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Crime and the legalization of recreational marijuana

Crime and the legalization of recreational marijuana
Davide DragoneGiovanni PraroloPaolo VaninGiulio Zanella∗†
January 31, 2017
We provide first-pass evidence that the legalization of the cannabis market across US
states may be inducing a crime drop. Exploiting the recent staggered legalization en-
acted by the adjacent states of Washington (end of 2012) and Oregon (end of 2014) we
find, combining county-level difference-in-differences and spatial regression discontinu-
ity designs, that the legalization of recreational marijuana caused a significant reduction
of rapes and thefts on the Washington side of the border in 2013-2014 relative to the
Oregon side and relative to the pre-legalization years 2010-2012. We also find evidence
that the legalization increased consumption of marijuana and reduced consumption of
other drugs and both ordinary and binge alcohol.
Keywords: cannabis, recreational marijuana, crime
JEL codes: K23, K42
University of Bologna, Department of Economics. Piazza Scaravilli 2, 40126 Bologna BO, Italy
Corresponding author. E-mail:
1 Introduction
Gary Becker was a strong advocate of the legalization of drugs (Becker and Murphy, 2013),
particularly — in the wake of the first wave of legalization of recreational cannabis in the
US — of marijuana (Becker, 2014). Becker and Murphy (2013) claimed that the largest
costs of a prohibitionist approach to buying and selling drugs in the US “are the costs of
the crime associated with drug trafficking”, predicting that legalizing this market would
“reduce the role of criminals in producing and selling drugs [and] improve many inner-city
neighborhoods”: “Just as gangsters were largely driven out of the alcohol market after the
end of prohibition, violent drug gangs would be driven out of a decriminalized drug market”.
That is, letting the drug market emerge from illegality would make illegal activities in this
market not pay, thus greatly reducing fertile ground for crime, a central theme in Becker’s
economic approach to crime (Becker, 1968).
The present paper provides evidence in favor of these conjectures exploiting the full
legalization of the cannabis market recently enacted by some states in the US. Although
possessing, using, selling and cultivating marijuana is illegal under US federal law,1between
2012 and 2016 eight states have legalized recreational marijuana: Colorado and Washington
in 2012, Alaska and Oregon in 2014, California, Nevada, Maine and Massachusetts in 2016.2
The comparison between Washington (WA) and Oregon (OR) offers an experimental oppor-
tunity to study the effect of such legalization on crime because these are neighboring (hence
similar, in many respects) states that legalized cannabis for recreational use at about the
same time, but with a 2-year time lag that induces a quasi-experiment, and sufficiently early
to allow the observation of crime rates for at least two years from official sources. Combin-
ing difference-in-differences (DID) and spatial regression discontinuity (SRD) designs at the
county level to identify the causal impact of the legalization of cannabis for recreational use
on crime rates we find that the legalization reduced rapes by about 4 per 100,000 inhabitants
1Except for restricted uses, cannabis has been illegal under US federal law since the Marihuana Tax Act
of 1937. The Controlled Substance Act of 1970 (Title II of the Comprehensive Drug Abuse Prevention and
Control Act, Public Law 91-513) classified marijuana and tetrahydrocannabinols among the drugs listed in
Schedule I, which have high potential for abuse and no accepted medical value.
2Many more states have passed medical marijuana laws. These, however, do not legalize the supply side
of the market. Making marijuana legal for recreational purposes is the strongest form of legalization of the
cannabis market.
(a 30% drop), and thefts by about 100 per 100,000 inhabitants (a 20% drop ).
These results support Becker and Murphy’s conjectures, and are also in line with two
possible reasons that have been suggested for why illicit drugs may increase crime (Goldstein,
1985): stealing to buy expensive drugs, and drug wars within the system of drug distribution.
However, they stand in sharp contrast with the presumption that drugs cause crime, a major
argument in support of a prohibitionist approach to substance use. For instance, according
to the California Police Chiefs Association (2009), “public officials and criminal justice or-
ganizations who oppose medical marijuana laws often cite the prospect of increased crime”.
Case studies of crime reports found drugs to be, in fact, a contributing factor (Goldstein,
1985), and it has been observed that a higher percentage of persons arrested test positive
for illicit drugs compared with the general population (US Department of Justice). Yet,
research on the recent wave of legalization of cannabis for medical use (“medical marijuana
laws”, MML henceforth) in the US yields mixed results on the association between illicit
drug use and crime. Some researchers find no significant relationship between MML and
crime (Keppler and Freisthler, 2012; Braakman and Jones, 2014; Morris et al., 2014; Freisth-
ler et al., 2016; Shepard and Blackley, 2016), while others show that MML may reduce some
kind of non-drug crimes (Ingino, 2015) because of reduced activity by drug-trafficking orga-
nizations (Gavrilova et al., 2014). Using data from the UK, Adda et al. (2014) argue that
the decriminalizing marijuana allows the police to reallocate effort away from drug-related
crimes and towards other types of offenses. However, the estimation of a causal effect going
from legalizing cannabis to crime rates remains an elusive question because of the lack of
an experimental design (Miron, 2004). The present paper makes progress in this respect
by engineering a quasi-experiment that is able to provide first-pass causal evidence on the
relationship between recreational cannabis and crime rates.
