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.

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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.

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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

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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.

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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

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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 —

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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.

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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

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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.

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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

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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

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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.

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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

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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) Description Price Amount given in $US Date Day/month/year of purchase (09/14/2010-01/31/2013) Location Nearest town/city, state Quantity 1, 5, 10, 15, 20, or 25 grams; 1/8, 1/4, 1/2, or 1 ounce Quality 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.

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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 self-

reported, 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

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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

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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.

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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

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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.

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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 Mean Std. Dev. Obs Min Max

High $256.50 $95.89 47,501 $20 $450 Medium $149.66 $89.70 38,596 $10 $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

  21  

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-in-

differences 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

  22  

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:

(5) 𝑃!" =  𝛽! +  𝜷𝑬!" +  𝛾!𝐷2!  +  ⋯  +  𝛾!𝐷𝑛!  +  𝛿!𝐵2!  +  ⋯  +  𝛿!𝐵𝑇!  +  𝜀!"

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

  23  

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

  24  

Figure 4 — Average Price Per Ounce of High Quality Marijuana by Region

Figure 5 — Average Price Per Ounce of Medium Quality Marijuana by Region

  25  

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,

  26  

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,

  27  

Table 3 — Effect of Decriminalization on Price Per Ounce of High Quality Marijuana in Connecticut Versus All Regional Controls

(1) (2) (3) (4) Decriminalization Effect -13.187*

(3.404) -14.136**

(1.988) -12.339* (3.541)

-13.345** (1.895)

Event Dummy -18.895** (3.404) -19.743**

(3.541)

Connecticut Dummy 13.430*** (1.413)

13.623** (2.378)

Constant 304.123*** (1.413)

292.489*** (9.626)

317.553*** (8.99e-12)

305.885*** (8.301)

Individual State Dummies No No Yes Yes Monthly Dummies No Yes No Yes Observations 6,152 6,152 6,152 6,152 Regressors 4 27 7 30 R2 .011 .027 .015 .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

  28  

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.

  29  

Table 4 — Effect of Decriminalization on Price Per Ounce of Medium Quality Marijuana in Connecticut Versus All Regional Controls

(1) (2) (3) (4) Decriminalization Effect -13.236

(5.042) -13.780* (4.011)

-12.148* (4.287)

-12.629* (3.328)

Event Dummy 11.324 (5.042) 10.236

(4.287)

Connecticut Dummy 7.887 (8.645)

8.187 (8.387)

Constant 165.390*** (8.645)

170.230*** (8.898)

173.277*** (1.30e-12)

177.414*** (15.255)

Individual State Dummies No No Yes Yes Monthly Dummies No Yes No Yes Observations 4,897 4,897 4,897 4,897 Regressors 4 27 7 30 R2 .003 .007 .018 .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

  30  

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 Medium Quality (>1/2 Ounce) (≤1/2 Ounce) (>1/2 Ounce) (≤1/2 Ounce) Decriminalization Effect -1.619***

(0.185) -0.574 (0.423)

-0.901 (1.982)

-1.871* (0.588)

Constant 28.473*** (0.632)

23.053*** (0.999)

22.837** (3.888)

25.166*** (1.274)

Individual State Dummies Yes Yes Yes Yes Monthly Dummies Yes Yes Yes Yes Observations 2,442 3,710 1,751 3,146 Regressors 30 30 30 30 R2 .055 .035 .025 .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

  31  

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.

  32  

Table 6 — Effect of Decriminalization on Price Per Ounce in Connecticut versus Border states and Effects Using Dynamic Specifications

High Quality Medium Quality

(Border States)

(Dynamic Effects)

(Border States)

(Dynamic Effects)

Decriminalization Effect -11.506* (1.688)

-7.700 (2.580)

(Jul-Sep 2011) -26.825** (4.352)

-8.668 (3.582)

(Oct-Dec 2011) 3.707 (8.971)

-23.991* (6.727)

(Jan-Mar 2012) -27.036** (4.938)

-30.965* (6.827)

(Apr-Jun 2012) -12.677** (1.670)

-3.089 (2.487)

(Jul-Sep 2012) -6.542*** (0.447)

-14.525* (3.922)

Constant 315.142*** (7.853)

303.975*** (8.881)

191.416** (12.403)

178.825*** (15.342)

Individual State Dummies Yes Yes Yes Yes Monthly Dummies Yes Yes Yes Yes Observations 3,937 6,152 3,063 4,897 Regressors 28 34 28 34 R2 .037 .032 .007 .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.

  33  

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.

