1. health and fitness
2. addiction

The Price of Cannabis:
An analysis of how decriminalization and Federal enforcement
affect the price of marijuana
By: Lucas Husted
Advisor: Brian Knight
Abstract
In mid-2011, two separate events occurred that disrupted the market equilibrium in the
price of marijuana. In California, Federal agents began a crackdown on medical marijuana
dispensaries that lasted the course of approximately one year. In Connecticut, the state voted to
decriminalize small-scale possession of marijuana for personal use. This paper measures the
significance of these disruptions on the price of marijuana, applying traditional estimation
techniques to a large crowd-sourced database of end user-supplied marijuana transactions. It
finds that decriminalization of marijuana lowers the price at which it is sold in the black market.
In addition, it finds that efforts to curb supply through the elimination of medical marijuana
dispensaries have no meaningful impact on the equilibrium price in the market, regardless of
whether users purchase the product in medical shops or from black market sources.
I. Introduction
On Tuesday, November 6th, 2012 Colorado and Washington voted to legalize the
possession of cannabis for personal use, choosing to regulate and tax it like alcohol or cigarettes.
Though the future of cannabis — or marijuana* as it is commonly called — is uncertain, this is
the most significant change in its regulation since the Federal government first declared it illegal
in 1937. To this day cannabis remains Federally illegal despite being the most commonly used,
and culturally accepted, illicit drug in the United States (NIDA).
Colorado and Washington are just two examples of states that have adopted marijuana
policy that seems to be enacted in contempt of Federal law; though state policies differ
significantly, sixteen other states and the District of Columbia have medical marijuana laws that
allow patients to get access to various forms of cannabis. Several others have decriminalized
personal use of the drug. Yet marijuana remains illegal under Federal law and according to the
Office of National Drug Control, “Enforcing Federal law against significant traffickers in illegal
drugs including marijuana remains a core Department of Justice priority” (ONDC).
Social, cultural, and medical reasons notwithstanding, it is clear that a great deal of
impetus behind changes in marijuana laws revolves around balancing budgets. Some states, like
Massachusetts, decriminalize the drug to save money on enforcement efforts. Others, like
California, legalize medical marijuana and gain tax revenue, estimated at $100 million for the
medical industry there alone (BBC, 2012). Caputo and Olstrom (1994) estimated that in 1991 the
potential tax revenue from legalizing personal use of marijuana in the United States ranged from
$2.5 billion to $9 billion dollars.
This paper looks at two significant disruptions in marijuana market equilibrium. The first
involves one such state effort to reform marijuana policy through decriminalization. On July 1st
*
This paper uses the street names “marijuana” and “pot” interchangeably with cannabis, the proper medical name.
2 2011, Connecticut reduced the penalty for possession of up to .5 ounces of marijuana from a
potential year in jail and $1,000 fine to a non-criminal fine, much like a parking ticket.
Connecticut Governor P. Malloy called it “common sense reforms,” allowing police to focus on
violent crime (Press Release, June 2011).
Unfortunately for states looking to set their own policy, Federal law trumps state law, and
the Federal government has the power to enforce cannabis possession laws as it sees fit. No
matter one’s status as a state-authorized patient, grower, or seller of marijuana, one can be
arrested and charged for possessing any amount of the substance (see section III for more details
about Federal policy). The second case study thus focuses on this increasing tension between
state and Federal policy with regards to medical programs: on October 7th, 2011 in California,
the Department of Justice launched one of the largest crackdown on medical marijuana
dispensaries ever attempted. Over the course of approximately one year, at least 600 dispensaries
operating within state law were forced to shut down (Onishi 2012a).
Though these cases will be estimated slightly differently, for each I ask the same
question: what is the effect of changing regulation on the black market supply of marijuana
holding other factors constant? For California, does the price increase, as we expect, given a
significant supply-shock, accompanied by a consumer shift from the medical to black market?
For Connecticut, does the price decrease, as we expect, given lower legal penalties?
This paper advances the literature in several ways. Firstly, it illustrates an example of
estimation strategies within a black market, in this case focusing on a drug that is currently in the
public spotlight. Black markets are difficult to study, for obvious reasons, and the market for
marijuana is particularly interesting given its size and “grey market” status, where people who
run legitimate medical shops operate alongside extensive drug trafficking networks. Secondly, it
3 illustrates the potential of individually crowd-sourced numerical data in economic research,
made possible by the advent of the Internet. These data are particularly interesting because they
are far larger and specific than most data previously used to study illicit drug markets.
II. Motivation
There have been various studies on the effects of regulation on the price and availability
of controlled substances; however, these studies often conflict with each other, further showing
how hard it is to estimate causal effects in black market settings. Caulkins and Reuter (1998) find
that drug enforcement increases prices. Miron (2003) confirms this, though says that the increase
is not as high as expected. Evidence to the contrary notwithstanding, DiNardo (1993) find that
enforcement does not have any significant impact on cocaine prices, and Yuan and Caulkins
(1998) actually find a negative relationship between seizures and prices. Assuming you can find
accurate data to capture the changes in discrete variables due to enforcement in the black market,
one of the difficulties of measuring the effects of enforcement is that these supply shocks are
usually small scale, local, and intermittent. However, there have been a few notable papers that
look at much larger supply shocks as historical event studies.
Carlos Dobkin and Nancy Nicosia (2009) studied what has quite possibly been the
greatest success in disrupting the supply of a major illicit substance in the United States: the
elimination of approximately half of the precursor supply for methamphetamine in mid-1995.
They find that the price of meth tripled and purity declined significantly; however, this shock
was temporary as prices reverted to original levels within four months and purity largely returned
within a year and a half.
4 Dobkin’s research and estimation strategies are relevant to this paper, but it is important
to keep in mind that marijuana is very different than methamphetamines. In fact, marijuana is
most often considered in relation to alcohol. Indeed, Frank Chaloupka and Adit Laixuthai (1997)
find that among youth, alcohol and marijuana are substitutes for each other. To this end, it seems
likely that economic work about demand during alcohol prohibition should tell us something
about the black market for marijuana. In their work regarding Prohibition, Jeffrey Miron and
Jeffrey Zwiebel (1991) find that consumption of alcohol fell by 70 percent immediately
following Prohibition. In the subsequent years, though, alcohol consumption rose again to about
60-70 percent of its pre-Prohibition levels. Over the decade following the end of Prohibition,
consumption rose back to its pre-Prohibition levels. They note that social pressure and
lawfulness did little to reduce consumption, concluding that the assertion that consumption of
drugs would go up upon legalization is baseless.
III. Background
California
The marijuana industry in California is large. Ever since 1996, when California legalized
the medical use of marijuana through Proposition 215, a state-sanctioned medical business has
flourished, in part fueled by low requirements to receive medical access. One only need show a
doctor’s recommendation to visit dispensaries and purchase marijuana legally under state law.
This has caused part of the booming black market for the illicit substance to move above ground.
In Los Angeles alone, the dispensaries outnumber the Starbucks’, with an estimated 500-1000
dispensaries within the city limits (Onishi, 2012b). Betty Yee, from California’s Board of
Equalization, notes that the total industry in California generates about $700 million to $1.3
5 billion in state-legal medical marijuana sales each year, translating to $57 million to $100 million
a year in tax revenue (BBC News, 2012).
According to U.S. Department of Health and Human Services estimates in 2006, 11.3%
of Californians above age 12 reported using marijuana in the past year — slightly higher than the
national average of 10.4% (SAMHSA). Of these, The National Organization for the Reform of
Marijuana Laws (NORML) estimates that over 750,000 people in California — roughly 2% of
the population — use marijuana in compliance with state law. This number has to be estimated
since patients are not required to register to be in compliance with the law; it is based on rates in
Colorado and Montana, which report usage of 2.5% and 3.0%, respectively (NORML, 2011a).
Since estimates of overall marijuana use have remained relatively constant, and even decreased,
since the passage of Prop. 215, a great deal of the increase in medical use seems to be people
moving from the black market to the medical market for the sake of convenience (NORML,
2011a). This industry, though legal in California, is, as stated above, Federally illegal.
On October 7th 2011, the four California-based U.S. Attorneys disseminated news that
they would ramp up the enforcement measures on the marijuana industry in California. Officials
began using civil forfeiture proceedings against dispensaries and the landlords that own
properties in which dispensaries reside. While state and Federal laws have long conflicted when
it comes to their policies on marijuana, this particular announcement came as a rude surprise to
thousands of marijuana users and dispensary owners in the West, since it reversed a long-held
belief that the Obama Administration was sympathetic to state marijuana policies.