At this level of analysis we cannot pin down the mechanisms operating behind the effects
we identify. Moving retail cannabis deals from degraded streets to safe, legal shops most
likely played a role. Anecdotal evidence is provided by this message posted on Twitter
by the Portland Police on June 10, 2016: “If you are looking to buy marijuana, go to a
legit business and avoid street dealers who might rob you”. Substitution away from drugs
which have remained illegal and from alcohol which makes consumers more aggressive than if
consuming cannabis is another possibility for which we provide evidence via a complementary
analysis that uses substance consumption as an outcome. We find that the legalization of
recreational marijuana in Washington induced an increase in the consumption of cannabis of
about 2.5 percentage points (off a base level of about 10%), a decrease in the consumption
of other drugs of about 0.5 points (off a base level of about 4%), and a decrease in the
consumption of both ordinary alcohol and binge alcohol of about 2 points (off base levels
of about 50% and 20%, respectively). Finally, the police reallocation channel suggested
by Adda et al. (2014) is certainly a plausible mechanism. We expand on mechanisms in
the concluding Section of the paper. In the next one, we summarize the legal details that
generate our quasi-experiment. The data and the results are presented in Section 3.
2 Legal framework
At the general election ballot of November 2012, voters in the state of WA approved with
about 56% of votes Initiative 502, which allows producing, processing, and selling cannabis,
subject to licensing and regulation by the Liquor Control Board, allows limited possession
by persons aged 21 and over (but not home cultivation), and taxes sales. Legal possession
began on December 9, 2012. Regulations for producers, processors and sellers were approved
in 2013 and retail sales of recreational cannabis began July, 8 2014 (Darnell, 2015). Shortly
after, the state of OR passed a similar reform. At the November 2014 general election
ballot, voters in OR approved with about 56% of votes Measure 91, a cannabis law reform
that is similar to the one passed in WA in terms of taxing sales and subjecting them to
regulation and licensing by the Liquor Control Commission, but is more permissive in terms
of possession and cultivation.3A previous legalization attempt in OR (Measure 80 of 2012),
quite permissive in terms of regulation and oversight, was marginally rejected with around
53% of votes in November 2012, thus enhancing the comparability with WA. Legalization of
possession, use and home cultivation started in OR in July 2015, recreational sales through
medical dispensaries in October 2015, and retail store licenses began in October 2016.
3Home cultivation of up to four plants per household is allowed. Adults over the age of 21 are allowed to
carry 1 ounce and keep 8 ounces at home, whereas WA establishes a possession limit of 1 ounce.
Therefore, the timing of the reforms was such that cannabis was legal on one side of the
border two years before the other side. Specifically, in 2013 and 2014 cannabis was legal in
WA but not in OR, a temporary 2-year window followed by a virtually identical legal status
across the border between two similar states where voters had a similar attitude towards
legalizing cannabis. This allows us to combine a difference-in-differences (DID) design (where
WA acts as the treatment group, OR as the control group, 2010-2012 is the pre-legalization
period and 2013-2014 is the post-legalization period) and a spatial regression discontinuity
(SRD) design (where the WA-OR border marks a discontinuity in the legal status of cannabis
in 2013-2014) to identify the causal impact of legal cannabis on violent and property crime.
Even after the legalization, there are counties in WA where cannabis business is pro-
hibited or where, according to the WA Liquor Control Board, Marijuana Sales Activity
by License Number, no recreational cannabis retailers are present. These are Columbia,
Franklin, Garfield, Wahkiakum, and Walla Walla County, all of them bordering Oregon ex-
cept Franklin County. We show later that our results are robust to excluding these counties
from the analysis.
A potential confounding factor in our analysis is that other relevant legal or institutional
changes affecting crime rates in WA may have taken place in 2013-2014. A search for such
changes reveals no relevant events that may have affected crime rates at the same time as the
legalization of cannabis possession and use. During this period, a reorganization of the 911
emergency call system took place in WA, and there were reforms related to health services,
regulation of wine and beer, and drug courts. There were also changes in the statute of
limitations for child molestation, incest (victim under age eighteen), and rape (victim under
age eighteen), as well as new norms concerning commercial sale of sex and commercial sexual
abuse, sexually violent predators, and sexual violence at school. However, all of these changes
were too marginal to exert a plausible first-order effect on crime.
3 Data and results
We employ data on criminal activity at the county level from the US Uniform Crime Re-
porting (UCR) statistics. The data base contains the number of offenses reported by the
sheriff’s office or county police department. For the reasons detailed below, these are not
necessarily the county totals, but they are the only publicly available information from the
UCR at the county level of disaggregation. We collected these crime data for years 2010
to 2014. For each county and each year, we have the total number of reported offenses for
murder, rape, assault, robbery, burglary, and theft. The final dataset is an unbalanced panel
(since not all counties report crime data every year) consisting of 335 observations for 75
counties, 36 in OR and 39 in WA. County-level population from the 2010 Census is used
to obtain crime rates per 100,000 inhabitants. The distance of each county’s centroid from
the WA-OR border is computed using a GIS software. Table 1 reports crime rates in WA
and OR counties between 2010 and 2014: all counties at the top of the table, counties at
the WA-OR border (where our comparison takes place) at the bottom. Because these rates
result from the aggregation of county-level reports in the UCR, they do not necessarily co-
incide with state-level counts. The reason of the discrepancy is twofold, as explained by the
FBI’s Criminal Justice Information Services Division at the UCR website. First, “only data
for city law enforcement agencies 10,000 and over in population and county law enforcement
agencies 25,000 and over in population are on this site”. That is, crimes occurring in smaller
cities are not counted for the published county-level totals. Second, “Because not all law
enforcement agencies provide data for complete reporting periods, it is necessary to estimate
for the missing data” when building statistics beyond the county level of aggregation. That
is, the FBI imputes crime counts to non-reporting agencies when building estimates at the
state and nation levels.
