Is Marijuana a Gateway DrugAuthor(s) Jeffrey DeSimoneSou.docx

Is Marijuana a Gateway Drug?
Author(s): Jeffrey DeSimone
Source: Eastern Economic Journal, Vol. 24, No. 2 (Spring,
1998), pp. 149-164
Published by: Palgrave Macmillan Journals
Stable URL: http://www.jstor.org/stable/40325834 .
Accessed: 18/11/2014 03:18
Your use of the JSTOR archive indicates your acceptance of the
Terms & Conditions of Use, available at .
http://www.jstor.org/page/info/about/policies/terms.jsp
.
JSTOR is a not-for-profit service that helps scholars,
researchers, and students discover, use, and build upon a wide
range of
content in a trusted digital archive. We use information
technology and tools to increase productivity and facilitate new
forms
of scholarship. For more information about JSTOR, please
contact [email protected]
.
Palgrave Macmillan Journals is collaborating with JSTOR to
digitize, preserve and extend access to Eastern
Economic Journal.
http://www.jstor.org
This content downloaded from 130.166.3.5 on Tue, 18 Nov 2014
03:18:48 AM
All use subject to JSTOR Terms and Conditions

http://www.jstor.org/action/showPublisher?publisherCode=pal
http://www.jstor.org/stable/40325834?origin=JSTOR-pdf
IS MARIJUANA A GATEWAY DRUG?
Jeffrey DeSimone
Yale University
INTRODUCTION
Marijuana is by far the most widely-used illicit drug. Though
marijuana is a pow-
erful intoxicant with subjective psychedelic-like effects that are
more complicated
than those of alcohol or cocaine, research has yet to show that
marijuana consump-
tion has harmful consequences. In truth, the primary cause for
concern about mari-
juana use may be that it potentially leads to the use of more
hazardous illegal drugs
such as cocaine. This premise arises from evidence that the
overwhelming majority of
adolescent and young adult cocaine users have previously used
marijuana [O'Donnell
and Clayton, 1982; Mills and Noyes, 1984; Yamaguchi and
Kandel, 1984; Newcomb
and Bentler, 1986; Kandel and Yamaguchi, 1993] and is known
as the gateway hy-
pothesis. Since the use of cocaine is associated with problems
such as crime, child
poverty, poor neonatal health, and the spread of HIV, a gateway
effect of marijuana

on cocaine could signify a sizable social cost of marijuana use.
Prior marijuana consumption may, however, predict current
cocaine consump-
tion without necessarily causing it. Marijuana use may simply
be a marker of either
observable personal characteristics or unobservable factors that
make marijuana us-
ers more likely to progress to cocaine use [Kleiman, 1992].
Conversely, marijuana
may truly act as a gateway to cocaine in two fashions.
Marijuana intoxication may
spawn curiosity or diminish apprehension about trying more
dangerous drugs. Or,
the physiological and psychological benefits, say "euphoria"
(from Stigler and Becker
[1977]), induced by a certain level of marijuana consumption
may decline over time,
thereby prompting users to experiment with new drugs in an
attempt to regain their
original levels of euphoria [Kleiman, 1992].
The latter process above can be explained in terms of the
economic model of ad-
diction as specified by Becker and Murphy [1988] and
Chaloupka [1991]. In this model,
the cumulative past consumption of an addictive good
represents an "addictive stock"
that raises current consumption by creating both tolerance,
through a negative mar-
ginal utility, and reinforcement, through a positive effect on the
marginal utility of
current consumption. Grossman et al. [1996a] and Pacula [1997]
find that marijuana
is addictive in the sense that past use raises the likelihood of
current use. Addictive

marijuana can generate a gateway effect in two distinct ways.
One is simply through
contemporaneous complementarity of marijuana and cocaine.
The other is through a
direct positive effect of past marijuana consumption on the
marginal utility of current
Eastern Economic Journal, Vol. 24, No. 2, Spring 1998
149
03:18:48 AM
150 EASTERN ECONOMIC JOURNAL
consumption of other euphoria-producing goods such as
cocaine, which may occur
because the addictive stock is not of marijuana itself but rather
of the euphoria pro-
duced by marijuana. In this case, marijuana could conceivably
serve both as a gate-
way to cocaine and as a substitute to cocaine in the production
of euphoria [Kleiman,
1992]. Evidence of negative cross-price effects between the two
drugs, as reported by
DeSimone [1997], Grossman et al. [1996b] and Saffer and
Chaloupka [1996], implies
contemporaneous complementarity but is also consistent with a
direct intertemporal
relationship because none of these studies disentangles past and
current price ef-

fects.
In combination, then, the empirical findings of addictive
marijuana and negative
cross-price effects suggest a gateway effect of marijuana on
cocaine. Notwithstand-
ing, no formal econometric evidence of the intertemporal
relationship between the
two drugs has been obtained to this point. This paper provides
such evidence using
data from the National Longitudinal Survey of Youth (NLSY).
Though the study does
not attempt to identify the causal mechanisms by which
marijuana and cocaine are
related, it is the first to estimate a structural effect of past
marijuana demand on
current cocaine demand. The results provide strong
confirmation of the gateway hy-
pothesis.
EMPIRICAL SPECIFICATION
To econometrically test the gateway hypothesis, the current
demand for cocaine
must be estimated, for a sample of individuals who have not
previously used cocaine,
as a function of past marijuana demand and current values of
other variables that
influence cocaine use. Using the subscript t to denote the
current period and t -k to
denote the period k years in the past, current cocaine
consumption Ct is thus repre-
sented as a function of past marijuana consumption Mt_k and a
vector Xt of additional
exogenous determinants,

(1) Ct=b0 + btMt.h + b2Xt + e,
A gateway effect exists if b1 > 0 in equation (1).
Two empirical considerations make ordinary least squares
(OLS) estimation of
equation (1) inappropriate. First, marijuana use is a function of
many of the same
variables that determine cocaine use.1 Although OLS on
equation (1) holds Mt_k con-
stant while estimating the direct effect of variables in Xt, some
components of Xt may
concurrently affect Ct indirectly through Mt_k. More
importantly, Mt_k and Ct may be
spuriously correlated because of unobserved factors that
simultaneously affect the
use of both types of drugs. For instance, tastes for intoxication
or deviance are likely
to vary positively with both marijuana and cocaine use.
Meanwhile, other unobserved
factors may lead individuals to choose one drug over the other
as a producer of eupho-
ria. Since all previous users of cocaine are excluded from the
estimation sample,
unobservables may in this situation reflect a preference for
marijuana over cocaine in
period t -k because marijuana is less costly, less dangerous, less
stigmatized and more
easily obtained. The regression assumption that Mt_k and ec are
uncorrelated may
03:18:48 AM