  34  

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

  35  

Table 7 — Effect of Enforcement on Price Per Ounce of High Quality Marijuana in California Versus All State Controls

(1) (2) (3) (4) Enforcement Effect -10.367***

(2.632) -7.707** (2.828)

-8.036*** (1.802)

-4.953** (1.793)

Event Dummy -22.090*** (2.632) -24.421***

(1.802)

California Dummy -50.556*** (5.599)

-52.467*** (5.658)

Constant 280.964*** (5.599)

272.154*** (7.127)

230.409*** (1.64e-11)

221.905*** (3.176)

Individual State Dummies No No Yes Yes Monthly Dummies No Yes No Yes Observations 36,002 36,002 36,002 36,002 Regressors 4 29 48 74 R2 .057 .063 .127 .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.

  36  

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

(1) (2) (3) (4) Enforcement Effect -21.814***

(2.914) -21.260***

(2.759) -21.766***

(2.766) -21.248***

(2.680) Event Dummy 10.153***

(2.914) 10.104***

(2.766) California Dummy 2.798

(5.180) 2.358

(4.971)

Constant 143.763*** (5.180)

131.538*** (6.551)

146.562*** (9.17e-11)

134.313*** (3.319)

Individual State Dummies No No Yes Yes Monthly Dummies No Yes No Yes Observations 27,733 27,733 27,733 27,733 Regressors 4 29 48 74 R2 .006 .009 .043 .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.

  37  

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

  38  

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 net-

exporter 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

  39  

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

  40  

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 Medium Quality

(Medical States)

(Dynamic Effects)

(Medical States)

(Dynamic Effects)

Enforcement Effect -4.003 (4.332)

-3.215 (11.785)

(Oct 7-Nov 6, 2011) -18.119*** (3.318)

-17.945*** (3.180)

(Nov 7-Dec 6, 2011) -37.565*** (3.314)

-13.971*** (3.707)

(Dec 6, 2011-Jan 6, 2012) -22.200*** (3.313)

-14.303** (4.261)

(Jan 7-Feb 6, 2012) -27.944*** (3.143)

-28.603*** (4.222)

(Feb 7-Mar 6, 2012) -40.353*** (2.640)

-36.489*** (4.178)

(Mar 7-Apr 6, 2012) -10.736*** (3.160)

-20.147*** (4.125)

(Apr 7-May 6, 2012) -2.690 (2.417)

-23.052*** (3.202)

(May 7-Jun 6, 2012) -3.567 (2.584)

-21.654*** (3.552)

(Jun 7-Jul 6, 2012) -2.885 (2.498)

-23.253*** (3.297)

(Jul 7-Aug 6, 2012) 5.439* (2.205)

-21.820*** (2.665)

(Aug 6-Sep 6, 2012) -1.667 (2.169)

-25.286*** (3.228)

(Sep 7-Nov 6, 2012) -0.318 (2.204)

-17.555*** (3.060)

Constant 204.908*** (10.956)

221.897*** (3.178)

130.791*** (4.360)

134.318*** (3.319)

Individual State Dummies Yes Yes Yes Yes Monthly Dummies Yes Yes Yes Yes Observations 8,618 36,002 6,849 27,733 Regressors 32 85 32 85 R2 .091 .135 .016 .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.

  41  

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.

  42  

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 Medium Quality

(Los Angeles) (Bay Area) (Los Angeles) (Bay Area) Enforcement Effect -3.713

(3.140) -4.738 (3.280)

-0.778 (3.093)

10.210*** (1.867)

Los Angeles Dummy 27.114*** (3.174)

8.206* (3.492)

Bay Area Dummy

-8.485 (7.367) -12.178***

(2.817) Constant 175.182***

(9.957) 185.315***

(7.361) 132.573***

(8.359) 136.371***

(7.681) Monthly Dummies Yes Yes Yes Yes Observations 4,647 4,647 4,220 4,220 Regressors 29 29 29 29 R2 .069 .054 .016 .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.

  43  

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

  44  

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

  45  

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.

  46  

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

  47  

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

  48  

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, policy-

makers 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

  49  

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.

  50  

Works Cited

"Crackdown on Marijuana in California." BBC News. BBC, 25 Sept. 2012. Web. 12 Dec. 2012.

"DrugFacts: Marijuana." Drugabuse.gov. National Institute on Drug Abuse, 1 Nov. 2010. Web.

12 Dec. 2012.

"Gov. Malloy on Decriminalization of Marijuana: Common Sense Reforms to Our Criminal

Justice System." Governor Malloy: Press Releases. State of Connecticut, 4 June 2011.

Web. 12 Dec. 2012.

"Marijuana Resource Center: Federal Laws Pertaining to Marijuana." The White House. Office

of National Drug Control Policy, n.d. Web. 12 Dec. 2012.

“Over One Million Patients Likely Using Medical Cannabis In California, CA NORML Study

Estimates.” The NORML Foundation. NORML. 02 June 2011a. Web. 12 Dec. 2012.

"Tables of Model-Based Estimates, State Estimates from the 2005-2006 NSDUH." SAMHSA

OAS. U.S. Department of Health and Human Services, 30 Dec. 2008. Web. 12 Dec.