In addition, we employ data from the National Survey on Drug Use and Health (NSDUH)
to include in our analysis information on substance consumption. Such information may shed
some light on competing channels in the explanation of our results. Specifically, we pulled
from the NSDUH the rates of use over the previous month for marijuana, other Federal
illicit drugs, and alcohol. These statistics are publicly available only as averages over the
2010-2012 and 2012-2014 periods. Fortunately, these roughly correspond to the “pre” and
“post” periods in our DID-SRD analysis.4Table 2 reports these consumption rates for the
4For smaller counties the NSDUH data come as aggregates for larger units consisting of groups of
neighboring counties. In these cases, each county in the group is imputed the group-level average rate of
Table 1: Crime rates at the county level
Year Murder Rape Assault Robbery Burglary Theft
All WA counties (N= 39)
2010 0.76 10.96 46.66 12.17 265.79 458.97
2011 0.85 9.65 40.84 10.30 265.08 440.87
2012 1.03 9.16 42.70 9.99 287.77 432.55
2013 0.80 9.07 41.23 9.21 258.73 419.59
2014 0.73 9.70 41.21 10.47 246.90 399.60
All OR counties (N= 36)
2010 0.80 7.22 34.31 6.82 132.96 393.71
2011 0.66 7.26 32.02 6.26 142.14 387.37
2012 0.84 7.51 29.31 6.75 150.93 412.93
2013 0.88 5.69 22.48 5.40 146.14 433.22
2014 0.66 7.22 30.21 4.72 115.17 335.12
Border WA counties (N= 11)
2010 0.35 15.37 33.69 8.51 224.00 529.80
2011 0.48 13.56 33.55 9.69 212.19 491.00
2012 0.75 12.80 42.00 7.58 223.30 445.11
2013 0.59 10.28 40.78 6.15 210.41 407.93
2014 0.71 10.52 39.48 6.97 184.76 357.10
Border OR counties (N= 10)
2010 0.34 1.58 13.40 3.04 41.88 163.57
2011 0.44 2.51 11.22 1.31 49.15 158.78
2012 0.31 2.59 10.76 1.14 56.88 176.11
2013 0.10 1.77 11.67 1.67 41.04 144.27
2014 0.11 0.91 14.89 2.39 40.91 128.08
Notes: Average crimes per 100,000 inhabitants in WA and OR counties, estimated from the county-level
counts reported in the Uniform Crime Reporting Statistics. The averages are weighted by county population.
Table 2: Substance Consumption rates at the county level
Year Marijuana Other drugs Alcohol Binge alcohol
All WA counties (N= 39)
2010-2012 0.102 0.044 0.560 0.222
2012-2014 0.127 0.039 0.542 0.206
All OR counties with consumption data (N= 34)
2010-2012 0.112 0.042 0.596 0.214
2012-2014 0.122 0.040 0.579 0.213
Border WA counties (N= 11)
2010-2012 0.093 0.042 0.535 0.223
2012-2014 0.101 0.034 0.486 0.199
Border OR counties (N= 10)
2010-2012 0.145 0.050 0.630 0.238
2012-2014 0.130 0.043 0.600 0.233
Notes: Average rates of substance use in WA and OR counties, estimated from the rates reported in the
National Survey on Drug Use and Health. The averages are weighted by county population.
same WA and OR counties used in Table 1.
Four features of our data are crucial for identification. First, WA and OR share similar
geographic, economic and institutional characteristics, including (quite crucially) a similar
attitude towards legal cannabis (see Section 2). Second, WA legalized the cannabis market
at the end of 2012, and OR (despite an attempt to legalize in that same year, marginally
failed) in 2014, which results in a 2-year period in which recreational cannabis is legal on one
side of the border and illegal on the other side. Third, the longitudinal dimension of the data
allows us to condition on county fixed effects and time effects, thus netting out unobserved
local characteristics that do not change over time, as well as those factors that vary over
time but are common to all counties. Fourth, the geographical features of the data allow us
to identify the effect of the policy at the WA-OR border, where treated and control counties
offer a better comparison: arguably, the similarity between two different states is maximized
when comparing bordering counties. Moreover, by conditioning on distance from the border
and by allowing for different effects of the spatial gap before and after the legalization, the
SRD design controls for the effect of distance from the border on crime rates, including
possible spillovers due to cross-border activity in response to the different legal status of
Preliminary graphical evidence about the causal effect of interest is offered in Figure 1.
The figure plots nonparametric estimates of the difference between county-level crime rates
before (2010-2012) and after (2013-2014) the WA legalization, as a function of the distance
(measured in hundreds of kilometers) of the county centroid from the WA-OR border. In
each panel of Figure 1, the difference between the variations in crime rates at the border (i.e.,
the jump at zero distance) is therefore a nonparametric estimate of the effect of legalizing
cannabis. Except for murders (for which the variation is essentially zero on both sides of
the border) and assaults, the drop in crime on the WA side of the border is much larger
than the corresponding drop on the OR side. Figure 2 illustrates the analogous evidence for
Figure 1: Variation in crime between before and after the WA legalization
-1 0 1 2
-4 -3 -2 -1 0 1 2 3 4
-6 -4 -2 0 2
-4 -3 -2 -1 0 1 2 3 4
-20 -10 0 10 20
-4 -3 -2 -1 0 1 2 3 4
-3 -2 -1 0 1 2
-4 -3 -2 -1 0 1 2 3 4
-40 -20 0 20 40 60
-4 -3 -2 -1 0 1 2 3 4
-200 -100 0 100 200
-4 -3 -2 -1 0 1 2 3 4
Notes: Variation in county-level crimes per 100k inhabitants (vertical axis) as a function of the distance of the county centroid
from the OR-WA border measured in hundreds Km (horizontal axis). A positive distance means that the county is located in
WA, and a negative distance means that the county is located in OR. The jump at zero distance is a non-parametric DID-SRD
estimate of the effect of the legalization policy on crime. The lines are smoothed county-level differences in crime rates obtained
from local linear regressions, weighted by county population, employing a triangular kernel and a bandwidth of 100 Km.