Indeed, Obama stated on the campaign trail in 2008, “I’m not going to be using Justice
Department resources to try to circumvent state laws on this issue” (Dickinson, 2012). The
administration seemingly reiterated this position in 2009, when the infamous Ogden memo —
6 signed on October 2009 by Deputy Attorney General David Ogden — stated that, given “limited
investigative and prosecutorial resources,” marijuana patients and their “caregivers” who operate
in "clear and unambiguous compliance with existing state law" would be left alone (Dickinson,
2012). It also reversed the stance that US Attorney General Eric Holder took at a press
conference in March 2009 when he said that marijuana dispensary raids did not represent
American policy moving forward, a statement that led the Huffington Post, along with state
representatives, to call it a “high point for the movement to legalize medical marijuana” (Grim,
2012). This Ogden memo — and perceptions of the medical market in California — in fact
motivated the further proliferation of medical shops in the state. So, it is safe to assume that the
October 7th announcement was unexpected for most medical operations.
What followed the news was approximately one year of Federal raids on marijuana
dispensaries — predominately in California, but also in the nearby states of Colorado and
Montana too. According to the New York Times, Federal authorities shut down at least 600
dispensaries all over California in the year following the announcement of a crackdown, making
it “the biggest push against medical marijuana since California legalized it in 1996” (Onishi,
2012b). Since the actions seem to renege on his campaign promises and policy statements, the
crackdown has been called “Obama’s war on pot” or “Obama’s war on weed” by several
mainstream news sources, including the Huffington Post and Rolling Stone. Previous attempts at
such a large-scale intervention in California were executed at the local level of government and
were met with little success. Most recently, in July 2012 the Los Angeles city council voted
unanimously to ban all storefront medical dispensaries. However, dispensaries were able to gain
enough political support to challenge and ultimately overturn the vote.
7 According to the New York Times, most of Federal intervention involved prosecutors
“sending letters to operators, landlords and local officials, warning of criminal charges and the
seizure of assets.” The article goes on to say, “The United States attorneys said the dispensaries
were violating not only Federal law but also state law, which requires operators to be primary
caregivers to their customers and distribute marijuana only for medical purposes” (Onishi,
2012a). The actions seem to have made marijuana more difficult to get medically, whether for
legitimate or illegitimate reasons, potentially pushing users to the black market or other medical
shops. However, the crackdown has not been focused on prosecuting end-users, and enforcement
agencies deny that legitimate patients have had to seek alternative ways of acquiring medicine.
The New York Times provided more accurate estimates of the extent of the crackdown, stating
that within the four districts: dozens were shut down in the Eastern District; 217 in the Southern
District — largely in San Diego; and greater than 200 in the Central District — largely in Los
Angeles. The Northern District, that includes San Francisco and Oakland, did not provide figures.
Estimates for all of California come from the United Food and Commercial Workers Union.
They stated that about 650 out of the 1,400 dispensaries have closed their doors (Onishi, 2012a).
A great deal of controversy has met the Federal crackdown. A proposed measure that
would have removed funding for Federal raids on marijuana failed in the Federal House of
Representatives in May 2012; it was in clear response to the crackdown efforts (Graves, 2012).
There were several large dispensaries that were shut down by the crackdown, sparking the
biggest pushback. Among the dispensaries in the crosshairs in Oakland was Harborside Health
Center, which sells more than $20 million annually and generates over $1 million in tax revenues.
Its attempted removal resulted in the filing of Federal lawsuits (Sheck, 2012). In Los Angeles, on
one day alone, 71 dispensaries were given two days notice to close and criminal warnings were
8 sent to at least 68 others (Onishi, 2012b). According to the New York Times, the United States
attorney for the Central District of California indicated that this was only the beginning of the
Los Angeles campaign (Onishi, 2012b).
No robust empirical research has been conducted regarding the medical supply-side
intervention; however, initial reports suggest that there has been a testable effect on the black
market price of marijuana. According to the Center for Investigative Reporting’s California
Watch, the price for outdoor-grown marijuana rose 20-40 percent since the US Attorneys began
cracking down on shops. The report added that “prices that were as low as $1,000 a pound have
risen as high as $2,500 a pound, making business potentially much more profitable”
(Montgomery, 2011). The California Watch attributed some of this rise in price to a rainy season
and an outbreak of mold in the crop. Regardless, data collected by local law enforcement
indicate that prices for marijuana have nearly doubled in some parts of the state, much more than
would be expected based solely on yearly variation in rain patterns (Montgomery, 2011).
Connecticut
Though this paper, thus far, has focused a great deal on the historical context of the
regulation of marijuana in California, Connecticut had an equally interesting change in policy
regarding use of the drug during the same period. On July 1st, 2011, legislation went into effect
to decriminalize the possession of up to .5 ounces of marijuana by adults for personal use. The
legislation, approved in June, reduced the penalty from a criminal misdemeanor — punishable
by one year in jail and a $1,000 fine — to a non-criminal civil infraction punishable by a
maximum $150 fine for the first offense and a maximum $500 fine for subsequent offences.
9 According to the 1995 estimates, Connecticut has a higher average rate of marijuana
consumption than California, with 13.23% of the population reporting use in the past year
(SAMHSA). Detractors of the bill worry that decriminalization will lower the price of marijuana
and thus encourage use, making this number even higher. Empirical work contradicts this
assertion, with one study finding that marijuana decriminalization has had no effect on use (L.
Johnson et al. 1981) and another finding that those who live under decriminalization laws
consume marijuana at rates comparable to (or lower than) those who live under stricter laws
(Single et al. 2000). This indicates that any change in regulation can be estimated as a supply
effect, holding demand constant. Given the high amounts of data available in Connecticut, it
seems likely that if decriminalization affected the supply for black market marijuana in the state,
this effect would be detectable.
For both states, there is an advantage in terms of the analysis of the disruptions that
occurred in the market. Specifically, there was no change to the structure of the market vehicle
by which marijuana is bought and sold within the state. Unlike analysis regarding states
legalizing medical marijuana or legalizing marijuana entirely, this paper benefits from the
change of just one supposed causal influence over the price of marijuana: in California,
enforcement, and in Connecticut, legal penalties.
Federal versus State Laws
Marijuana is perhaps the most interesting drug of study in the black market precisely
because opinions and laws differ so wildly about it. As mentioned before, Federal law trumps
state law, and in the United States, under Federal law, marijuana is completely illegal, not matter
the state you are in. Marijuana is a schedule I drug — like heroin — and as such, it is not
10 approved or condoned for use in medical treatment by the Federal government. Federal laws
cover everything about the plant. Possession of any amount is a misdemeanor on the first offense
punishable by up to a year in prison and a $1,000 fine. After the first offense, the penalty
increases to a fifteen-day minimum prison sentence, regardless of amount. Possession charges
get more severe with subsequent offenses. The sale or cultivation of marijuana is an automatic
felony charge punishable by up to five years in prison and a $250,000 fine for less than 50kg.
These penalties increase to twenty years and a $1,000,000 fine for amounts more than 50kg and
even more for amounts greater than 100kg. The penalties double if sold to a minor or within
1000ft of a school. The sale of paraphernalia is also an automatic felony charge.
Luckily for most users and distributors, the Federal government does not typically
prosecute low-level offenders. Possessing or selling the same amount that would give you a
felony if charged Federally could result in a simple civil penalty or minor infraction if charged
by the state. That is because state courts are the ones most equipped to handle minor criminal
cases, and the Federal government does not typically intervene with local law enforcement on
these issues. That is how state laws can act in seeming contradiction to Federal law: states are
granted leeway in setting their own policies so long as they do not encourage the use and abuse
of marijuana. However, regardless of compliance with state law, the Federal government has
every right to regulate even tiny amounts of marijuana sold within state borders. Federal
enforcers pick their battles wisely, and in the case of California seemed to intervene in the state
only because the industry had grown beyond an ignorable threshold. Many new growers and
medical shops were popping up and growers were increasingly willing to sell marijuana across
state borders to make a profit (Montgomery, 2011). Instead of charging people in Federal courts
— which is not what these courts are designed for — in this crackdown, Federal government
11 officials mostly sent out letters and threatened civil forfeiture proceedings against the owners of
dispensaries and the landlords and financial institutions who make these spaces available for
medical shops. Feds also raided several shops to confiscate the merchandise though. Most of
these shops would have been complicit with state law.
IV. Data Description
To study the effects of decriminalization and enforcement on the price and quality of
marijuana in California, Connecticut, and surrounding states, data from Priceofweed.com were
used. This is a publically available, crowd-sourced website that relies on anonymous user
submissions to generate average prices for marijuana across the world.