IS MARIJUANA A GATEWAY DRUG? 15 1
thus be violated, resulting in bias and inconsistency in the OLS
estimates of the pa-
rameters of equation (1).
To account for the potential correlation between Mt_h and cc, a
two-stage instru-
mental variable (IV) regression procedure is employed. The first
stage equation rep-
resents past marijuana demand as a function of the exogenous
variables Xt that affect
current cocaine demand and a vector Zt_k of variables that
affect past marijuana de-
mand but not current cocaine demand,
(2) Mt_k = a0 + a2 X, + a2 Zt_k + em.
In theory the lagged values of X also belong on the right-hand
side of equation (2), but
are omitted from the empirical analysis because the variables in
X are either time-
invariant or highly correlated over time.2 Then, since the
predicted value of Mt_k in
equation (2) represents the component of Mt_k that is
uncorrelated with the unobserv-
able factors ec that impact Ct, equation (1) is typically
estimated using this predicted
value of Mt_k as a regressor in place of the observed Mt_k.
The second empirical concern is that Ct andMt_A are specified
as binary indicators
of drug use, since the gateway hypothesis posits a relationship
between the use, rather

than explicit quantities of consumption, of each drug.3 Because
Ct is binary, equation
(I) is most suitably estimated with a probit model. Smith and
Blundell [1986] and
Blundell and Smith [1989] show that equation (1) is
consistently estimated with a
probit that uses the observed value ofM^ but also includes the
residual term em from
equation (2),
(II) Ct = b0 + b1Muk + b2Xt + b3em + ec.
These studies also derive the expression for the correct
asymptotic covariance matrix
for the probit estimates of equation (I1). A limitation of this
procedure is its assump-
tion that the included endogenous variable, in this caseMt_ki is
fully observed. Equa-
tion (2) must therefore be estimated as a linear probability
model. In order to correct
for heteroskedasticity, equation (2) is first estimated by OLS to
obtain predicted val-
ues of Mt_k, and then reestimated by weighted least squares
using the inverse of these
predicted values as weights [Gujarati, 1995].
To improve on OLS, the set of instrumental variables Zt_k that
is excluded from
the cocaine demand equation must be highly correlated with
Mt_k but uncorrelated
with ec [Gujarati, 1995]. As is well known, nonzero correlation
of Zt_k and ec will ren-
der the IV estimates of equation (1) inconsistent. Moreover,
Bound et al. [1995] illus-
trates that the explanatory power of Zt_k is crucial for two
reasons. First, if Zt_k is only

weakly correlated with Mt_k, then even a weak correlation
between Z<jk and ec can
produce a large inconsistency in the IV estimates of equation
(1) that may even sur-
pass that of the OLS estimates. Second, IV estimates are biased
in finite samples, and
the magnitude of this bias approaches that of OLS as the
explanatory power of Zt_k
approaches zero. These empirical considerations must guide the
choice of Zt_k in the
analysis.
03:18:48 AM
DATA
The primary source of data is the NLSY [Center for Human
Resource Research,
1995]. Since 1979, the NLSY annually has collected detailed
demographic and eco-
nomic information from a cohort of 12,686 individuals
originally aged between 14 and
22. In the 1984 and 1988 interviews, respondents were asked
about their current and
lifetime use of a variety of drugs, including marijuana and
cocaine. Consequently, the
relationship between cocaine demand in t=1988 and the demand
for marijuana k=4
years previously is estimated for respondents who report no

previous lifetime use of
cocaine in 1984. Past year rather than lifetime measures of drug
use are employed in
an attempt to restrict attention to individuals who are persistent
rather than experi-
mental drug users, following the interpretation of Saffer and
Chaloupka [1996] that
annual participation reflects at least occasional use. The
estimate here thus mea-
sures the gateway from recurrent marijuana to cocaine use,
which is more relevant
for drug policy than one that also encompasses experimental
drug use. As mentioned
earlier, the drug use variables are binary indicators of
consumption.
Note that NLSY respondents are between 23 and 31 years old in
1988, an impor-
tant consideration since all existing evidence of a marijuana
gateway comes based
upon responses from individuals in their teens and early
twenties. A specific concern
is that the sample is highly selective because cocaine initiation
declines with age in
the NLSY. More precisely, even though only 17 percent of the
respondents inter-
viewed in 1984 had previously used cocaine and are thus
excluded from the sample,
this group is almost four times as large as the number of
respondents in the sample
reporting past year use in 1988. The estimate of the gateway
effect reported here,
therefore, may not hold at younger ages or for the population in
general. In particu-
lar, given that past year marijuana prevalence in the NLSY also
fell over time, from

46 percent in 1980 to 32 percent in 1984, a gateway effect may
not be detectable
because precisely those individuals for whom the effect is likely
to be strongest are
not included in the sample.4 On the other hand, the finding of a
gateway effect would
mark an extension of the age range within which the
phenomenon is considered to be
consequential.
The set of explanatory variables included in the vector X^ is as
follows. Race is
represented by indicators of black and Hispanic ethnicity.
Educational attainment
measures human capital and is expected to be negatively
correlated with drug de-
mand, as is age and marriage. Males traditionally have higher
rates of drug use than
females. Indicators of living in a central city and in an SMSA
outside a central city
control for the possibility of increased availability of illegal
drugs in urban areas.
Indicators of a Catholic or nonreligious upbringing may reflect
the presence or ab-
sence of anti-drug values and attitudes. An indicator of the
presence of both parents
in the household at age fourteen is intended to capture parental
supervision and may
further capture family values that discourage drug use. Past year
earnings will be
positively associated with drug demand if drugs are normal
goods.6 In addition, esti-
mates of the average past year retail price of one pure gram of
cocaine, imputed for
over 140 cities using data from the Drug Enforcement
Administration (DEA) on pur-