Figure 2: Variation in consumption between before and after the WA legalization
-.02 0 .02 .04 .06
-4 -3 -2 -1 0 1 2 3 4
marijuana consumption
-.01 -.008-.006-.004-.002 0
-4 -3 -2 -1 0 1 2 3 4
other drugs consumption
-.06 -.04 -.02 0
-4 -3 -2 -1 0 1 2 3 4
alcohol consumption
-.03 -.02 -.01 0 .01
-4 -3 -2 -1 0 1 2 3 4
binge alchool
Notes: Variation in county-level rates of use of substances (vertical axis) as a function of the distance of the county centroid
from the OR-WA border measured in hundreds Km (horizontal axis). A positive distance means that the county is located in
WA, a negative distance means that it is located in OR. The jump at zero distance is a non-parametric DID-SRD estimate of
the effect of the legalization policy on consumption. The lines are smoothed county-level differences in crime rates obtained
from local linear regressions, weighted by county population, employing a triangular kernel and a bandwidth of 100 Km.
To provide a more formal statistical analysis, we employ a parametric model that allows us
to condition on unobserved county and time effects. Let cit be the crime rate in county iand
year t, and define the following binary variables: first, wi= 1 if county iis located in WA
(treatment), and wi= 0 if county iis located in OR (control); second, pt= 1 if year t > 2012
(post), and pt= 0 if year t2012 (pre). The DID-SRD design, sometimes referred to as
the Difference-in-Spatial-Discontinuity design (Dickert-Conlin and Elder, 2010; Gagliarducci
and Nannicini , 2013) can be represented by the following model:
cit =k+αpt+βwipt+f(di)pt+g(di)wipt+θi+ξit ,(1)
where kis a constant, f(.) and g(.) are polynomials of the same order (but possibly different
coefficients) in distance difrom the WA-OR border, θiare county fixed effects, and ξit are
residual determinants of crime. Coefficient βis the difference in the SRD estimates between
the pre and post periods, i.e., by how much liberalizing recreational cannabis in WA changed
the difference in crime rates right across the WA-OR border. We estimated Eq. (1) by OLS,
employing quadratic polynomials in distance as is appropriate in a parametric framework
(Gelman and Imbens, 2014). The resulting estimates of βare reported in Table 3.
Table 3: Effect of recreational cannabis on crime
Murder Rape Assault Robbery Burglary Theft
Estimated β0.23 –4.21** –1.30 –1.26 –36.32 –105.62*
(0.45) (1.26) (8.79) (1.92) (22.20) (40.21)
Observations 335 335 335 335 335 335
Notes: The table reports estimates of βfrom OLS on Equation 1, a coefficient that represents the difference in
the spatial regression discontinuity estimates between the pre and post periods, i.e., by how much liberalizing
recreational cannabis in WA changed the difference in crime rates right across the WA-OR border. Ordinary
standard error are reported in parentheses (robust standard errors clustered at the county level are smaller
than the ordinary ones displayed here). Each county is weighted in the regression based on the size of its
population in the 2010 Census. Significance level: * 5%; ** 1% or better.
There is evidence in this table that the legalization of recreational cannabis enacted in
WA caused a decrease in crime rates. The point estimates for rape, assault, robbery, burglary
and theft are all negative. This conclusion is reinforced by the statistical significance of the
drop in rapes (p-value = 0.001) and thefts (p-value = 0.01). For rapes, the reduction is 4.2
offenses per 100,000 inhabitants, which is about 30% of the 2010-2012 rate. For thefts, the
reduction is 105.6 offenses per 100,000 inhabitants, which is about 20% of the 2010-2012
rate.5Note that the parametric estimates of βin Table 3are in the same ballpark of the
jump at zero-distance in Figure 1(except for burglaries). This indicates that our parametric
choices are not driving the results.
As a robustness check, we re-estimate the DID-SRD model after excluding 5 WA counties
where cannabis business is prohibited and where, according to the Liquor Control Board,
Marijuana Sales Activity by License Number, no non-medical cannabis retailers are present.
These are Columbia, Franklin, Garfield, Wahkiakum, and Walla Walla County, all of them
bordering Oregon except Franklin County. Results are reported in Table 4 . These confirm
negative point estimates for all of the categories considered, and significant drops in rapes
and thefts.
The analogous estimates using consumption as an outcome are reported in Table 5. Our
DID-SRD estimates reveal that the legalization increased consumption of cannabis by about
2.5 percentage points (off a base level of about 10%), decreased in the consumption of other
5Although the point estimate for murders is positive, it is imprecise and not statistically significant.
drugs by about 0.5 points (off a base level of about 4%), and decreased consumption of both
ordinary alcohol (in a marginally significant way) and binge alcohol of about 2 points (off
base levels of about 50% and 20%, respectively). These effects on consumption suggest that
one of the mechanisms underlying the reduction in crime may be a substitution away from
other drugs which have remained illegal substances, such as alcohol, which makes consumers
more aggressive than if consuming cannabis. We expand on this point in the next section.