Obtaining data on black market activity is never particularly easy no matter the product
— for obvious reasons. Despite its widespread prevalence, marijuana is no exception, as there
are only a handful of sources to get pricing data. The most obvious sources are the prices cited
by news reports about seizures and policy changes. For example on December 12th 2011, two
months after the Federal crackdown began, the Huffington Post released an article called
“California Marijuana Prices Rising In Wake Of Federal Crackdown” (Montgomery, 2011). The
analysis was based on “interviews with growers, law enforcement agents and analysts.” Another
similar example of proliferated marijuana price data is the “High Times” monthly price index.
This well-known pro-marijuana journal receives submissions and aggregates prices across the
country at the state level, where possible. It is clear that these data are rather speculative and
qualitative in nature; while they should inform research topics, they are unfit for meaningful
analysis.
12 There is one notable example of an illicit drug dataset: The U.S. Drug Enforcement
Administration’s System to Retrieve Information from Drug Evidence (STRIDE) data. This is
currently the most commonly used dataset for studying the illicit drug economy. There is a great
deal of controversy regarding the accuracy of these data however. Horowitz concluded that due
to the large variation in the prices of heroin and cocaine, STRIDE data are an inaccurate
representation of the market prices for those drugs and should not be used in analyses that
require precise estimates (2001). Manski et al made similar remarks in the same year (2001).
Arkes et al, acknowledged this criticism but demonstrated that through proper construction of the
dataset, the STRIDE data were still useful for understanding drug markets (2008). Regardless,
there are few credible options available to study the price of illicit drugs, and STRIDE data have
been used in numerous studies including recently by Dobkin et al, who looked at the effect of
supply shocks to meth prices (2009). Despite their common use, the data have several glaring
problems. They are only made publically available four years after they are reported. In addition,
the dataset for marijuana has not been updated since 2003, so any recent policy analysis is not
possible to capture.
Luckily for this research, a new and unique data source became publically available in
late 2010: the Priceofweed.com dataset. These data are aggregated automatically after individual
submissions by Internet users. When last accessed on February 2nd 2013, the website had
upwards of 114,000 total submissions across the globe, though mostly in the United States. This
comes out to roughly 130 entries, on average, a day. Most of these entries are in states with
larger populations, or places with medical marijuana accessibility — the former for obvious
reasons, the latter presumably because of widespread availability and a more outspoken
marijuana-consuming population. Using a computer generated code, the publicly available
13 entries were scraped off the website.† The final dataset included entries from September 14th,
2010 through January 31st, 2013 and contained 98,658 entries within the United States. The data
include the price (in $US Dollars); date of transaction; quantity purchased (dropdown menu
includes: 1, 5, 10, 15, 20, or 25 grams in addition to 1/8, 1/4, 1/2, or 1 ounce); average quality
(low, medium or high); and location (city/town and state). The website also includes submission
of two statistics: a ranking from 1-5 of the perceived threat of law enforcement of marijuana and
a ranking from 1-5 of the perceived social acceptance of marijuana. In addition, the website has
recently required that the user provide the name of the strain — in this paper’s estimation, this is
likely to be highly inaccurate among black market purchases. These additional statistics and
strain-level data were unattainable, and not relevant to this study regardless. A summary of the
data is available in Table 1.
Table 1 — Summary of Priceofweed.com Data
Variable (n = 98,658)
Price
Date
Location
Quantity
Quality
Description
Amount given in $US
Day/month/year of purchase (09/14/2010-01/31/2013)
Nearest town/city, state
1, 5, 10, 15, 20, or 25 grams; 1/8, 1/4, 1/2, or 1 ounce
Low, medium, or high quality (user judged)
The one primary advantage of the dataset is its size and availability. The website has
enough entries to make inferences about the impact of market changes for both medium and high
quality marijuana. Furthermore, the dataset is updated in real time — at any point, one could go
online and scrape all of the most recent entries. The popularity of the website has been growing
in recent years as Figure 1 shows in detail. In addition to size and availability, the data also has
the advantage of being geared towards end-users of marijuana.
†
Special thanks to Varun Singh ’13 who wrote the code that made collecting this data simple. Without his help,
none of this would have been possible.
14 Figure 1 — Priceofweed.com Entries Per Month
Despite these obvious advantages, there are two notable disadvantages to these data. One,
the data are crowd-sourced — a double-edged sword. On one hand, this lends itself to having
large sample sizes that can be updated in real-time. On the other hand, since the data are selfreported, this is not necessarily a random sample of drug transactions. Compounding this
problem is the fact that the number of submissions has grown over time (see Figure 1). Thus the
composition of transactions may have changed — that is, the data may have started with more
serial users and grown to include more casual users over time. In addition, the data are
susceptible to fraudulent entries. With ease, someone could ruin the data by inputting numerous
erroneous purchases. Priceofweed.com has mitigated some of this risk by inserting a CAPTCHA
that requires a user to complete a simple task (i.e. word completion) before entering the data.
This helps ensure that if someone wants to tamper with the website, they would have to do so by
hand. It is unclear if this CAPTCHA has been in place since the site’s founding, or if it was put
15 in place after a perceived threat to data-accuracy was encountered. Indeed, after looking at the
data manually, it is clear that there are more than a few “false” entries — such as purchases of
marijuana for $0 or $1000, the input maximum. Nonetheless, assuming that on average people
are reliable, the size of the data helps ensure that the entries can be refined for meaningful
analysis. In addition, the fact that medium and high quality entries both increased at equal rates
indicates that this was largely due to website popularity alone.
The second drawback to this data is that it is unclear whether the purchases were made on
the black market or in medical shops. Assumptions must be made on a case-by-case basis. For
example, reports that come from states without medical marijuana — like Connecticut at the
time of decriminalization — necessarily are black market transactions. California has a
widespread medical marijuana industry. Here, this paper assumes that most of the medium
quality reports are coming from the black market, since medical marijuana tends to be of higher
quality. Where medical shops exist, a good deal of the high quality reports are expected to be
medical in nature, keeping in mind that estimates state that only 2% of people purchase
marijuana within state medical laws.
Only one other study has made use of the data provided on Priceofweed.com. Zook et al
(2011) regressed the price data against independent variables such as rank in plant eradication,
presence of medical industry, youth population, marijuana arrests, and distance from Humbolt
County, California (identified as the largest production center in California for export to the rest
of the country). They found that distance from California and the presence of a medical
marijuana industry were the two biggest factors influencing the price. The study raises more
questions than it answers as many of the regressors included in the models were correlated with
16 each other. Since this paper focuses on historical events within states, many of the variables that
Zook et al tried to account for will simply be held constant.
V. Empirical Methods/Hypotheses Tested
Hypotheses Tested
Two separate event studies — occurring in Connecticut and California — on the impact
of government policies on the market price of marijuana will be estimated. The model
specification and the data formulation are highly comparable for both states. For California, I
hypothesize that the Federal intervention will directly affect the market for high quality
marijuana and indirectly affect the market for medium quality marijuana. First, the intervention
will decrease the market supply of high quality marijuana — since most medical product is
assumed to be high quality — by reducing the number of shops and scaring other shop owners.
The intervention will leave demand for high quality marijuana unchanged though. The result will
be a leftward shift in the supply curve for high quality marijuana and no shift in the demand
curve, corresponding to an increase in the price, proportional to the impact of the policy. The
difficulty of attaining high quality marijuana — or the increase in its price — will increase the
demand for black-market marijuana, which is assumed to be largely medium quality. Since the
supply curve of medium quality product is unchanged by the policy, this will result in a
rightward shift in the demand curve and a proportional increase in the price for medium quality
product. So the net effect will increase the price of both high quality (assumed to be largely
medical) and medium quality (assumed to be largely black market) marijuana, ceteris paribus.
Therefore, I will test this hypothesis against the null that there was no significant change in the
price related to the crackdown.
17 For Connecticut, I hypothesize that any change in regulation that lowers the cost of
distributing and consuming marijuana should negatively affect the price of marijuana, regardless
of quality. A decrease in regulation will shift the supply curve to the right. The market demand
curve for marijuana will be unchanged. Thus the net effect will be a decrease in the price
proportional to the shift in the supply curve alone. This assumes — consistent with the work of L.
Johnson and Single — that the overall demand for marijuana will be unaffected by
decriminalization. So in Connecticut I test the hypothesis that the price of marijuana went down,
against the null that the intervention caused no change in the market for both high and medium
quality marijuana.
