949.3
3112.5 230
61.09944
110.9
338.6
VERMONT 2775
1150.539
796.7 5115 770
333.176
242.1
1577.9
1859
819.1205 377 3200 146
60.71756 69.5
337.1
DC 6232
1897.427
2159.2
9512.1
1499
667.6459
556.5
2873.7
3727
1219.644 1257
5833.8
1005
442.3924
262.2
1839.9
WASHINGTON
4797
1240.571
2173.2
6646.6
1193
439.1288
471.9
1904.5
3191
821.0131
1530.1
4358.1 412
153.9753 158
783.3
WISCONSIN 3094
916.1067
1113.7 4616 609
220.737
198.6
1079.4
2247
659.6068
810.6
3291.4 238
78.62628 85.8
436.4
WESTVIRGINIA
1928
634.4691
599.2
2632.6 533
149.4746
216.4
775.1
1246
455.9868
293.3
1797.2 149
45.29109 68.3 216
WYOMING 3247
829.3469
1564.7
4701.8 587
175.3099
284.4 961
2489
649.5885
1144.4
3449.6 171
61.98858 91.7
363.3
All crime data was obtained from the UCR Data tool online.
38
Offense Definitions taken from Crime in the United States, 2012. U.S.Department of Justice—Federal Bureau of Investigation (Released Fall 2013)42 The Uniform Crime Reporting (UCR) Program divides offenses into two groups, Part I and Part II crimes.
Each month, participating law enforcement agencies submit information on the number of Part I
offenses that become known to them; those offenses cleared by arrest or exceptional means; and the
age, sex, and race of persons arrested for each of the offenses. Contributors provide only arrest data for
Part II offenses.
The UCR Program collects data about Part I offenses in order to measure the level and scope of crime
occurring throughout the nation. The program’s founders chose these offenses because they are serious
crimes, they occur with regularity in all areas of the country, and they are likely to be reported to police.
The Part I offenses are:
Criminal homicide―a.) Murder and nonnegligent manslaughter: the willful (nonnegligent) killing of one
human being by another. Deaths caused by negligence, attempts to kill, assaults to kill, suicides, and
accidental deaths are excluded. The program classifies justifiable homicides separately and limits the
definition to: (1) the killing of a felon by a law enforcement officer in the line of duty; or (2) the killing of
a felon, during the commission of a felony, by a private citizen. b.) Manslaughter by negligence: the
killing of another person through gross negligence. Deaths of persons due to their own negligence,
accidental deaths not resulting from gross negligence, and traffic fatalities are not included in the
category Manslaughter by Negligence.
Forcible rape―The carnal knowledge of a female forcibly and against her will. Rapes by force and
attempts or assaults to rape, regardless of the age of the victim, are included. Statutory offenses (no
force used―victim under age of consent) are excluded.
Robbery―The taking or attempting to take anything of value from the care, custody, or control of a
person or persons by force or threat of force or violence and/or by putting the victim in fear.
Aggravated assault―An unlawful attack by one person upon another for the purpose of inflicting severe
or aggravated bodily injury. This type of assault usually is accompanied by the use of a weapon or by
means likely to produce death or great bodily harm. Simple assaults are excluded.
Burglary (breaking or entering)―The unlawful entry of a structure to commit a felony or a theft.
Attempted forcible entry is included.
Larceny-theft (except motor vehicle theft)―The unlawful taking, carrying, leading, or riding away of
property from the possession or constructive possession of another. Examples are thefts of bicycles,
motor vehicle parts and accessories, shoplifting, pocket-picking, or the stealing of any property or article
that is not taken by force and violence or by fraud. Attempted larcenies are included. Embezzlement,
confidence games, forgery, check fraud, etc., are excluded.
42
"FBI" FBI. http://www.fbi.gov/about-us/cjis/ucr/crime-in-the-u.s/2013/preliminary-semiannual-uniform-crime-report-january-june-2013 (accessed May 28, 2014).
39
Motor vehicle theft―The theft or attempted theft of a motor vehicle. A motor vehicle is self-propelled
and runs on land surface and not on rails. Motorboats, construction equipment, airplanes, and farming
equipment are specifically excluded from this category.
Arson―Any willful or malicious burning or attempt to burn, with or without intent to defraud, a dwelling
house, public building, motor vehicle or aircraft, personal property of another, etc.
The Part II offenses, for which only arrest data are collected, are:
Other assaults (simple)―Assaults and attempted assaults where no weapon was used or no serious or
aggravated injury resulted to the victim. Stalking, intimidation, coercion, and hazing are included.