Table 4: Effect of recreational cannabis on crime: robustness check
Murder Rape Assault Robbery Burglary Theft
Estimated β0.20 –3.77** –0.36 –1.19 –41.84 –117.51**
(0.49) (1.49) (9.14) (2.04) (25.40) (39.67)
Observations 310 310 310 310 310 310
Notes: The table reports estimates of βfrom OLS on Equation 1, a coefficient that represents the difference in
the spatial regression discontinuity estimates between the pre and post periods, i.e., by how much liberalizing
recreational cannabis in WA changed the difference in crime rates right across the WA-OR border. WA
counties are excluded were cannabis business is prohibited and where, according to the Liquor Control
Board, Marijuana Sales Activity by License Number, no non-medical cannabis retailers are present. These
are Columbia, Franklin, Garfield, Wahkiakum, and Walla Walla County, all of them bordering Oregon except
Franklin County. Ordinary standard error are reported in parentheses (robust standard errors clustered at
the county level are smaller than the ordinary ones displayed here). Each county is weighted in the regression
based on the size of its population in the 2010 Census. Significance level: + 10%; * 5%; ** 1% or better.
Table 5: Effect of recreational cannabis on consumption
Marijuana Other drugs Alcohol Binge alcohol
Estimated β0.025** –0.005** –0.023+–0.020**
(0.009) (0.001) (0.014) (0.007)
[0.016] [0.002] [0.016] [0.010]
Observations 135 135 135 135
Notes: The table reports estimates of βfrom OLS on Equation 1when measures of consumption are used
as an outcome, a coefficient that represents the difference in the spatial regression discontinuity estimates
between the pre and post periods, i.e., by how much liberalizing recreational cannabis in WA changed
the difference in consumption right across the WA-OR border. Ordinary standard error are reported in
parentheses, and robust standard errors clustered at the county level are reported in brackets. Each county
is weighted in the regression based on the size of its population in the 2010 Census. Significance level: * 5%;
** 1% or better.
4 Concluding remarks
Our analysis of the causal effects on crime of the legalization of cannabis for recreational use
reaches conclusions in line with what Becker and Murphy (2013) expected when advocating
the full decriminalization of the drugs market, namely a crime drop. What are the possible
possible channels through which legalizing the production and sales of cannabis affects crim-
inal behavior? The effects may work through a change in market price and market structure,
as well as through institutional changes.
First, the policy leads to the emergence of a legal market, which offers more safety and
more reliable product quality. It thus reduces the risk of being victimized while buying,
the risk of being sanctioned, search costs (especially for first-time buyers), as well as the
psychological unease possibly related to purchasing an illegal product. From the consumer’s
point of view, this amounts to a reduction in quality-adjusted relative prices. Moreover,
retail prices should be expected, on average, to drop when the market is legalized due to a
corresponding lower risk on the supply side. Provided that cannabis is a normal good, a price
reduction should lead to an increase in its consumption, which is what we find analyzing
consumption data. Such increase may take place both at the extensive and intensive margin:
the number of consumers may increase and existing ones may consume more. Since cannabis
use determines a variety of psychoactive effects, which include a state of relaxation and
euphoria (Hall et al., 2001; Green et al, 2003), an increase in consumption may reduce the
likelihood of engaging in violent activities. This would hold, in particular, if cannabis is a
substitute for violence-inducing substances such as alcohol, cocaine and amphetamines.
Interestingly, the evidence is mixed in this respect. Some studies find that marijuana and
alcohol are substitutes (Anderson, Hansen, and Rees 2014; Crost and Guerrero 2012; Kelly
and Rasul, 2014; DiNardo and Lemieux, 2001), while others find that they are complements
(Williams et al., 2004; Wen et al., 2014). As observed in Sabia et al. (2016), who study the
effects of MML on body weight and health, the substitutability/complementarity between
alcohol and marijuana seems to be heterogeneous, depending on age.
Our results are in line with Gavrilova et al. (2016), who find that in US states bordering
Mexico the introduction of MML leads to a decrease in violent crimes such as homicides,
aggravated assaults and robberies, and that this reduction in crime rates is mainly due to
a drop in drug-law and juvenile-gang related homicides. The introduction of MML is found
to reduce the violent crime rate in Mexican-border states by 15-25 percent. This is a large
effect, but it is fully compatible with our estimates on the impact of recreational marijuana.
Besides directly affecting cannabis price and consumption, legalizing cannabis also changes
market structure. Entry of new legal sellers, who provide better quality than illegal com-
petitors, may drive the latter out of the market. Some illegal dealers might survive if legal
consumption is severely taxed, and they will surely survive during the time it takes to open
legal dispensaries. Yet, one may expect their profitability to fall – certainly their expected
future profits do. One reason is the increase in competitive pressure. Another one is that
product quality is not only likely to be higher in the legal part of the market, but it is
presumably also easier to identify, so that legalization might in principle introduce price
divergence: prices might increase in the legal relative to the illegal part of the market. The
likely result is an increase in average product quality and market exit by illegal suppliers.
This change in market structure is likely to reduce the presence of drug-trafficking criminal
organizations, together with drug-related conflicts and associated crimes. Yet, we do not
really know what previous dealers do after legalization, so this argument remains necessarily
incomplete. Moreover, one might be concerned that even legal dispensaries attract criminals,
e.g., to steal cash or marijuana. Yet, this concern is mitigated by the fact that dispensaries
may take measures to reduce crime and increase guardianship, such as doormen or video
cameras (Kepple and Freisthler, 2012). What seems more obvious is that the legalization
may not just affect the behavior of potential offenders, but also of potential victims. The
availability of cannabis through legal channels arguably makes consumers substantially less
willing to take risks in the illegal market. This might also contribute to explain the drop in
assaults, robberies and thefts that we document.