Data Treatment
Before testing these hypotheses though, the data had to be properly refined. There were
an insignificant number of “low quality” entries, perhaps because the marijuana in the United
States is typically of higher quality or perhaps because people who purchase low quality
marijuana don’t bother to report their purchases. Regardless, these entries were removed from
the data. Further, entries of 15, 20, and 25 grams were insignificant in quantity and highly
variable to aggregate so these were likewise removed. Next, the upper and lower 1% of the data
from each type of entry (1, 5, or 10 grams, or fractions of an ounce) were removed to eliminate
blatant outliers — essentially entries of $0 and entries of $1000, the website maximum.
Marijuana, like soda, is sold in various package sizes, and, like soda, consumers can
expect to receive discounts for purchasing in bulk. So in order to meaningfully compare
purchases of different quantities, the price could not simply be multiplied linearly to form a
common unit of measure (price per ounce in this case). For example, this data showed that the
18 average price of 1/8 of an ounce of high quality marijuana was $53.66 and the average price of
an ounce was $257.48. If we multiplied $53.66 by 8 we get $429.28, greatly overstating the price
per ounce in the United States.
To take advantage of all of the data in the dataset, a proper conversion factor had to be
established to compare entries of a recorded price across any given unit of measure. Kenneth
Clements found through empirical study of the marijuana market that the best way to standardize
the unit of measure is to use the percentage change in the price to the corresponding change in
the package size (2006). Caulkins and Padman likewise found that a log-linear relationship was
the best way to model the quantity discounts given to marijuana purchases (1993). Indeed, as
Figure 2 (below) shows, the log-linear model fit the Priceofweed.com data well. This implies the
following specification for the price discounts:
(1)
ln 𝑃 = 𝛼 + 𝛽 ln 𝑄
where P is price and Q is quantity. α and β are parameters to be estimated. Solving this provides:
(2)
P 𝑄 = 𝑒 ! 𝑄!
Using this expression for the fitted price of any given package size based on regression estimates
of α and β, the data were converted into standard units of measure. Since the majority of the
Priceofweed.com data was in terms of ounces, the majority of the regression analysis in this
paper uses price per ounce as the unit of measure, so the prices of the ounces were left
unchanged, and all other variables were proportionally converted into prices per ounce. Given
the linearity of the expression though, all the regressions could have just as easily been specified
with a different unit of measure to provide the same results.
19 Figure 2 — Average Log Price vs. Log Quantity for Marijuana
Once all the data were converted into prices per ounce, all of the variability of the smaller
package sizes resulted in a number of results that were larger, or smaller, than the existing price
per ounce sample. To remove these new outliers, and control for further variability in the range
of data provided by users, the existing 5th and 95th percentiles of the ounce-only data was used as
the absolute minimum and maximum for the resulting fitted price per ounce values. The resulting
dependent variable used in all regressions is summarized in Table 2.
Table 2 — Summary of Dependent Variable, The Fitted Price Per Ounce
Quality
High
Medium
Mean
$256.50
$149.66
Std. Dev.
$95.89
$89.70
Obs
47,501
38,596
Min
$20
$10
Max
$450
$380
Estimation Technique
Since both changes in regulation policy constituted natural experiments with unaffected
states serving as adequate control groups, the analysis of the events relied on the basic
20 differences-in-differences model. This model was then adapted to account for more control
groups, time periods, and dynamic effects.
The analysis begins with the most basic formulation: a standard differences-indifferences model. By analyzing the dependent variable at the state level, the following model is
postulated:
(3)
𝑃!" = 𝛽! + 𝛽! 𝑋! + 𝛽! 𝑇! + 𝛽! 𝑋! 𝑇! + 𝜀!"
where i = 1, 2 denotes the individual, and t = 1, 2 denotes the time period of measurement. Pit is
the dependent variable, the price per ounce of marijuana. Xi takes on a value of 1 if the state in
question is where the effect happened — for the decriminalization experiment, Connecticut, and
for the enforcement experiment, California — and 0 otherwise. Tt is a variable that equals 1 after
the date of the event and 0 otherwise — for the decimalization experiment, July 1st 2011, and for
the enforcement experiment, October 7th, 2011. XiTt is an interaction term that captures the
treatment effect. This model assumes that the trend in the price is the same before and after the
date of the event, which makes it easy to estimate. This model further assumes that there are only
two time periods and a single treatment and a single control group, which makes it rather naïve.
To account for these more complicated specifications, the causal effect is estimated by
using the fixed effects regression model. This is specified as follows:
(4)
𝑃!" = 𝛽! + 𝛽! 𝐸!" + 𝛾! 𝐷2! + ⋯ + 𝛾! 𝐷𝑛! + 𝛿! 𝐵2! + ⋯ + 𝛿! 𝐵𝑇! + 𝜀!"
where i = 1,…,n denotes the individual, t = 1,…,T denotes the time period of measurement, Eit =
1 if the ith individual has received the treatment by date t and = 0 otherwise. Pit is defined as
before. Dii is a binary variable indicating the ith individual; Btt is a binary variable indicating the
tth time period. In this case Eit is the regression coefficient of interest measuring the treatment
21 effect while controlling for all time periods (in this paper, all the time dummies are monthly
variables) and control variables (unaffected states or metropolitan areas).
To see how the effect changes within the treatment period, the analysis is extended as
follows:
𝑃!" = 𝛽! + 𝜷𝑬!" + 𝛾! 𝐷2! + ⋯ + 𝛾! 𝐷𝑛! + 𝛿! 𝐵2! + ⋯ + 𝛿! 𝐵𝑇! + 𝜀!"
(5)
where all of the variables are defined exactly as before, except now 𝜷 and Eit are vectors. Eit has
multiple dummy variables that = 1 if the treatment is within the months specified and = 0
otherwise. The sum of all the entries in Eit is equivalent to the Eit of before to create the same
specification as equation (4). For all regressions (3-5) the heteroskedasticity robust standard
errors are used and errors are clustered on state or metropolitan areas accordingly.
VI. Results
Trends in Marijuana Prices
Though this paper’s main focus is on the effects of State policy on the price of marijuana
in the United States, one other role of this research is to shed light on the large black market for
marijuana in the country. The size of this market is enormous, yet because of its illegal nature,
remarkably little is known about the overall trends in the business. In fact, Priceofweed.com is a
website that presumably exists to notify users of prices in their area so that they can be informed
as they make their transactions. Without this collected information, most end-users exist within
virtual bubbles, having little information about the quality or the true worth of the marijuana they
are purchasing. Even with the website, they still cannot readily see trends in the industry. One
must aggregate the data to understand how the market has changed over time. Figure 3 gives us
22 an interesting glimpse at this market, showing the price aggregated by month and quality all
across the United States.
Figure 3 — Average Price Per Ounce of Marijuana in the United States
As one can see, there are — at least — two distinct markets for marijuana in the country,
based on quality. This is especially impressive given the lack of information that end-users
receive — in states that have no easily accessed medical market — about potency, growth
processes, location of growing, and even strain. Despite this lack of information, there is a
dramatic increase in price over perceived quality of strain. Figures 4 and 5 (next page) give
another look at this information. Here the data are aggregated by region in the continental United
States, with the Northeast and the West — the central focus of this paper — highlighted.
There are two interesting trends in the data. Firstly, the price of medium quality
marijuana nationally has remained stable over the last two years. Though dipping during the
early months of 2011, the price remained at around $150 an ounce, indicating a relatively stable
23 Figure 4 — Average Price Per Ounce of High Quality Marijuana by Region
Figure 5 — Average Price Per Ounce of Medium Quality Marijuana by Region
24 market supply and demand for cheap cannabis. When viewed regionally, this is not as apparent,
but it is still relatively true. The Northeast saw little change. The South and Midwest saw very
slight increases in the price of medium quality marijuana. The West seems to be the only place
that saw a decrease in the price of cheap marijuana.
In stark contrast to this, the price of high quality marijuana has plummeted in this country
over the same period. By January 2013, it would cost one almost $40 less to purchase an ounce
of high quality marijuana compared to just 2 years earlier. According to Figure 4, this does not
seem to depend on what region of the country one is in. Assuming consumption rates of
marijuana have remained relatively stable, this tells us that there is likely something occurring on
the supply side to negatively influence the price. Either there are many more crops making it to
market or the perceptions of enforcement of distribution have declined. It is also interesting to
note just how much cheaper high quality marijuana is in the West. This is probably driven by
states like California, Colorado, and Washington that have large, locally produced, medical
cannabis markets. No similar trend is apparent in medium quality marijuana; this is consistent
with the idea that most medical cannabis is of higher quality.