chases made by undercover agents from 1977 to 1994, are
matched to the individual
03:18:48 AM
IS MARIJUANA A GATEWAY DRUG? 153
NLSY responses.6 As previously mentioned, values of all time-
varying variables are
taken from the 1988, or current period, interview.
The set of instruments 7*M that identify marijuana use includes
two measures of
state-level penalties for marijuana possession and two
additional variables that are
expected to affect marijuana use through their influence on
alcohol consumption. The
marijuana penalty variables, which pertain to the possession of
one ounce of mari-
juana for first-time offenders in the state of residence of the
respondent in 1984, are
the maximum prison term and an indicator that fines are not
assessed. Both of these
variables are components of the expected cost of marijuana use
and as such contrib-
ute to the full price of marijuana.7 The expected cost of
marijuana use, in terms of
time, lost wages and stigma from being arrested and sent to
prison, rises as the maxi-
mum prison term increases from zero in ten states to a year in
eighteen states and

more than that in six others. Similarly, the expected monetary
cost is lower in the two
states, Massachusetts and Oklahoma, in which no fine can be
levied compared to
other states in which the maximum fine ranges from $100 (in
ten states) to $2,500 or
more (in seven states).8 The alcohol-related variables are the
state excise tax on beer
and an indicator of parental alcoholism or problem drinking.
The beer tax is the tax
on a case of 24 twelve-ounce beers containing 3.2 percent
alcohol [Beer Institute,
1995]. If marijuana and alcohol are substitutes, as reported by
DiNardo and Lemieux
[1992], Model [1993] and Chaloupka and Laixuthai [1994], then
marijuana demand
will respond positively to increases in the beer tax, though
Saffer and Chaloupka
[1996] find evidence of the reverse relationship. Meanwhile,
Cadoret [1992] and
Merikangas et al. [1992] report an association between parental
alcohol problems
and filial drug use. This relationship may operate through
alcohol use, since the pre-
viously cited research on the chronological path of drug
initiation shows that alcohol
use almost always precedes the initiation of illegal drug use,
while Kenkel and Ribar
[1994] find that children of parents with alcohol problems are
more likely to consume
alcohol than others.
Although marijuana penalties could conceivably influence
cocaine and marijuana
use separately if they reflect sentiments towards illegal drug use
in general [Model,

1993], the exclusion of the two marijuana penalty variables
from the cocaine demand
equation is natural because their direct impact is on marijuana
demand. However,
the exclusion of the two alcohol-related variables is more
questionable. The rationale
is the expectation that the consumption of alcohol is more
closely related with that of
marijuana than that of other illegal drugs like cocaine that are
more dangerous and
less socially acceptable. Besides evidence that alcohol use
usually precedes the initia-
tion of marijuana use even for the large fraction of marijuana
users who do not progress
to cocaine use, additional support for this exclusion strategy is
given by Mills and
Noyes [1984], Yamaguchi and Kandel [1984] and Newcomb and
Bentler [1986], who
fail to find a direct connection between past alcohol and current
cocaine use. Never-
theless, the propriety of this restriction and the full set of
exclusions is empirically
tested below, as is the power of the marijuana penalty variables
and the full set of
excluded instruments Z^ to explain the variation in marijuana
use.
03:18:48 AM

TABLE 1
Descriptive Statistics
(n=7,279)
Standard
Mean Deviation
Dependent Variables
Used Cocaine Past Year 0.057 0.232
Used Marijuana Past Year (in 1984) 0.246 0.431
Explanatory Variables
Age 27.005 2.265
Education 12.683 2.437
Male 0.443 0.497
Black 0.270 0.444
Hispanic 0.162 0.368
Married 0.482 0.500
Central City Residence 0.143 0.350
SMSA Residence 0.595 0.491
Catholic Background 0.323 0.468
No Religious Background 0.039 0.193
Earnings Past Year (in 1000s) 17.663 15.111
Cocaine Price 123.620 30.144
Both Parents Present Age 14 0.691 0.462
Excluded Instrumental Variables
Alcoholic Parent 0.215 0.411
Beer Tax (in 1984) 0.540 0.621
Maximum Jail Time for Marijuana (in 1984) 0.775 1.269
No Fine for Marijuana (in 1984) 0.036 0.187
Table 1 presents the means and standard deviations of the
variables included in
the analysis for the 7,279 respondents with complete data.9

Almost a quarter of the
sample used marijuana in the year prior to the 1984 interview,
but fewer than 6
percent used cocaine in the year preceding the 1988 interview.
Table 2 exhibits the
joint frequency of past marijuana and current cocaine demand.
Even though 1984
marijuana nonusers outnumber users by a three-to-one margin,
almost two-thirds of
those who consumed cocaine in 1988 also consumed marijuana
in 1984. A chi-square
test of independence formally rejects the hypothesis that
marijuana users are no more
likely than nonusers to progress to cocaine use. However, more
rigorous examination
is necessary to determine whether this preliminary evidence of a
gateway effect actu-
ally represents a causal relationship.
A final point before proceeding to the regression results is that
since drug use is
an illegal activity, it may be underreported by NLSY
respondents. Some evidence of
underreporting exists for lifetime cocaine use in 1984 [Mensch
and Kandel, 1988] and
for both lifetime and past month marijuana and cocaine use, in
both 1984 and 1988,
in the modest (around 9 percent) fraction of interviews in which
parents are present
03:18:48 AM