On top of altering behavior through changes in the cannabis market, legalization may
also generate a reallocation of police efforts. A lower rate of drug-related crimes opens the
possibility for the police to divert resources toward preventing non-cannabis related crimes,
as shown by Adda et al. (2014) for the decriminalization of possession of small quantities
of cannabis in London, UK. Interestingly, such reallocation may be driven by expectations,
and therefore need not wait for the actual opening of new dispensaries.
Summing up, the WA-OR quasi-experiment provides first-pass evidence that legalizing
cannabis may well cause a drop in crime. What we estimate is the short-run response. As
new data become available over time, for these states as well as for the other ones that
legalized in 2016, it will be possible to appropriately distinguish between short and long-run
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We are currently witnessing a shift in the approach to combat traffic and consumption of illegal harmful drugs, being cannabis legalization a prominent example. In this paper, we study how to optimally regulate the market for cannabis, in a setting where consumers differ in their utility from consumption of the psychoactive component of cannabis, THC, and suffer from misperception of the health damage it causes. We analyze this problem through a vertical differentiation model, where a black market firm and a public firm compete in prices and qualities (THC content). A paternalistic government would like to correct for the misperceived health damage caused by cannabis consumption, as well as to reduce the size of the black market. It is the undesirability of black market profits what explains that the first-best allocation cannot be decentralized. We find two possible equilibria, depending on whether the public firm serves those consumers with the highest or lowest willingness to pay for quality. Paradoxically, when the public firm serves those consumers with higher taste for THC, a lower average health damage is achieved together with a better economic result for the public firm.
We estimate the effects of legalized recreational marijuana on entry into the foster‐care system. Exploiting state‐level variation in legalization and its timing, we estimate that legalization decreases foster‐care placements by at least 10%, with larger effects in years after legalization, and for admissions for reasons of parental drug and alcohol abuse, physical abuse, neglect, and parental incarceration. Our findings imply that legalization may have important consequences for child welfare, and that substitution toward marijuana from other substances can be an important part of how legalization affects admissions.
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Purposeof Review Epidemiologic research on the health effects of social policies is growing rapidly because of the potentially large impact of these policies on population health and health equity. We describe key methodological challenges faced in this nascent field and promising tools to enhance the validity of future studies. Recent Findings In epidemiologic studies of social policies, causal identification is most commonly pursued through confounder-control but use of instrument-based approaches is increasing. Researchers face challenges measuring relevant policy exposures; addressing confounding and positivity violations arising from co-occurring policies and time-varying confounders; deriving precise effect estimates; and quantifying and accounting for interference. Promising tools to address these challenges can enhance both internal validity (randomization, front door criterion for causal identification, new estimators that address interference and practical positivity violations) and external validity (data-driven methods for evaluating heterogeneous treatment effects; methods for transporting and generalizing effect estimates to new populations). Summary Common threats to validity in epidemiologic research play out in distinctive ways in research on the health effects of social policies. This is an active area of methodologic development, with ongoing advances to support causal inferences and produce policy-relevant findings. Researchers must navigate the tension between research questions of greatest interest and research questions that can be answered most accurately and precisely with the data at hand. Additional work is needed to facilitate integration of modern epidemiologic methods with econometric tools for policy evaluation and to increase the size and measurement quality of datasets.
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Objectives Since 2012, the production, sale, and use of marijuana and its derivatives for recreational consumption have been legalized in 15 states and Washington, DC, but remain controversial nationwide. Critics argue that marijuana retail outlets attract criminal activity in surrounding areas and promote social disorganization. This paper examines this issue by analyzing secondary data from Washington State, one of the first states to legalize the medical and recreational use of marijuana.Methods Using geocoded police incident reports as a proxy for criminal events, retail outlet licensing/sales data, and other contextual data, we use a difference-in-difference and other quasi-experimental techniques to model whether the opening of a recreational outlet affects crime in the surrounding neighborhoods, specifically in the cities of Seattle, Bellevue, and Tacoma.ResultsThe analyses find modest but statistically significant increases in property crime in Census block groups containing new retail stores.Conclusions The findings from this study are consistent with some previous research that finds a link between marijuana outlets opening and an increase in neighborhood crime, but additional research is needed to further explore the association, for instance, in other states that have legalized recreational marijuana.
Background and Aims In the United States, 15 states and the District of Columbia have implemented recreational cannabis laws (RCLs) legalizing recreational cannabis use. We aimed to estimate the association between RCLs and street prices, potency, quality and law enforcement seizures of illegal cannabis, methamphetamine, cocaine, heroin, oxycodone, hydrocodone, morphine, amphetamine and alprazolam. Design We pooled crowdsourced data from 2010–19 Price of Weed and 2010–19 StreetRx, and administrative data from the 2006–19 System to Retrieve Information from Drug Evidence (STRIDE) and the 2007–19 National Forensic Laboratory Information System (NFLIS). We employed a difference‐in‐differences design that exploited the staggered implementation of RCLs to compare changes in outcomes between RCL and non‐RCL states. Setting and cases Eleven RCL and 40 non‐RCL US states. Measures The primary outcome was the natural log of prices per gram, overall and by self‐reported quality. The primary policy was an indicator of RCL implementation, defined using effective dates. Findings The street price of cannabis decreased by 9.2% [β = −0.092; 95% confidence interval (CI) = −0.15–, –0.03] in RCL states after RCL implementation, with largest declines among low‐quality purchases (β = −0.195; 95% CI = –0.282, –0.108). Price declines were accompanied by a 93% (β = −0.93; 95% CI = –1.51, –0.36) reduction in law enforcement seizures of cannabis in RCL states. Among illegal opioids, including heroin, oxycodone and hydrocodone, street prices increased and law enforcement seizures decreased in RCL states. Conclusions Recreational cannabis laws in US states appear to be associated with illegal drug market responses in those states, including reductions in the street price of cannabis. Changes in the street prices of illegal opioids analyzed may suggest that in states with recreational cannabis laws the markets for other illegal drugs are not independent of legal cannabis market regulation.