Connecticut
Beginning with high quality marijuana in Connecticut, the effect of decriminalization was
measured using the specifications in equations (3) and (4). Since Connecticut legalized medical
marijuana on October 1st 2012, the data used included entries from September 14th, 2010 through
September 30th, 2012 so as not to conflate the effect of decimalization with that of a newly
formed medical market. The control group included only states in the same geographic area to
control for geographic variation in the price of marijuana. These states were New York,
25 Massachusetts, New Jersey, and Pennsylvania. Figure 6 shows the event graphically. It does not
appear that the decriminalization caused an abrupt change in the market equilibrium. However,
there is a rather striking downward trend — the opposite of the other states — following the
decriminalization along with subsequent averages lower than surrounding states, where
previously they had been higher. Specifically, the price for high quality marijuana in Connecticut
appears to have dropped relative to other states; post-decriminalization, the price does not
experience the same increases in the equilibrium price that the control states do. Table 3 shows
the regression results for the high quality analysis.
Figure 6 — High Quality Price Per Ounce in Connecticut Over Time
As we can see, all of the variables of interest are significant at least at the 5% level. We
fail to reject the null hypothesis that the decriminalization had no significant effect on the price
of marijuana in Connecticut and conclude that decriminalization had a negative effect on the
price. The best estimate for this effect is between $13 and $14. Indeed, though the data are noisy,
26 Table 3 — Effect of Decriminalization on Price Per Ounce of High Quality Marijuana in
Connecticut Versus All Regional Controls
Decriminalization Effect
Event Dummy
Connecticut Dummy
Constant
Individual State Dummies
Monthly Dummies
Observations
Regressors
R2
(1)
-13.187*
(3.404)
-18.895**
(3.404)
13.430***
(1.413)
304.123***
(1.413)
No
No
6,152
4
.011
(2)
-14.136**
(1.988)
13.623**
(2.378)
292.489***
(9.626)
No
Yes
6,152
27
.027
(3)
-12.339*
(3.541)
-19.743**
(3.541)
(4)
-13.345**
(1.895)
317.553***
(8.99e-12)
Yes
No
6,152
7
.015
305.885***
(8.301)
Yes
Yes
6,152
30
.031
Notes: Standard errors are in parenthesis; they are robust and clustered on state. * means P≤0.05; ** means P≤0.01;
*** means P≤0.001. The dependent variable is the price of marijuana per ounce.
there is a clear difference in the average price per ounce from just one year earlier. The average
in November 2010 was roughly $360 an ounce, and this was approximately $300 a year later.
Though Connecticut once had prices higher than its neighboring states, the price per ounce
seems to drop abruptly relative to them, especially in September 2011 when many states
experienced spikes in their prices.
Repeating this process for medium quality marijuana also yielded significant results,
although not to the same degree as the high quality results. Indeed, as Figure 7 shows, there does
not appear to be as dramatic an impact on the price of the medium quality data, although the
price does appear to rise noticeably in other states while dipping slightly in Connecticut during
the same time periods following July 1st, in particular between September 2011 and April 2012.
The regression results in Table 4 summarize the effect of decriminalization on the medium
quality marijuana market, telling a similar story as those in Table 3. The price dropped by about
$13 dollars relative to surrounding states due to the decriminalization. Most of these results are
27 significant at the 5% level, with the exception of simple specification of equation (3) in
regression (1) of Table 4.
Figure 7 — Medium Quality Price Per Ounce in Connecticut Over Time
It should be immediately clear that these results are based on a noisy dependent variable.
Indeed, the R2 values are all rather low. However, this is assumed to be largely due to the nature
of crowd-sourced submission of the data — which is not necessarily, at all times, a
representative sample of buyers. It could also be due to a potential stickiness of prices in the
market due to information asymmetries about availability of other dealers and existing wholesale
prices — which would negatively impact end-users. However, when we step back and take a
look at the two periods — before decriminalization and after — the results paint a clear picture
of a drop in the price of the product relative to other states due to decriminalization. This is true
of both medium and high quality marijuana, a satisfying result, despite the noisy appearance of
the graphs.
28 Table 4 — Effect of Decriminalization on Price Per Ounce of Medium Quality Marijuana
in Connecticut Versus All Regional Controls
Decriminalization Effect
Event Dummy
Connecticut Dummy
Constant
Individual State Dummies
Monthly Dummies
Observations
Regressors
R2
(1)
-13.236
(5.042)
11.324
(5.042)
7.887
(8.645)
165.390***
(8.645)
No
No
4,897
4
.003
(2)
-13.780*
(4.011)
8.187
(8.387)
170.230***
(8.898)
No
Yes
4,897
27
.007
(3)
-12.148*
(4.287)
10.236
(4.287)
(4)
-12.629*
(3.328)
173.277***
(1.30e-12)
Yes
No
4,897
7
.018
177.414***
(15.255)
Yes
Yes
4,897
30
.021
Notes: Standard errors are in parenthesis; they are robust and clustered on state. * means P≤0.05; ** means P≤0.01;
*** means P≤0.001. The dependent variable is the price of marijuana per ounce.
Though we have failed to reject the hypothesis that decriminalization had a negative
impact on the price of marijuana, it seems likely that this influence was not felt for all package
sizes. The Connecticut law put into place on July 1st dropped criminal penalties for possession of
up to half an ounce of marijuana while leaving in place the laws that govern possession of more
than this amount. Curiously, this drives a wedge in the pricing data available to users of
Priceofweed.com since great deals of the purchases are recorded in ounces. Indeed, the data were
consolidated at the price per ounce because this would alter the fewest number of entries.
Though the hypothesis was that the policy would negatively influence the price of weed
no matter the package size — by reducing the perceived threat of law enforcement in targeting
potential suspects of marijuana crimes and through the possibility of transactions done in parts
— it is worthwhile to observe the differences that the new policy made for the different package
sizes to see if any modifications need to be made to the strength of the conclusion. To see
whether the legislation impacted purchases of amounts less than or equal to half an ounce
29 differently than those greater than half an ounce, four regressions on the high and medium
quality data were run. First the data were grouped by package size of greater than half an ounce
— in the Priceofweed.com data kept, this only included package sizes of exactly one ounce —
and less than or equal to half an ounce of marijuana. Then the data were consolidated at the price
per gram level to avoid confusion, although, as stated before, this does not influence the
outcomes or significance, but simply the unit of measure. The regression specification (4) was
then run on the resulting groups to see how the price was influenced differently depending on
package size. The results are in Table 5.
Table 5 — Effect of Decriminalization in Connecticut on Price Per Gram With Varying
Package Size
High Quality
Decriminalization Effect
Constant
Individual State Dummies
Monthly Dummies
Observations
Regressors
R2
(>1/2 Ounce)
-1.619***
(0.185)
28.473***
(0.632)
Yes
Yes
2,442
30
.055
(≤1/2 Ounce)
-0.574
(0.423)
23.053***
(0.999)
Yes
Yes
3,710
30
.035
Medium Quality
(>1/2 Ounce)
-0.901
(1.982)
22.837**
(3.888)
Yes
Yes
1,751
30
.025
(≤1/2 Ounce)
-1.871*
(0.588)
25.166***
(1.274)
Yes
Yes
3,146
30
.037
Notes: Standard errors are in parenthesis; they are robust and clustered on state. * means P≤0.05; ** means P≤0.01;
*** means P≤0.001. The dependent variable is the price of marijuana per gram where the “>1/2 Ounce” columns only
include entries where the transaction was larger than .5 ounces and the “≤1/2 Ounce” column only includes entries
where the transaction was smaller or equal to .5 ounces.
Though all coefficients are negative, the only significant effects seem to be for high
quality purchases of greater than half an ounce and medium quality purchases of less than half an
ounce. The War on Drugs has had a disproportionate enforcement impact on low-income
communities. People with more money have better access to legal representation and are also
less likely to be targeted by police officers carrying out drug raids. As Harry Levine describes,
police departments patrol “high crime” neighborhoods more frequently and make the bulk of
30 their stops and searches in these low-income areas (2010). That the data show that the largest
decrease in the price occurs among small purchases of lower quality marijuana should come as
no surprise since this is likely the type of marijuana that the majority of people punished for drug
crimes carry. They have to “play by the rules” more carefully to avoid facing harsh penalties,
explaining why the new policy has no influence over large package sizes of this type of weed.
On the contrary, changing attitudes about likelihood of enforcement of marijuana crimes likely
explains the significant impact on purchases of larger than half an ounce of high quality
marijuana since the penalty for carrying this marijuana has not actually changed. People buying
expensive product in bulk receive the majority of this decrease in the cost of distribution while
existing prices continue for smaller scale purchases. Though the results for the other two are not
significant, the coefficients are still negative, a good sign that the conclusions from before still
hold.