TABLE 2
Joint Frequencies of Marijuana and Cocaine Use
Used Cocaine Past Year 1988
Yes No Total
Used Marijuana Past Year 1984
Yes 270 1522 1792
No 147 5340 5487
Total 417 6862 7279
Chi-Squared Test of Independence: 2(1) = 383.8, p = 0.000
or that take place over the telephone [Hoyt and Chaloupka,
1994]. However, Sickles
and Taubman [1991] provide evidence that reported past year
drug use should be
fairly accurate in both years. Furthermore, as long as marijuana
use is truthfully
reported, underreporting of 1988 cocaine use will only bias the
estimate of the gate-
way effect if this underreporting is correlated with 1984
marijuana use. But if the
strong association between marijuana and cocaine use recorded
in Table 2 also holds
for cocaine users who report not using cocaine, so that
marijuana users are more
likely than nonusers to underreport cocaine, then the effect of
underreporting of 1988
cocaine use but not 1984 marijuana use is to bias the estimated
gateway coefficient
towards zero.10 Such downward bias is exacerbated if lifetime
cocaine use in 1984 is

also underreported, as suggested by evidence from Johnston et
al. [1988] that respon-
dents who lie do so consistently over time, because the primary
effect of underreporting
in this case is to count respondents as current cocaine nonusers
when in fact they
should be excluded from the sample.11
RESULTS
Table 3 displays the regression results. Column 1 shows the
estimates of equation
(2), the first stage equation for marijuana use in 1984. The
variables of primary con-
cern are the excluded instruments Z^. The signs are as
anticipated for the three of
these variables for which prior expectations of the sign exist.
The predicted likelihood
of marijuana use is 3 percent lower in Nevada, which has the
highest maximum prison
term of six years, than in the ten states in which first-time
marijuana possession
offenses carry no prison sentence. The coefficient approaches
statistical significance
with ap-value of 0.136. The remaining three identifying
instruments are statistically
significant at all conventional confidence levels. Having an
alcoholic parent increases
the probability of marijuana consumption slightly more than
living in a state with no
fines for first-time marijuana possession. Meanwhile, the
coefficient of the beer tax
predicts that marijuana consumption is over 4 percent more
probable in Wyoming,
which has the lowest beer tax of $0.04, than in Alabama, which
has the highest of

$2.28. Although this evidence of complementarity between
alcohol and marijuana
contradicts the findings of DiNardo and Lemieux [1992], Model
[1993] and Chaloupka
and Laixuthai [1994], this result may not generalize because of
the selection of the
sample used here.
03:18:48 AM
TABLE 3
Equations for Past Year Drug Demand
First Stage: Second Stage:
WLS for 1984 Probits for 1988
Marijuana Use Cocaine Use
0) Í2a) Í2b)
Constant 0.422a 0.108b 0.177
(0.066) (0.054) (0.200)
Marijuana Use (in 1984) 0.292a 0.226
(0.074) (0.176)
Age -0.006a -0.0017 -0.0022
(0.002) (0.0012) (0.0015)
Education -0.001 -0.0028b -0.0028b

(0.002) (0.0011) (0.0011)
Male 0.114a -0.006 0.002
(0.010) (0.009) (0.021)
Black -0.015 -0.011 -0.012c
(0.012) (0.007) (0.007)
Hispanic -0.088a 0.002 -0.004
(0.015) (0.011) (0.017)
Married -0.070a -0.020b -0.024c
(0.012) (0.008) (0.014)
Central City Residence 0.071a 0.0001 0.006
(0.017) (0.010) (0.015)
SMSA Residence 0.079a -0.006 -0.000003
(0.011) (0.009) (0.015)
Catholic Background 0.041a -0.001 0.002
(0.013) (0.007) (0.009)
No Religious Background 0.075a -0.025c -0.020
(0.028) (0.014) (0.018)
Earnings Past Year -0.0011a 0.0004c 0.0003
(in 1000s) (0.0003) (0.0002) (0.0003)
Cocaine Price -0.206 -0.184b -0.199b
(in 1000s) (0.163) (0.093) (0.091)
Both Parents Present -0.015 0.001 0.0004
Age 14 (0.011) (0.006) (0.006)
Alcoholic Parent 0.065a 0.007

(0.013) (0.013)
Beer Tax (in 1984) -0.019a 0.001
(0.007) (0.006)
Maximum Jail Time for Marijuana -0.0053
(in 1984) (0.0036)
No Fine for Marijuana 0.057a
(in 1984) (0.018)
Standard deviations are given in parentheses. In columns 2a and
2b, the constant indicates the probabil-
ity of cocaine use when all variables are set to zero, while
remaining coefficients represent the change in
probability of cocaine use for a unit change in the explanatory
variable while holding all other variables
constant at their mean values.
a. Significant at the 0.01 level.
b. Significant at the 0.05 level.
c. Significant at the 0.10 level.
03:18:48 AM
Of the other explanatory variables, those of greatest economic
relevance are earn-
ings and the price of cocaine. The results for these two
variables contradict, to a great
extent, evidence from Grossman et al. [1996a; 1996b] and

Saffer and Chaloupka [1996]
that marijuana is a normal good that is complementary with
cocaine. Though the
cocaine price does carry a negative coefficient, its effect is
statistically insignificant.
Moreover, the effect of earnings is negative and significant. An
interpretation of these
results, though, must consider the construction of the sample.
As cocaine becomes
more expensive, some respondents who would otherwise have
initiated cocaine and
therefore been excluded from the sample may instead substitute
toward marijuana
rather than away from illegal drugs altogether, contributing to
the insignificance of
the cocaine price. The same may occur as earnings declines,
since the large expense of
cocaine per dose may imply a large income effect. It does not
necessarily follow that
marijuana and cocaine use are unrelated, nor that marijuana is
an inferior good, for
the general population that also includes cocaine users. The
only other surprising
result is the significantly higher likelihood of use for Catholics,
which again may be
an artifact of the sample selection procedure if Catholic drug
users are more inclined
to use marijuana than cocaine as a producer of euphoria. The
remaining explanatory
variables have the expected relationship with marijuana
consumption, and all are
significant besides educational attainment, black racial status,
and the presence of
both parents in the household at age fourteen.
Column 2a of Table 3 gives the results for equation (I1), the

second stage probit for
1988 cocaine demand. As previously discussed, past marijuana
use is identified through
the exclusion of both the two marijuana penalty variables and
the two alcohol-related
variables. The coefficients are reported in terms of the change
in probability of co-
caine use induced by a unit change in the explanatory variable
while holding all other
variables constant at their mean values, while the constant
represents the probabil-
ity of cocaine use when all variables equal zero. The crucial
result, of course, is the
statistically significant and extremely large positive effect of
past marijuana use. In
particular, past marijuana use raises the probability of
consuming cocaine by more
than 29 percentage points. This impact is clearly substantial,
dwarfing that of the
other explanatory variables, for realistic variations in their
values, by a factor of at
least ten. Not only is a strong gateway effect of marijuana on
cocaine apparent, there-
fore, but it is by far the most important factor in explaining
current cocaine demand.
The relevance of economic factors is shown by the fact that
besides earnings and
the price of cocaine, only three other exogenous variables in
column 2a are signifi-
cant. The coefficients of the earnings and price variables
indicate, in agreement with
findings of Grossman et al. [1996b] and Saffer and Chaloupka
[1996], that cocaine is
a normal good with a downward-sloping demand curve. The
magnitudes of these ef-