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While increasing attention is given to how health reductions affect workers, estimating its their effects is usually challenging. This paper aims to identify the causal effect of health deterioration on labor market outcomes by exploiting the incidence of stroke. Stroke, which often reduces health suddenly and unexpectedly, allows us to exploit the within‐person random variation of the timing and isolate the effects of health reduction. By applying the fixed‐effects method to a sample of stroke survivors in the University of Michigan Health and Retirement Study data, I find that stroke immediately halves the employment probability as well as hours and weeks worked 1 year after the occurrence and its effects persist for at least 3 years, while earnings reduction is relatively moderate and gradual. I also find the negative effects of stroke are larger among men with severe stroke and women with longer pre‐stroke job tenure, while the effects are mitigated for younger women. These results make a stark contrast with the studies on other health events such as cancer diagnosis, which generally find much smaller effects and significant heterogeneity by education and occupation. This analysis shows that the labor market effects largely differ by types of diseases and calls for disease‐specific studies in order to understand the social gradient in health and how workers adjust to work limitations.
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The rapid pace of cannabis legalization in North America has provoked a backlash that is predictable and discouraging. The New Prohibitionists, distinct but related to their predecessors, the Old Prohibitionsists, have offered scholarship rife with conceptual errors, methodological flaws, and practical oversights. While their advice would likely hasten that which they seek to decrease, they overlook the costs of returning to practices associated with prohibition. To counter simplistic research interpretations and ill-considered policy, we present a critically informed research program on cannabis and crime based on previous scholarship. Our work is designed to apply replacement discourse and refocus research to withstand the tendency for justice systems to subvert, rather than embrace, reform. Cannabis legalization has been decades in the making and serious questions remain for proponents, opponents, and policymakers. Society, however, will be far worse off if the mistakes of reefer madness are repeated.
Research Summary In 2017, Seattle, Washington, became the first city in the United States to increase its minimum wage to $15 per hour, more than double the federal minimum wage. Not only was a $15 minimum wage unprecedented, but the increase was also extremely rapid, with the minimum wage rising by nearly 60% in just 2 years. Using a synthetic differences‐in‐differences estimator, we consider the impact of Seattle's landmark minimum wage legislation on public safety. Although there is speculative evidence for an increase in commercial burglaries, we find little evidence that Seattle experienced a change in its aggregate rate of violent or property offending relative to other U.S. cities. To better understand the mechanisms underlying our findings, we investigate the impacts of the local wage law on employment and earnings for Seattle's low‐skilled labor market. We detect no meaningful adverse effects on the employment rates of low‐wage workers. Policy Implications Our results suggest that Seattle increased its minimum wage without compromising public safety. Seattle's experience shows that increasing wages can be a tool for increasing the opportunity cost of crime without reducing employment levels. To the extent that other cities enact higher minimum wages to a level that generates unemployment among low‐skilled workers, public safety changes could be considerably different.
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We study the effect of recent legalization of recreational marijuana use laws (RMLs) in the United States on new applications and allowances for Social Security Disability Insurance and Supplemental Security Income over the period 2001–2019. We combine administrative caseload data from the Social Security Administration with state policy changes using two‐way fixed‐effects methods. We find that RML adoption increases applications for both benefits. However, there is no change in allowances post‐RML. We provide suggestive evidence that the observed changes in applications post‐RML are driven by increases in marijuana misuse and selective migration, and decreases in unemployment.
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This study is a spinoff of the cross-disciplinary project “FloraGuard: Tackling the Illegal Trade in Endangered Plants”, and focuses on the analysis of online forums dedicated to the discussion and the trades of plant species, often highly endangered in nature, that are sought after for their psychotropic properties. The study sheds light on the interesting but overlooked area of the intersection of environmental crimes, illegal online trades, and drug use. Some species of conservation concern have known psychoactive/analgesic properties; as these properties are now openly and broadly discussed in specialised online communities, attention is required both as regards the potential for health-related harms suffered by reckless users, and for environmental-related harms for the species in question.
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This study is the first to examine the effects of medical marijuana laws (MMLs) on body weight, physical wellness, and exercise. Using data from the 1990 to 2012 Behavioral Risk Factor Surveillance System and a difference-in-difference approach, we find that the enforcement of MMLs is associated with a 2% to 6% decline in the probability of obesity. We find some evidence of age-specific heterogeneity in mechanisms. For older individuals, MML-induced increases in physical mobility may be a relatively important channel, while for younger individuals, a reduction in consumption of alcohol, a substitute for marijuana, appears more important. These findings are consistent with the hypothesis that MMLs may be more likely to induce marijuana use for health-related reasons among older individuals, and cause substitution toward lower-calorie recreational 'highs' among younger individuals. Our estimates suggest that MMLs induce a $58 to $115 per-person annual reduction in obesity-related medical costs. Copyright © 2015 John Wiley & Sons, Ltd.
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We evaluate the impact on crime of a localized policing experiment that depenalized the possession of small quantities of cannabis in the London borough of Lambeth. We find that depenalization policy caused the police to reallocate effort toward nondrug crime. Despite the overall fall in crime attributable to the policy, we find that the total welfare of local residents likely fell, as measured by house prices. We shed light on what would be the impacts on crime of a citywide depenalization policy by developing and calibrating a structural model of the market for cannabis and crime.