In addition to these standard regressions, a couple additional looks were taken at the
impact of decriminalization. Firstly, the analysis was done again on just the Border States —
New York and Massachusetts — to see the influence of state lines in the transfer of drugs and to
test the robustness of the results against states where geographic variation should be entirely
negligible. Coincidentally, these states were also far less noisy, so this could serve as an
additional robustness check against the results from earlier. Secondly, the regressions were run
again using the specification in equation (5) to see whether the influence of decriminalization
“faded” over time versus staying relatively constant — as we would expect given the fact that
this was a permanent event lowering the cost of distributing drugs in the state. The results can be
found in Table 6.
31 Table 6 — Effect of Decriminalization on Price Per Ounce in Connecticut versus Border
states and Effects Using Dynamic Specifications
High Quality
Decriminalization Effect
(Border
States)
-11.506*
(1.688)
(Jul-Sep 2011)
(Oct-Dec 2011)
(Jan-Mar 2012)
(Apr-Jun 2012)
(Jul-Sep 2012)
Constant
Individual State Dummies
Monthly Dummies
Observations
Regressors
R2
315.142***
(7.853)
Yes
Yes
3,937
28
.037
(Dynamic
Effects)
-26.825**
(4.352)
3.707
(8.971)
-27.036**
(4.938)
-12.677**
(1.670)
-6.542***
(0.447)
303.975***
(8.881)
Yes
Yes
6,152
34
.032
Medium Quality
(Border
States)
-7.700
(2.580)
191.416**
(12.403)
Yes
Yes
3,063
28
.007
(Dynamic
Effects)
-8.668
(3.582)
-23.991*
(6.727)
-30.965*
(6.827)
-3.089
(2.487)
-14.525*
(3.922)
178.825***
(15.342)
Yes
Yes
4,897
34
.022
Notes: Standard errors are in parenthesis; they are robust and clustered on state. * means P≤0.05; ** means P≤0.01;
*** means P≤0.001. The dependent variable is the price of marijuana per ounce. The Dated effect variables are equal
to one if the state is Connecticut during the dates in parenthesis and zero otherwise to capture the dynamic effect of the
policy change.
As we can see from the results, the effect is the exact same for the border state analysis,
although the medium quality effect becomes barely statistically insignificant. More interestingly
for the regression based on equation (5), we can see that while the effect was larger in the earlier
stages of the decriminalization, the effect does not go away over time and remains — for the
high quality data — statistically significant and negative (or positive and insignificant) when the
data is split into quarters of time after the change in policy. This makes sense, since the
disruption was permanent and caused a dramatic shift in the level of enforcement when
compared to the pre-decriminalization period.
32 California
In California, a similar process was used to test whether State policy affects the price of
marijuana. The analysis starts with the high quality data. The hypothesis was that the shutting
down of hundreds of medical marijuana shops would increase the price of cannabis by making it
harder to attain and increasing the risks involved in operating a shop, in particular in cities like
Los Angeles and San Francisco/Oakland (referred to as the Bay Area) where the crackdown on
shops was fierce and the concentration of shops was high.
In order to test this, California was compared not to surrounding states, but to all states in
the continental United States with the exception of Connecticut. The reason for this is threefold.
One, there was a largely insignificant number of entries in states immediately surrounding
California, especially compared to California itself. Two, the illegal market in California is
unlike that of Connecticut, in that a great deal of marijuana in the United States is believed to
originate in California — this is likely very true in surrounding states — so including all states in
the regressions hopes to control for this by using states that have very small amounts of
marijuana coming from California. Third, the marijuana trade in California is part of a large grey
market, where a great deal of the industry operates aboveground through easy access to medical
cards and state slackness on enforcing marijuana laws. Including non-border states in similar
situations — namely, Colorado and Washington — hoped to isolate the effect that enforcement
had on California alone. In order to use both of these states as controls though, no data could be
used after November 7th 2012, when marijuana was legalized for personal consumption in these
states. So the data include entries from September 14th, 2010 until November 6th, 2012.
33 Figure 8 shows the graph of high quality marijuana in California versus the rest of the
United States. It does appear that perhaps the price rose in comparison to the rest of the country
following the October 7th announcement. The data appear to spike during the period of
heightened enforcement relative to the surrounding states. After testing the prediction against
models (3) and (4) though, it is largely clear that this is not the case. Despite the overwhelming
crackdown efforts and the large samples with which to test the predictions, the findings shown in
Table 7 demonstrate that enforcement had no effect on the price of marijuana. If anything, the
results show that enforcement appears to have negatively influenced the price. This paper will
not attempt to make that claim; instead it reasons that enforcement had no effect on the price and
that the decreases were a natural by-product of marijuana farmers and distributors continuing to
expand within the state. All of the coefficients of interest are negative and statistically significant
at least at the 1% level.
Figure 8 — High Quality Price Per Ounce in California Over Time
34 Table 7 — Effect of Enforcement on Price Per Ounce of High Quality Marijuana in
California Versus All State Controls
Enforcement Effect
Event Dummy
California Dummy
Constant
Individual State Dummies
Monthly Dummies
Observations
Regressors
R2
(1)
-10.367***
(2.632)
-22.090***
(2.632)
-50.556***
(5.599)
280.964***
(5.599)
No
No
36,002
4
.057
(2)
-7.707**
(2.828)
-52.467***
(5.658)
272.154***
(7.127)
No
Yes
36,002
29
.063
(3)
-8.036***
(1.802)
-24.421***
(1.802)
(4)
-4.953**
(1.793)
230.409***
(1.64e-11)
Yes
No
36,002
48
.127
221.905***
(3.176)
Yes
Yes
36,002
74
.134
Notes: Standard errors are in parenthesis; they are robust and clustered on state. * means P≤0.05; ** means P≤0.01;
*** means P≤0.001. All State Controls include all U.S. States except Hawaii, Alaska, and Connecticut. The dependent
variable is the price of marijuana per ounce.
For medium quality marijuana, it was hypothesized that the lack of access to the medical
market and the rise in prices for high quality marijuana would increase the demand for medium
quality marijuana, raising the price through a demand shock. Figure 9 shows the price of medium
quality marijuana over time in California versus the rest of the United States and Table 8 shows
the regression results. It is immediately obvious that enforcement seems to have had no effect on
the price of medium quality marijuana either. As the graph shows, the price of this cannabis
seemed to be on par with the price in the rest of the country, and these prices drastically drop in
the months following the October 7th announcement such that the medium quality price per
ounce in California became far lower than the rest of the United States. The regression results in
Table 8 corroborate these observations with incredibly negative and statistically significant
effects of enforcement for all regressions run. During enforcement, the gap in the prices between
California and the rest of the country widened dramatically.
35 Figure 9 — Medium Quality Price Per Ounce in California Over Time
Table 8 — Effect of Enforcement on Price Per Ounce of Medium Quality Marijuana in
California Versus All State Controls
Enforcement Effect
Event Dummy
California Dummy
Constant
Individual State Dummies
Monthly Dummies
Observations
Regressors
R2
(1)
-21.814***
(2.914)
10.153***
(2.914)
2.798
(5.180)
143.763***
(5.180)
No
No
27,733
4
.006
(2)
-21.260***
(2.759)
2.358
(4.971)
131.538***
(6.551)
No
Yes
27,733
29
.009
(3)
-21.766***
(2.766)
10.104***
(2.766)
(4)
-21.248***
(2.680)
146.562***
(9.17e-11)
Yes
No
27,733
48
.043
134.313***
(3.319)
Yes
Yes
27,733
74
.046
Notes: Standard errors are in parenthesis; they are robust and clustered on state. * means P≤0.05; ** means P≤0.01;
*** means P≤0.001. All State Controls include all U.S. States except Hawaii, Alaska, and Connecticut. The dependent
variable is the price of marijuana per ounce.
36 If — as this paper assumes — most of the medium quality marijuana is not being sold in
medical shops and instead sold on the black market, then we can immediately reject the
hypothesis that many medical users turned to the black market to get medicine when shops were
closed. They likely just turned to open stores instead. Regardless, they did not experience any
change in the quality of their marijuana. Instead, since enforcement seemed to have no impact on
the high quality market, it is far more likely that the continued drop during this period in high
quality prices further lowered the demand for medium quality marijuana, causing the prices to
drop and stay low during the enforcement. Why would you buy poor quality weed when for just
a bit more, you can get much better product? In other states, high quality marijuana is priced
much higher, so there exist two distinctly separate markets. Here, the medium quality marijuana
could have dropped in price as a response to the continued fall in price of high quality product.
As Table 8 shows, the effect of enforcement seems to be negligible — or negative — on the
price of marijuana.