fects, however, are modest. An increase in earnings of one
standard deviation, or
$15,000, raises the probability of using cocaine by just over
half a percentage point,
which is almost identical to the impact of lowering the price of
cocaine by one stan-
dard deviation, or $30. The implied price elasticity of -0.40 is,
nonetheless, consis-
tent with that estimated in the studies mentioned above.
Meanwhile, the other sig-
nificant results indicate that four additional years of education,
marriage, and an
03:18:48 AM
TABLE 4
Specification Tests for Cocaine Use Probits
Model (a) Model (b)
First Stage F-statistic for Excluded Instruments 12.84 6.25
(0.000) (0.002)
Overidentification Test x2-statistic 1.577 0.523
(0.816) (0.770)
Second Stage x2-statistic for Alcohol Variables - 0.83
(0.660)

Hausman Test t-statistic 0.231a 0.164
(0.060) (0.152)
Hausman Test for (a) vs (b) - 0.067
(0.140)
Models (a) and (b) correspond to columns 2a and 2b,
respectively, of Table 3. P-values are given in
parentheses beneath F and x2-statistics, while standard
deviations are given in parentheses beneath t-
statistics.
a. Significant at the .01 level.
upbringing that involves religion lowers the probability of using
cocaine by 1, 2, and
2.5 percentage points, respectively. Again, though, the most
notable characteristic of
the significant coefficients is that their magnitudes are trivial in
comparison with
that of past marijuana use.
Table 4 reports the results of various tests regarding the
specification of the IV
procedure. The test statistics in column a correspond to the
model of column 2a in
Table 3. In the top row, the joint F-statistic for the excluded
instruments Z^ verifies
that these variables are highly correlated with marijuana
demand, an essential con-
dition for minimizing the standard errors of the IV estimates.
Consequently, any
inconsistency in the IV estimates arising from a weak nonzero
correlation between
Z^ and ec should be low relative to that of OLS. Following
Bound et al. [1995], this F-
statistic also implies that the inherent small sample bias in the

IV estimates is only
about 1 IF, or 8 percent, of the bias arising from OLS
estimation.
The next row gives the statistic for a test of the validity of the
overidentifying
restrictions. If ZM is jointly correlated with ec and therefore
should in reality be in-
cluded in equation (I1), then the IV estimates of equation (I1)
are inconsistent. Lee
[1991] illustrates how a chi-square test statistic for the
overidentifying restrictions,
with degrees of freedom equal to the number of excluded
instruments, can be ob-
tained as a by-product of the IV regression methodology
because, as Blundell and
Smith [1989] show, the estimation procedure is a minimum chi-
square method. The
joint hypothesis that the excluded instruments neither belong in
the second stage
equation (I1) nor are correlated with the second stage residuals
ec cannot be rejected
at standard significance levels. More precisely, the extremely
high p-value provides
evidence that any correlation between Zw and ec is quite weak.
03:18:48 AM
The remaining statistic in column a of Table 4, appearing in the

fourth row, is for
a test of the exogeneity of past marijuana use in equation (I1).
As Smith and Blundell
[1986] show, the t-statistic for the coefficient of the first stage
residual em, b3, in equa-
tion (I1) provides an exogeneity test that is analogous to the
standard Hausman test
for exogeneity [Hausman, 1978]. Since the IV procedure is
correctly specified, as the
F and overidentification tests verify, any difference in the IV
and OLS estimates of bv
the gateway effect, arises because a nonzero correlation
between Mw and ec affects
the OLS but not the IV estimate. The exogeneity test statistic
measures the magni-
tude of this difference and thus indicates an OLS estimate of
0.061. Since the IV
estimate is significantly larger than the OLS estimate, the latter
must exhibit down-
ward bias. This bias has two potential causes: observable
factors that in reality affect
cocaine use in part through marijuana use, and unobservable
factors that increase
past marijuana demand while decreasing current cocaine
demand.
To disentangle these two sources of bias, I evaluate the IV
estimates of the co-
caine demand equation (I1) at mean values of the explanatory
variables for marijuana
users and nonusers (not reported here) while holding marijuana
use constant and
take the difference in predicted cocaine use between users and
nonusers. I then do
the same for the OLS estimates of equation (I1) (also not
reported here). The differ-

ence between these two quantities reveals that only 1.5
percentage points of the dis-
crepancy between the IV and OLS estimates of the gateway
effect represent effects of
exogenous explanatory variables that in reality work through
past marijuana use,
but are incorrectly attributed by OLS to the variables
themselves. Unobserved het-
erogeneity must account for the remainder of the difference
between the IV and OLS
coefficients. More precisely, unobserved factors make
marijuana users 21.6 percent-
age points less likely than nonusers to consume cocaine. Unlike
OLS, the IV method-
ology is able to separate this unobserved heterogeneity from the
true causal effect of
marijuana use. This finding contradicts the conventional
wisdom that the gateway
effect is an artifact of unobserved variables that make both
marijuana and cocaine
consumption more likely, suggesting instead that unobserved
substitutability of mari-
juana and cocaine use masks an even greater effect of marijuana
use than the OLS
estimate of the gateway effect indicates. This result is not
inconsistent with
intertemporal complementarity of marijuana and cocaine use. It
simply implies that
marijuana users have actively chosen marijuana over cocaine as
a producer of eupho-
ria. The unobserved reasons for this choice make them,
ceterisparibus, less likely to
progress to cocaine use than others who have never used
cocaine, even though these
others may have been more likely to avoid illegal drugs
altogether. Ironically, mari-