2012.

Arkes, Jeremy, et al. Why the DEA Stride data are still useful for understanding drug markets.

No. w14224. National Bureau of Economic Research, 2008.

Caputo, M. R. and Ostrom, B. J. Potential Tax Revenue from a Regulated Marijuana Market

A Meaningful Revenue Source. American Journal of Economics and Sociology, (1994).

53: 475–490. doi: 10.1111/j.1536-7150.1994.tb02619.x

Caulkins, Jonathan P., and Peter Reuter. 1998. “What Price Data Tell Us About Drug Markets.”

Journal of Drug Issues, 28(3): 593–613.

Caulkins, Jonathan P., and Rema Padman. 1993. “Quantity Discounts and Quality Premia for

Illicit Drugs.” Jounral of the American Statistical Association. Vol. 88, No. 423: 748-757.

  51  

Chaloupka, Frank J., and Adit Laixuthai. "Do youths substitute alcohol and marijuana? Some

econometric evidence." Eastern Economic Journal 23.3 (1997): 253-276.

Clements, Kenneth W. “Pricing and Packaging: The Case of Marijuana.” The Journal of

Business, Vol. 79, No. 4 (July 2006), pp. 2019-2044.

Dickinson, Tim. "Obama's War on Pot." Rolling Stone. Wenner Media, 16 Feb. 2012. Web. 12

Dec. 2012.

DiNardo, John. “Law Enforcement, the Price of Cocaine and Cocaine Use.” Mathematical

and Computer Modelling, (1993). 17(2): 53–64

Dobkin, Carlos, and Nancy Nicosia. "The war on drugs: methamphetamine, public health, and

crime." The American economic review 99.1 (2009): 324.

Graves, Lucia. "Medical Marijuana Raids To Continue After House Defeats Defunding Bill."

The Huffington Post. TheHuffingtonPost.com, 10 May 2012. Web. 12 Dec. 2012.

Grim, Ryan. "Holder Vows To End Raids On Medical Marijuana Clubs." The Huffington Post.

TheHuffingtonPost.com, 26 Feb. 2009. Web. 12 Dec. 2012.

Harry G. Levine, Jon B. Gettman, Loren Siegel. "Arresting Blacks for Marijuana in California:

Possession Arrests, 2006-08.” Drug Policy Alliance, LA: October 2010, p. 13.

Horowitz, Joel L. "Should the DEA's STRIDE data be used for economic analyses of markets for

illegal drugs?." Journal of the American Statistical Association 96.456 (2001):1254-1271.

L. Johnson et al. Marijuana Decriminalization: The Impact on Youth 1975-1980. Monitoring the

Future, Occasional Paper Series: Paper No. 13. Institute for Social Research, University

of Michigan. 1981.

Manski, Charles F., John Pepper, and Carol Petrie, eds. Informing America's policy on illegal

drugs: what we don't know keeps hurting us. National Academies Press, 2001.

  52  

Miron, Jeffrey A. “The Effect of Drug Prohibition on Drug Prices: Evidence from the

Markets for Cocaine and Heroin.” Review of Economics and Statistics, (2003). 85(3):

522–30.

Miron, Jeffrey A., and Jeffrey Zwiebel. Alcohol consumption during prohibition. No. w3675.

National Bureau of Economic Research, 1991.

Montgomery, Michael. "California Marijuana Prices Rising In Wake Of Federal Crackdown."

The Huffington Post. TheHuffingtonPost.com, 12 Dec. 2011. Web. 12 Dec. 2012.

Onishi, Norimitsu. “Cities Balk as Federal Law on Marijuana Is Enforced.” The New York Times

Company. New York Times. 30 June 2012a. Web. 12 Dec. 2012.

Onishi, Norimitsu. “Marijuana Only for the Sick? A Farce, Some Angelenos Say.” The New York

Times Company. New York Times, 7 Oct. 2012b. Web. 12 Dec. 2012.

Sheck, Justin. “City Fights For Marijuana Dispensary.” Dow Jones & Company. The Wall Street

Journal, 11 Oct. 2012. Web. 12 Dec. 2012.

Valdes, Manuel. “Marijuana Plants Eradicated At Lower Rates In Most States Over Past Three

Years.” The Huffington Post. TheHuffingtonPost.com, 03 Aug. 2012. Web. 07 Apr. 2013.

Yuan, Yuehong, and Jonathan P. Caulkins. “The Effect of Variation in High-level

Domestic Drug Enforcement on Variation in Drug Prices.” Socio-Economic Planning

Services (1998). 32(4): 265–76.

Zook, Matthew, Mark Graham, and Monica Stephens. “Data Shadows of an Underground

Economy: Volunteered Geographic Information and the Economic Geographies of

Marijuana.” Floating Sheep Working Paper (2011).