Given the unexpected nature of the results, this raises several questions about the
assumptions made about these markets though. Namely, is it fair to assume that most medical
marijuana is high quality and most street marijuana is medium quality and that the high quality
data comes predominately from medical shop purchasers and that medium quality data comes
predominately from black market purchasers?
Let us assume for a moment that the assumptions are wrong: the vast majority of the
observations for high and medium quality marijuana are coming from the black market and this
dataset is not really picking up any of the direct effect on the medical shop buyers. This may
very well be the case. Then this paper has not shown that the direct effect of the intervention was
negligible; it could still be the case that the prices of medical product increased as a result of the
37 intervention. If this were the case, then given the vast amount of data in California, wouldn’t
there have been some measurable effect on the equilibrium prices of high and medium quality
marijuana regardless? If people were buying from local stores, and these stores closed their doors,
people would have had to search out other locations to get marijuana. Presumably some would
have turned to the black market if there were no nearby stores or if the prices rose to intolerable
levels. Shouldn’t there have been some disruption of the black market equilibrium price in the
positive direction? Instead we see the prices of both high and medium quality marijuana continue
to drop in the face of a vast enforcement effort. Part of the rationale behind the crackdown was
that the U.S. attorneys believed that medical product at these shops was being sold illegally on
the black market. At the very least, we can at reject the idea that the medical shops targeted had
any significant impact on the black market sale of the drug by channeling users there.
There is another possibility: that medium quality marijuana is also sold in medical shops
along with high quality product. If this is true, then this leads us to further reject the idea that
enforcement had any impact on the price of marijuana in the state. Either way, regardless of the
manner in which people were buying the product, it seems definitively clear that the enforcement
effort did little to disrupt the normal distribution of marijuana.
There are two potential problems with these conclusions about enforcement. First,
enforcement, unlike decriminalization in Connecticut, was not a universal effort that affected
everyone equally at the same time. Rather, it was a slow process of property seizures, notices to
landlords, and evictions that made its way across the state at different times. Second, it seems
problematic to assume that California can be compared to every other U.S. State since it is a netexporter of marijuana. Virtually all of the marijuana consumed in the state is grown there and it
has its own grey market system of distribution that makes it unique. In order to test the
38 hypothesis of enforcement with these two factors in mind, several additional regressions were
run. First, California was compared with just medical states with large self-contained markets —
Colorado, Washington, Oregon and Michigan. These are the closest approximations to the
culture of marijuana acceptance in California. Colorado and Washington legalized marijuana and
just prior had large medical operations allowed under state law. Oregon — a border state — and
Michigan are medical states that have a large amount of data and relatively flexible requirements
to be part of the medical program. Second, model (5) was tested to pick up the lagged effects of
enforcement. Maybe the announcement did not create much of a stir, but the market was affected
later. It is certainly true that Los Angeles experienced ramped up enforcement at a later date than
many other parts of the state. That is what this regression will test. The results of these additional
tests are in Table 9 below.
For both high and medium quality marijuana, the results still produce negative
coefficients when compared to other medical states, although neither of these is significant at the
5% level. We can conclude that enforcement still had a negligible effect on the price of
marijuana regardless of quality. As for the dynamic specification, the enforcement had an
entirely negative effect on medium quality pot, and for the high quality variety, it seems to have
only potentially had a positive impact on the price for one month during the whole period of
enforcement, July 2012. It seems unlikely that this is any more than a statistical anomaly. Since,
by this point, the federal government had shut down hundreds of shops, it is dubious that the
effect would be felt for only one random month during this period. There is no reason, given
news accounts, to think this month is unique.
What if these states are inadequate controls for other reasons? Perhaps the price in
California would have gone down by more had the Feds not intervened. Unfortunately, this is an
39 unobservable counterfactual, and exceedingly difficult to prove. However, variation in the effect
of enforcement in California could lead us to isolate whether or not the crackdown did indeed
Table 9 — Effect of Decriminalization on Price Per Ounce of Marijuana in California
Versus Medical states and Effects Using Dynamic Specifications
High Quality
Enforcement Effect
(Medical
States)
-4.003
(4.332)
(Oct 7-Nov 6, 2011)
(Nov 7-Dec 6, 2011)
(Dec 6, 2011-Jan 6, 2012)
(Jan 7-Feb 6, 2012)
(Feb 7-Mar 6, 2012)
(Mar 7-Apr 6, 2012)
(Apr 7-May 6, 2012)
(May 7-Jun 6, 2012)
(Jun 7-Jul 6, 2012)
(Jul 7-Aug 6, 2012)
(Aug 6-Sep 6, 2012)
(Sep 7-Nov 6, 2012)
Constant
Individual State Dummies
Monthly Dummies
Observations
Regressors
R2
204.908***
(10.956)
Yes
Yes
8,618
32
.091
(Dynamic
Effects)
-18.119***
(3.318)
-37.565***
(3.314)
-22.200***
(3.313)
-27.944***
(3.143)
-40.353***
(2.640)
-10.736***
(3.160)
-2.690
(2.417)
-3.567
(2.584)
-2.885
(2.498)
5.439*
(2.205)
-1.667
(2.169)
-0.318
(2.204)
221.897***
(3.178)
Yes
Yes
36,002
85
.135
Medium Quality
(Medical
States)
-3.215
(11.785)
130.791***
(4.360)
Yes
Yes
6,849
32
.016
(Dynamic
Effects)
-17.945***
(3.180)
-13.971***
(3.707)
-14.303**
(4.261)
-28.603***
(4.222)
-36.489***
(4.178)
-20.147***
(4.125)
-23.052***
(3.202)
-21.654***
(3.552)
-23.253***
(3.297)
-21.820***
(2.665)
-25.286***
(3.228)
-17.555***
(3.060)
134.318***
(3.319)
Yes
Yes
27,733
85
.046
Notes: Standard errors are in parenthesis; they are robust and clustered on state. * means P≤0.05; ** means P≤0.01;
*** means P≤0.001. The dependent variable is the price of marijuana per ounce. The Dated effect variables are equal
to one if the state is California during the dates in parenthesis and zero otherwise to capture the dynamic effect of the
policy change. The medical states include Colorado, Washington, Oregon, and Michigan.
40 have some effect on the marijuana market in the state. Figure 10 shows the price per ounce of
high quality marijuana in Los Angeles and the Bay Area, two places where the crackdown was
very prevalent, versus the rest of California. Figure 11 shows the same thing for medium quality
pot.
Figure 10 — High Quality Price Per Ounce in California by Metropolitan Area
In both cases, the only area that looks like it could potentially have experienced a slight
increase in price is Los Angeles. For the high quality data, there is a spike upwards around
February 2012, and the price remains quite high. Most of the prices in California seemed to have
dropped over the two-year period, but Los Angeles remains more expensive than the rest of the
state in largely all periods. For the medium quality case, the spike seems to occur right as that
enforcement was announced, although the price quickly reverts to a stable mean, $40 below its
price just two years prior. The results of this regression are displayed in Table 10.
41 Figure 11 — Medium Quality Price Per Ounce in California by Metropolitan Area
Table 10 — Effect of Enforcement on Price Per Ounce of Marijuana in Los Angeles and
the Bay Area Versus the Rest of California
High Quality
Enforcement Effect
Los Angeles Dummy
(Los Angeles)
-3.713
(3.140)
27.114***
(3.174)
Bay Area Dummy
Constant
Monthly Dummies
Observations
Regressors
R2
175.182***
(9.957)
Yes
4,647
29
.069
(Bay Area)
-4.738
(3.280)
-8.485
(7.367)
185.315***
(7.361)
Yes
4,647
29
.054
Medium Quality
(Los Angeles)
-0.778
(3.093)
8.206*
(3.492)
132.573***
(8.359)
Yes
4,220
29
.016
(Bay Area)
10.210***
(1.867)
-12.178***
(2.817)
136.371***
(7.681)
Yes
4,220
29
.015
Notes: Standard errors are in parenthesis; they are robust and clustered on metropolitan statistical area. * means
P≤0.05; ** means P≤0.01; *** means P≤0.001. The dependent variable is the price of marijuana per ounce.
42 According to the results — and consistent with the data of California compared to other
states — for high quality data, the effect on Los Angeles and the Bay Area was slightly negative
though statistically insignificant. The only significant result came from the Bay Area for medium
quality marijuana. Here it appears there was an increase in the price of marijuana by
approximately $10. Though the results are noisy, this could be the only instance of higher prices
of marijuana due to enforcement efforts found in this analysis.