juana users are subsequently more likely to use cocaine because
of the substantial
effect of marijuana use itself.
The variables in Z^ most likely to belong in the second stage
equation (I1) are the
alcoholic parent indicator and the beer tax. To further evaluate
the validity of exclud-
ing these two variables, I directly compare the specification
discussed so far to an
alternative specification in which these variables are included in
equation (I1). Col-
umn 2b of Table 3 shows the estimates of this alternative
model. Not surprisingly, the
effects of the exogenous variables are quite similar across the
two IV equations. In
03:18:48 AM
addition, the estimated gateway effect in column 2b is much
closer to that of column
2a than that of OLS. Because the standard error is more than
twice as large as that in
2a, though, the estimate in 2b is not significantly different from
zero and hence yields
a much different implication than does the estimate in 2a.
Column b of Table 4 displays the results of the relevant
specification tests for the

model presented in column 2b of Table 3, allowing investigation
of the sources of the
variation between the two IV estimates of the gateway effect.
The top two rows verify
that this alternative model is also validly specified. The relative
magnitudes of the F-
statistics in columns a and b imply that the gateway estimate in
column 2b of Table 3
will exhibit greater small sample bias than that in column 2a. It
is thus expected that,
as Table 3 indicates, the estimate in column 2b is closer to the
OLS estimate than is
that in column 2a. Meanwhile, the third row shows that the two
alcohol-related vari-
ables are jointly insignificant in the cocaine demand equation,
which is not surprising
given that neither individually approaches significance in Table
3. This result pro-
vides explicit evidence that the two alcohol variables are validly
excluded from the
cocaine demand equation, which supports the more general
conclusion from the col-
umn a overidentification test statistic that the full exclusion
restriction is valid. Fi-
nally, the bottom two rows report the statistics for the
exogeneity tests that compare
the gateway coefficient in this model to those of the OLS and
column a models, re-
spectively. Though the model a estimate is significantly
different from the OLS esti-
mate, the estimate of this model is not significantly different
from those of either
column a or OLS because of the large standard error.
The combined results of the specification tests indicate that the
primary conse-

quence of the exclusion of the two alcohol-related variables
from the cocaine demand
equation is an increase in efficiency of the IV estimate of the
gateway effect. Since
these variables are highly related to past marijuana consumption
but not separately
related to current cocaine consumption, their exclusion does not
alter the magnitude
of the estimate of the gateway effect, but does provide the
additional explanatory
power necessary to reduce the standard errors enough to
produce statistical signifi-
cance. Furthermore, the evidence that alcohol use leads to
cocaine use only through
its strong complementarity with marijuana use is consistent with
the hypothesis that
a separate intertemporal link from alcohol to marijuana
precedes the one from mari-
juana to cocaine that is shown here.
CONCLUSION
This study has found evidence of a gateway from marijuana to
cocaine that takes
place at later ages than were previously thought relevant.
Structural estimates ac-
counting for unobserved heterogeneity that affects both
marijuana and cocaine de-
mand indicate that past marijuana use increases the probability
of current cocaine
use by twenty-nine percentage points. This effect is nearly three
times as large as the
one-in-ten increase that Kleiman [1992] cites as warranting a
considerable expansion
in efforts to control marijuana use. To put the magnitude of this
result in perspective,

preventing past marijuana use decreases the likelihood of
cocaine initiation by an
amount that is almost thirteen times greater than that brought
about by a doubling
03:18:48 AM
of the average cocaine price, which any feasible enforcement
effort is unlikely to ac-
complish [Kleiman, 1992]. In order to most effectively deter
cocaine use, therefore,
some resources devoted to raising prices, which is the
predominant focus of present
U.S. drug policy, should be redirected towards the prevention of
marijuana initiation
by adolescents and young adults.
As a caveat, the estimate of the gateway effect obtained here
suffers from several
potential deficiencies as a result of data limitations. First,
current and past drug use
are crudely measured with a longtime interval between the two
periods. Consequently,
questions remain regarding the extent and persistence of
marijuana use necessary to
bring about cocaine use and the length of time between
marijuana and cocaine initia-
tion. Second, the gateway effect may be less pronounced for
younger cocaine users

who were excluded from the sample employed here. Earlier
cocaine use may reflect a
stronger inclination towards cocaine that makes it less likely to
be preceded by mari-
juana use. In addition, the unobserved factors found here to bias
the OLS estimate
towards zero may be an artifact of the substitution away from
cocaine by marijuana
users who have not tried cocaine by this relatively late age and
may thus not be
representative of unobservables for younger cohorts.
Conversely, to the extent that
late cocaine initiation reflects a lower propensity for
intoxication, this estimate of the
gateway effect may instead be conservative. Third, cohort
effects may make this esti-
mate inapplicable for the comparable age group today. In
particular, the demand for
illegal drugs has apparently shifted down in the last decade in
response to attitudinal
changes [SAMHSA, 1996].
Even conditional on the generalizability of the estimate
obtained here, policy im-
plications depend crucially on the contemporaneous relationship
between marijuana
and alcohol use. If these drugs are indeed complements, as
indicated here and in
Saffer and Chaloupka [1996], then policy should clearly aim to
restrict youth mari-
juana use. On the other hand, if these drugs are in fact
substitutes, as several previ-
ously cited studies have found, then restrictive marijuana policy
involves a tradeoff
between reducing future cocaine demand and increasing current
alcohol demand.

The latter is problematic because alcohol intoxication most
likely involves greater
health, accident and crime risks than marijuana intoxication.
Similarly, policy impli-
cations depend on the nature of the unobserved factors found
here to be responsible
for the inconsistency in the OLS estimates, since they reflect a
component of the
contemporaneous relationship between marijuana and cocaine.
Namely, does the
negative correlation between unobservables affecting marijuana
and cocaine imply
that restrictive marijuana policy would push teenagers and
young adults to substi-
tute cocaine for marijuana at an earlier age rather than simply
block the gateway to
cocaine use?
To further clarify policy goals, future research should attempt
to identify the fun-
damental relationships underlying the gateway from marijuana
to cocaine. In par-
ticular, empirical analysis should seek to establish the relative
importance of mari-
juana addiction, the contemporaneous relationship between
marijuana and cocaine,
and the direct intertemporal effect that exists because both
drugs produce euphoria.
For instance, Pacula [1997] reports evidence of both addictive
marijuana and strong
contemporaneous complementarity between alcohol and
marijuana, but fails to find a
03:18:48 AM