Forgery and counterfeiting―The altering, copying, or imitating of something, without authority or right,
with the intent to deceive or defraud by passing the copy or thing altered or imitated as that which is
original or genuine; or the selling, buying, or possession of an altered, copied, or imitated thing with the
intent to deceive or defraud. Attempts are included.
Fraud―The intentional perversion of the truth for the purpose of inducing another person or other
entity in reliance upon it to part with something of value or to surrender a legal right. Fraudulent
conversion and obtaining of money or property by false pretenses. Confidence games and bad checks,
except forgeries and counterfeiting, are included.
Embezzlement―The unlawful misappropriation or misapplication by an offender to his/her own use or
purpose of money, property, or some other thing of value entrusted to his/her care, custody, or control.
Stolen property: buying, receiving, possessing―Buying, receiving, possessing, selling, concealing, or
transporting any property with the knowledge that it has been unlawfully taken, as by burglary,
embezzlement, fraud, larceny, robbery, etc. Attempts are included.
Vandalism―To willfully or maliciously destroy, injure, disfigure, or deface any public or private property,
real or personal, without the consent of the owner or person having custody or control by cutting,
tearing, breaking, marking, painting, drawing, covering with filth, or any other such means as may be
specified by local law. Attempts are included.
Weapons: carrying, possessing, etc.―The violation of laws or ordinances prohibiting the manufacture,
sale, purchase, transportation, possession, concealment, or use of firearms, cutting instruments,
explosives, incendiary devices, or other deadly weapons. Attempts are included.
Prostitution and commercialized vice―The unlawful promotion of or participation in sexual activities for
profit, including attempts. To solicit customers or transport persons for prostitution purposes; to own,
manage, or operate a dwelling or other establishment for the purpose of providing a place where
prostitution is performed; or to otherwise assist or promote prostitution.
Sex offenses (except forcible rape, prostitution, and commercialized vice)—Offenses against chastity,
common decency, morals, and the like. Incest, indecent exposure, and statutory rape are included.
Attempts are included.
Drug abuse violations―The violation of laws prohibiting the production, distribution, and/or use of
certain controlled substances. The unlawful cultivation, manufacture, distribution, sale, purchase, use,
40
possession, transportation, or importation of any controlled drug or narcotic substance. Arrests for
violations of state and local laws, specifically those relating to the unlawful possession, sale, use,
growing, manufacturing, and making of narcotic drugs. The following drug categories are specified:
opium or cocaine and their derivatives (morphine, heroin, codeine); marijuana; synthetic
narcotics―manufactured narcotics that can cause true addiction (demerol, methadone); and dangerous
nonnarcotic drugs (barbiturates, benzedrine).
Gambling―To unlawfully bet or wager money or something else of value; assist, promote, or operate a
game of chance for money or some other stake; possess or transmit wagering information;
manufacture, sell, purchase, possess, or transport gambling equipment, devices, or goods; or tamper
with the outcome of a sporting event or contest to gain a gambling advantage.
Offenses against the family and children―Unlawful nonviolent acts by a family member (or legal
guardian) that threaten the physical, mental, or economic well-being or morals of another family
member and that are not classifiable as other offenses, such as Assault or Sex Offenses. Attempts are
included.
Driving under the influence―Driving or operating a motor vehicle or common carrier while mentally or
physically impaired as the result of consuming an alcoholic beverage or using a drug or narcotic.
Liquor laws―The violation of state or local laws or ordinances prohibiting the manufacture, sale,
purchase, transportation, possession, or use of alcoholic beverages, not including driving under the
influence and drunkenness. Federal violations are excluded.
Drunkenness―To drink alcoholic beverages to the extent that one’s mental faculties and physical
coordination are substantially impaired. Driving under the influence is excluded.
Disorderly conduct―Any behavior that tends to disturb the public peace or decorum, scandalize the
community, or shock the public sense of morality.
Vagrancy―The violation of a court order, regulation, ordinance, or law requiring the withdrawal of
persons from the streets or other specified areas; prohibiting persons from remaining in an area or place
in an idle or aimless manner; or prohibiting persons from going from place to place without visible
means of support.
All other offenses―All violations of state or local laws not specifically identified as Part I or Part II
offenses, except traffic violations.
Suspicion―Arrested for no specific offense and released without formal charges being placed.
Curfew and loitering laws (persons under age 18)―Violations by juveniles of local curfew or loitering
ordinances.
41
Map of the US showing US cannabis laws.
State with legal medical cannabis.
State with decriminalized cannabis possession laws.*
State with both medical and decriminalization laws.
State with legalized cannabis.
Source: http://en.wikipedia.org/wiki/File:Map-of-US-state-cannabis-laws.png (accessed May 28, 2014).