VII. Implications and Robustness
The question then becomes: why and how does State policy influence — or not influence
— the market for marijuana across the country? Well, for the case of Connecticut, the answer
seems obvious. If the prison sentence for carrying small possession of marijuana goes away, it
becomes easier to distribute without being caught. For example, if someone choosing to sell a
quarter of marijuana is stopped by police, they can claim the product as their own and walk away
with no threat of jail time. It seems obvious why this would particularly affect the price of lower
quality marijuana sold in smaller amounts, as seen in the results. How this process affects larger
package sizes is unclear. Indeed, the price for an ounce of marijuana in Connecticut of medium
quality marijuana did not change significantly before and after decriminalization. This is the
opposite for higher quality marijuana, where the price decreased dramatically for package sizes
larger than half an ounce and did not change all that much for package sizes smaller than this
amount. One explanation that this paper puts forward is that higher quality marijuana sold in
larger amounts is typically sold in higher income areas. These people are less likely to be caught
with marijuana regardless of the penalties due to the disproportionate impact that the War on
Drugs has on minorities and the poor. It may be the case that here, the price goes down simply
43 by virtue of the fact that the threat of enforcement is that much lower, while the price for
medium quality marijuana sold in larger packages stays the same because the new laws do not
directly affect these transactions.
For California, the reason the Federal crackdown had little effect on the market is likely
simple: it was not seen as a real threat to the long-term sustainability of the trade in the state.
Marijuana is abundant in California, and though the crackdown effort was large, it was targeted
at relatively small and new marijuana shops — with a few exceptions of larger shops in the Bay
Area. The market for marijuana in the state is likely very competitive. It is hard to imagine — in
a city like Los Angeles — one shop being able to survive selling product for above market rates
when a shop down the block sells relatively the same product at a lower price. It could also be
the case that these medical shops compete with the black market: people choose to register and
buy marijuana from shops because it is more convenient and seems more “safe.” For this they
are willing to pay a slight premium — in the form of taxes and operating expenses of owning a
store — to purchase medically. If users have no problem shifting to the black market again given
an increase in medical prices and the widespread availability of dealers, this could explain why
shops did not experience increases in prices due to enforcement — they are price takers and the
enforcement did not change the overall market equilibrium.
Enforcement was also not targeted at growers. In fact, the rate of marijuana plant
eradication dropped dramatically from 2010 to 2011. In California alone there was a 46% decline
in plants eradicated. However, from 2009 to 2011, the rate of bulk marijuana seizures doubled
from 53,843 pounds to 113,167 pounds nationwide (Valdes 2012). Therefore, it could be that the
crackdown had little effect on the price of the product because one way or another marijuana
makes it relatively easily into the hands of the people who demand it, regardless of whether it
44 occurs in one store, a store in a different part of the city, from friends, or from local dealers or
growers.
All this is subject to reasonable doubt though. There are several areas of concern with the
conclusions made about this market. They all have to do with the sample of data used to study
these effects, particularly in Connecticut. The regression results here, though significant, are not
entirely convincing. Looking again at the graphs, the price in all of these places seems highly
variable. It could be the product of sheer coincidence that Connecticut had a few months of
lower prices than its fellow neighboring states. This could have been assisted by the fact that the
sample size in Connecticut was lower than all the surrounding states. Though the sample sizes
used in this paper exceed those used in other drug studies, they are highly variable, in particular
in the early years of the data — from September 2010 until the middle of the data set. Looking
again at the website averages for entries, one can see that the entries skyrocketed in January 2012.
This is perhaps not a random sample of people, and the increase in numbers could have served to
undermine the results attained here. This factor alone could explain why the price was high in
Connecticut prior to the decriminalization: few and perhaps highly un-randomized samples. It
could be this pre-period that explains the subsequent “drop” in prices of marijuana following the
event. Therefore, it is with great caution that we accept the hypothesis that decriminalization
negatively influenced the price. This conclusion stood up to a variety of different measures —
indeed it became more significant as more state and time controls were added — but the data are
potentially problematic given the fact that they are a smaller sample than that of surrounding
states, and the majority of the sample comes from after the event itself, casting doubt that the
price was as high as it appears prior to decriminalization.
45 Another potential problem with the Connecticut disruption is that perhaps the prices in
Connecticut stayed low following decriminalization because of the announcement of a medical
marijuana program to start in the state. This could have influenced perceptions about
enforcement and lowered the price. Maybe the announcement of decriminalization had some
influence on the price as well. This paper relies on the assumption that the market operates
within the legal structure that it currently finds itself, so no attempt was made to isolate
expectations of changes in the legal structure of marijuana.
The results in California are certainly more robust in terms of these sampling problems
that Connecticut faces. California alone had 4,647 entries of high quality data and 4,220 entries
of medium quality data between September 10th, 2010 and November 6th, 2012 and the
crackdown was certainly unexpected and could not have been reasonably anticipated by users.
Given the size of the crackdown and the large availability of data, if there was an effect of
enforcement on the price, it should have been detected. All of the Figures show downward
trending data with a few noisy upswings, but without any cause to think that any major
disruption in the market took place. No matter how you look at it, at the least the crackdown did
not disrupt the end-users who visit this site in their ability to buy marijuana at prevailing or
dropping prices. If we accept that this is a representative sample, this extends to the whole state.
Despite these noisy and potentially un-randomized data, there are more reasons than not
to accept the conclusions made in this paper. This is a larger and more comprehensive set of data
than used previously to make similar conclusions about the market for marijuana. Furthermore,
conclusions were made about marijuana in places where the structure of the market did not
change. In California, the only thing that differed between the enforcement period and the period
prior was the level of focus on busting shop owners. Though enforcement occurred in some
46 neighboring states too, the effort was overwhelmingly in California. In Connecticut, only one
variable changed: the penalties associated with marijuana possession. States that legalized
marijuana — Colorado and Washington — or legalized medical marijuana, were not studied in
this paper since this would have changed the very market structure in which the product is
distributed. The results in this paper rely on stable natural experiments with a change of only one
variable.
Furthermore, in this market, prices are sticky and purchases are made in the dark without
complete knowledge of what other people are paying for similar product in other places. Are the
results noisy because the sample sizes are small, or because this is how the market truly behaves?
In fact, even in states that had large sample sizes, the variances of the results were fairly wide. It
makes sense that the results are noisy and that the decriminalization didn’t result in a dramatic
shock to the price. There are still dangers inherent to transporting an illegal drug across state
lines, and it is not as if end-users face a perfectly competitive market for marijuana. They face
instead localized markets dominated by a few large players that operate through distribution
networks. If the market was perfectly competitive and purchasers had full information and could
choose to buy from other places, then it is likely that these results would be more apparent. At
face value, it seems rather intuitive that the price dropped slightly from decreased enforcement
either directly — through lower sentencing and lower costs associated with possession — or
indirectly — through lower perceived threat of law enforcement efforts to stop distribution.
VIII. Conclusions
By using a large dataset of individual end-user entries on a public website called
Priceofweed.com, two conclusions were drawn: decriminalization of marijuana leads to a drop in
47 the price of marijuana in the black market, and the removal of medical marijuana shops does not
lead to higher prices for marijuana. These have several policy implications.
First and foremost, despite being a potentially good policy to combat state budget deficits
and reduce skyrocketing prison populations in the United States over nonviolent drug offenses,
decriminalization could perhaps be a policy that makes marijuana cheaper. This does not
necessarily mean that there will be more users of the product. As shown by studies mentioned
above, decriminalization does not seem to impact the use of the product. However, policymakers should be aware that this policy does not go without consequences. One of these
consequences could be that marijuana is more available on the black market and thus cheaper
following laws that make the distribution easier and less costly. Another — perhaps beneficial —
result is that if usage does not increase, then the net effect would be to decrease the profits that
drug dealers receive through pedaling the drug in the state.
Secondly, enforcement efforts, if intended to make marijuana more difficult or more
expensive to attain seem to be largely fruitless. Prices in California continued to drop rapidly
while ramped up enforcement was occurring. That such a massive market disruption had no
effect on the price and availability of marijuana is disappointing. It indicates what has been
shown countless other times, that efforts to curb the use of illicit substances based on supply-side
interventions are hardly efficient. Likely because marijuana is so readily available in California,
efforts to stop users from getting it publicly through medical shops do little to the market
equilibrium. Perhaps there are better ways to tackle drug-problems in the U.S.
This paper, because of its timing, encourages further study of the market for marijuana.
What will happen in Colorado and Washington as a result of legalization? Can the results found
in Connecticut be shown to be true in other states decriminalizing marijuana in the coming
48 years? How will the medical market in Massachusetts affect the price? These are all valid
questions going into the future. It also encourages others to create, maintain, and utilize publicly
sourced datasets. These have the advantage of reaching large audiences of people. Though noisy,
through proper construction, they have the power to shed light on previously unobservable
phenomena.
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