separate direct gateway from alcohol to marijuana.12
Comparable information for the
gateway from marijuana to cocaine can delineate the role that
reducing the persis-
tence over time of marijuana use must play in the effort to avert
the progression from
marijuana to cocaine use.
NOTES
I am grateñil to Frank Chaloupka and Mike Grossman of the
National Bureau of Economic Research
for graciously providing the cocaine price data used herein, the
Bureau of Labor Statistics for allow-
ing my use of the restricted NLSY geocode data, and two
anonymous referees and the editor for
suggestions that substantially improved the paper.
1. Even though Mt_h is a predetermined variable in equation
(1), this issue is empirically relevant
because many determinants of marijuana and cocaine use are
either time-invariant or highly corre-
lated over time, as is marijuana consumption if it is indeed
addictive.
2. For this reason, though I label equation (2) as a marijuana
demand equation and later analyze it as
such, it is more strictly interpreted as an instrumenting
equation. It should be noted, though, that
the use of lagged rather than current values of X does not

change the estimates of equation (2) much.
3. In addition, data constraints dictate the use of binary
measures of drug consumption, as discussed in
the following section.
4. Lack of information on lifetime cocaine use in 1980 prevents
the estimation of the relationship be-
tween 19S0 marijuana use and 19S4 cocaine use.
5. All monetary variables are converted to 1982-84 equivalents
using the CPI for all urban consumers.
6. As explained in detail in Grossman et al. [1996b], a
regression of the price of the transaction on its
weight and purity and a set of city and year dummies is run and
then used to predict a standardized
price for each city and year. This procedure is similar to the
ones developed by Caulkins [1994] and
Saffer and Chaloupka [1996]. These standardized prices were
generously supplied to me by Frank
Chaloupka and Mike Grossman of NBER. I then created a past
year price by taking a weighted
average of the 1987 and 1988 prices for each city based on the
interview month. Since a price esti-
mate exists for at least one city in each state, each county of
residence in the NLSY is assigned the
price from the city within the same state that is geographically
closest. Although the matching pro-
cess introduces measurement error that most likely biases the
estimates of the price elasticity of
marijuana and cocaine demand towards zero, the imputed prices
should be reasonably accurate for
the large majority of the sample that resides in urban areas.
7. The retail price of marijuana would be the ideal instrumental

variable for marijuana use. However,
because DEA agents do not focus on apprehending marijuana
dealers, the number of marijuana
purchases recorded in the DEA database that generate the
cocaine price estimates used here is
insufficient to estimate a reliable price series for marijuana.
8. The maximum prison term yields almost identical results to
the midpoint of the range of the poten-
tial prison term, or any other weighted average of the minimum
and maximum prison sentence,
because the minimum prison term exceeds zero in only two
states, Arizona (mandatory 1.5 years)
and West Virginia (range from 3 to 6 months). Note that beyond
the two included variables, other
measures of state-level marijuana penalties, including minimum
and maximum fines for possession
and an indicator of decriminalization, are not significantly
related to marijuana use.
9. The reasons for excluding the remaining 5,407 respondents
from the original NLSY cohort are as
follows. First, the 1,280 members of the military sample are not
included, though in any event 1,079
of these respondents are dropped from the NLSY after 1984 and
accordingly the necessary informa-
tion for inclusion exists for only 113 people out of the original
1,280. An additional 1,320 respondents
are not interviewed in one of the two years. The sample next
omits the 397 individuals who are in the
military (although drug price and penalty variables could not be
matched for this group in any case
because states of residence are not recorded for military
respondents) and the 190 respondents who
are incarcerated at the time of either interview, since these
people may not have the same opportu-

nity to consume illegal drugs as others. Another 120 individuals
live outside the United States in
03:18:48 AM
either 19S4 or 1988, while an additional 1,484 respondents have
consumed cocaine by the 1984
interview. The remaining exclusions occur because values are
missing for one of the two drug use
variables in 162 cases and for an explanatory variable in 454
instances.
10. A separate potential source of bias is the refusal of drug
users to answer the survey questions on drug
use. Refusal bias seems improbable, though, because missing
values for either 19S4 marijuana or
19SS cocaine use are responsible for the exclusion of only 162
individuals. Moreover, although 156 of
these respondents report marijuana use but not cocaine use, the
prevalence of marijuana use among
these respondents, 17.9 percent, is slightly lower than the
prevalence of 24.6 percent among sample
respondents.
11. To maintain unbiasedness under this scenario,
underreporters must be less likely to use marijuana
than others, rather than equally likely as in the case in which
cocaine use is underreported only in
1988.

12. Though in her analysis marijuana plays the role of cocaine
here, Pacula [1997] does not eliminate
previous users of marijuana from her sample and therefore must
consider marijuana addiction as an
additional element of the intertemporal relationship between
alcohol and marijuana.
REFERENCES
Becker, G. and Murphy, K. A Theory of Rational Addiction.
Journal of Political Economy, August 1988,
675-700.
Beer Institute. Brewer's Almanac. Washington, D.C.: U.S.
Brewer's Association, 1995.
Blundell, R. and Smith, R. Estimation in a Class of
Simultaneous Equation Limited Dependent Vari-
able Models. Review of Economic Studies, January 1989, 37-57.
Bound, J., Jaegar, D., and Baker, R. Problems With
Instrumental Variables Estimation When the
Correlation Between the Instruments and the Endogenous
Explanatory Variable is Weak. Jour-
nal of the American Statistical Association, June 1995, 443-50.
Cadoret, R. Genetic and Environmental Factors in Initiation of
Drug Abuse and the Transition to Abuse,
in Vulnerability to Drug Abuse, edited by M. Glantz and R.
Pickens. Washington, D.C.: American
Psychological Association, 1992.
Caulkins, J. Developing Price Series for Cocaine. Santa Monica,
CA: RAND, 1994.
Center for Human Resource Research. NLS User's Guide.