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Debate has surrounded the legalization of marijuana for medical purposes for decades. Some have argued medical marijuana legalization (MML) poses a threat to public health and safety, perhaps also affecting crime rates. In recent years, some U.S. states have legalized marijuana for medical purposes, reigniting political and public interest in the impact of marijuana legalization on a range of outcomes. Relying on U.S. state panel data, we analyzed the association between state MML and state crime rates for all Part I offenses collected by the FBI. Results did not indicate a crime exacerbating effect of MML on any of the Part I offenses. Alternatively, state MML may be correlated with a reduction in homicide and assault rates, net of other covariates. These findings run counter to arguments suggesting the legalization of marijuana for medical purposes poses a danger to public health in terms of exposure to violent crime and property crimes.
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Routine activities theory purports that crime occurs in places with a suitable target, motivated offender, and lack of guardianship. Medical marijuana dispensaries may be places that satisfy these conditions, but this has not yet been studied. The current study examined whether the density of medical marijuana dispensaries is associated with crime. An ecological, cross-sectional design was used to explore the spatial relationship between density of medical marijuana dispensaries and two types of crime rates (violent crime and property crime) in 95 census tracts in Sacramento, CA, during 2009. Spatial error regression methods were used to determine associations between crime rates and density of medical marijuana dispensaries, controlling for neighborhood characteristics associated with routine activities. Violent and property crime rates were positively associated with percentage of commercially zoned areas, percentage of one-person households, and unemployment rate. Higher violent crime rates were associated with concentrated disadvantage. Property crime rates were positively associated with the percentage of population 15-24 years of age. Density of medical marijuana dispensaries was not associated with violent or property crime rates. Consistent with previous work, variables measuring routine activities at the ecological level were related to crime. There were no observed cross-sectional associations between the density of medical marijuana dispensaries and either violent or property crime rates in this study. These results suggest that the density of medical marijuana dispensaries may not be associated with crime rates or that other factors, such as measures dispensaries take to reduce crime (i.e., doormen, video cameras), may increase guardianship such that it deters possible motivated offenders.
It is common in regression discontinuity analysis to control for third, fourth, or higher-degree polynomials of the forcing variable. There appears to be a perception that such methods are theoretically justified, even though they can lead to evidently nonsensical results. We argue that controlling for global high-order polynomials in regression discontinuity analysis is a flawed approach with three major problems: it leads to noisy estimates, sensitivity to the degree of the polynomial, and poor coverage of confidence intervals. We recommend researchers instead use estimators based on local linear or quadratic polynomials or other smooth functions.
We examine the effect of medical marijuana laws (MML) on crime treating the introduction of MML as a quasi-experiment and using three different data sources. First, using data from the Uniform Crime Reports, we find that violent crimes such as homicides and robberies decrease in states that border Mexico after MML are introduced. Second, using Supplementary Homicide Reports' data we show that for homicides the decrease is the result of a drop in drug-law and juvenile-gang related homicides. Lastly, using STRIDE data, we show that the introduction of MML in Mexican border states decreases the amount of cocaine seized, while it increases the price of cocaine. Our results are consistent with the theory that decriminalization of small-scale production and distribution of marijuana harms Mexican drug trafficking organizations, whose revenues are highly reliant on marijuana sales. The drop in drug-related crimes suggests that the introduction of MML in Mexican border states lead to a decrease in their activity in those states. Our results survive a large variety of robustness checks. Extrapolating from our results, this indicates that decriminalization of the production and distribution of drugs may lead to a drop in violence in markets where organized crime is pushed out by licit competition.
State medical marijuana programs have proliferated in the United States in recent years. Marijuana sales are now estimated in billions of dollars per year with over two million patients, yet it remains unlawful under Federal law, and there is limited and conflicting evidence about potential effects on society. We present new evidence about potential effects on crime by estimating an economic crime model following the general approach developed by Becker. Data from 11 states in the Western United States are used to estimate the model and test hypotheses about potential effects on rates of violent and property crime. Fixed effects methods are applied to control for state-specific factors, with adjustments for first-order autocorrelation and cross-section heteroskedasticity. There is no evidence of negative spillover effects from medical marijuana laws (MMLs) on violent or property crime. Instead, we find significant drops in rates of violent crime associated with state MMLs.
Drugs and violence were shown to be related in three possible ways: psychopharmacologically, economic compulsively, and systemically. These different forms of drug related violence were shown to be related to different types of substance use, different motivations of violent perpetrators, different types of victims, and differential influence by social context. Current methods of collecting national crime data were shown to be insensitive to the etiological role played by drug use and trafficking in creating violent crime. No evidence currently exists as to the proportions of violence engaged in by drug users and traffickers that may be attributed to each of the three posited models. We need such data. My own impression, arising from research in New York, is that the area of systemic violence accounts for most of the violence perpetrated by, and directed at, drug users. Systemic violence is normatively embedded in the social and economic networks of drug users and sellers. Drug use, the drug business, and the violence connected to both of these phenomena, are all aspects of the same general life style. Individuals caught in this lifestyle value the experience of substance use, recognize the risks involved, and struggle for survival on a daily basis. That struggle is clearly a major contributor to the total volume of crime and violence in American society.
This paper investigates the link between cannabis depenalisation and crime using individual-level panel data for England and Wales from 2003 to 2006. We exploit the declassification of cannabis in the UK in 2004 as a natural experiment. Specifically, we use the fact that the declassification changed expected punishments differently in various age groups due to thresholds in British criminal law and employ a difference-in-differences type design using data from the longitudinal version of the Offending, Crime and Justice Survey. Our findings suggest essentially no increases in either cannabis consumption, consumption of other drugs, crime and other forms of risky behaviour.