Columbus, OH: Ohio State University,
1995.
Chaloupka, F. Rational Addictive Behavior and Cigarette
Smokmg. Journal of Political Economy, Au-
gust 1991, 722-742.
Chaloupka, F. and Laixuthai, A. Do Youths Substitute Alcohol
and Marijuana.'' borne üiconometnc
Evidence. NBER Working Paper No. 4662, February 1994.
DeSimone, J. Illegal Drug Use and Labor Supply. Unpublished
manuscript, Yale University, November
1997.
DiNardo, J. and Lemieux, T. Alcohol, Marijuana, and American
Youth: The Unintended Effects of
Government Regulation. NBER Working Paper No. 4212,
November 1992.
Grossman, M., Chaloupka, F., and Brown, C. The Demand for
Marijuana by Young Adults: A Katio-
nal Addiction Approach. Unpublished manuscript, National
Bureau of Economic Research, April
1996a.
. The Demand for Cocaine by Young Adults: A Rational
Addiction Approach. NBER Working
Paper No. 5713, August 1996b.
Gujarati, D. Basic Econometrics. New York: McGraw-Hill,
1995.
Hausman, J. Specification Tests in Econometrics. Econometrica,
November 1978, 1251-71.
Hoyt, G. and Chaloupka, F. Effect of Survey Conditions on

Selt-Keported Substance use. contempo-
rary Economic Policy, July 1994, 109-21.
Johnston, L., O'Malley, P. and Bachman, J. Illicit Drug Use,
Smoking ana Drinking ay Americas
High School Students, College Students, and Young Adults.
Washington, D.C.: U.S. Department
of Health and Human Services, 1988.
Kandel, D. and Yamaguchi, K. From Beer to Crack:
Developmental Patterns of Drug Involvement.
American Journal of Public Health, June 1993, 851-55.
03:18:48 AM
Kenkel, D. and Ribar, D. Alcohol Consumption and Young
Adults* Socioeconomic Status. Brookings
Papers on Economic Activity: Microeconomics, 1994, 119-61.
Kleiman, M. Against Excess: Drug Policy for Results. New
York: Basic Books, 1992.
Lee, L. Amemiya's Generalized Least Squares and Tests of
Overidentification in Simultaneous Equation
Models with Qualitative or Limited Dependent Variables.
Center for Economic Research Discus-
sion Paper No. 262, University of Minnesota, May 1991.

Mensch, B. and Kandel, D. Underreporting of Substance Use in
a National Longitudinal Youth Cohort.
Public Opinion Quarterly, Spring 1988, 100-24.
Merikangas, K., Rounsaville, B., and Prusoff, B. Familial
Factors in Vulnerability to Substance Abuse,
in Vulnerability to Drug Abuse, edited by M. Glantz and R.
Pickens. Washington, D.C.: American
Psvcholocical Association. 1992.
Mills, C. and Noyes, H. Patterns and Correlates of Initial and
Subsequent Drug Use Among Adoles-
cents. Journal of Consulting and Clinical Psychology, April
1984, 231-43.
Model, K. The Effect of Marijuana Decriminalization on
Hospital Emergency Room Drug Episodes: 1975-
1978. Journal of the American Statistical Association,
September 1993, 737-47.
Newcomb, M. and Bentler, P. Cocaine Use Among Adolescents:
Longitudinal Associations with Social
Context, Psychopathology, and Use of Other Substances.
Addictive Behaviors, November 1986,
263-73.
O'Donnell, J. and Clayton, R. The Stepping-Stone Hypothesis -
Marijuana, Heroin and Causality.
Chemical Dependencies, March 1982, 229-41.
Pacula, R. Adolescent Alcohol and Marijuana Consumption: Is
There Really a Gateway Effect? Unpub-
lished manuscript, University of San Diego, 1997.
Saffer, H. and Chaloupka, F. The Demand for Illicit Drugs.
Unpublished manuscript, National Bureau

of Economic Research, 1996.
Sickles, R. and Taubman, P. Who Uses Illegal Drugs? American
Economic Review, May 1991, 248-51.
Smith, R. and Blundell, R. An Exogeneity Test for a
Simultaneous Equation Tobit Model with an
Application to Labor Supply. Econometrica, May 1986, 679-85.
Stigler, G. and Becker, G. De Gustibus Non Est Disputandum.
American Economic Review, March
1977, 76-90.
Substance Abuse and Mental Health Services Administration
(SAMHSA). National Household
Survey on Drug Abuse: Population Estimates 1995. Rockville,
MD: U.S. Department of Health and
Human Services, 1996.
Yamaguchi, K. and Kandel, D. Patterns of Drug Use from
Adolescence to Young Adulthood: 111. Predic-
tors of Progression. American Journal of Public Health, July
1984, 673-81.
03:18:48 AM
http://www.jstor.org/page/info/about/policies/terms.jspArticle
Contentsp. 149p. 150p. 151p. 152p. 153p. 154p. 155p. 156p.
157p. 158p. 159p. 160p. 161p. 162p. 163p. 164Issue Table of
ContentsEastern Economic Journal, Vol. 24, No. 2 (Spring,
1998), pp. 127-251Front MatterMonetary Rules [pp. 127-
136]New Deal Agricultural Appropriations: A Political
Influence [pp. 137-148]Is Marijuana a Gateway Drug? [pp. 149-
164]Does the Minimum Wage Affect Employment in Mexico?

[pp. 165-180]Coping Rationally with Unpreferred Preferences
[pp. 181-194]Does the Federal Reserve Lexicographically Order
Its Policy Objectives? [pp. 195-206]Revisiting Long-Run
Industry Supply [pp. 207-215]Islamic and Neo-Confucian
Perspectives on the New Traditional Economy [pp. 217-
227]Other Things EqualSmall Worlds, or, the Preposterousness
of Closed Economy Macro [pp. 229-232]Book ReviewsReview:
untitled [pp. 233-235]Review: untitled [pp. 235-238]Review:
untitled [pp. 247-249]Review: untitled [pp. 250-251]Back
Matter
The University of Chicago
The Booth School of Business of the University of Chicago
The University of Chicago Law School
Medical Marijuana Laws, Traffic Fatalities, and Alcohol
Consumption
Author(s): D. Mark Anderson, Benjamin Hansen, and Daniel I.
Rees
Source: Journal of Law and Economics, Vol. 56, No. 2 (May
2013), pp. 333-369
Published by: The University of Chicago Press for The Booth
School of Business of the University of
Chicago and The University of Chicago Law School
Stable URL: http://www.jstor.org/stable/10.1086/668812 .
Accessed: 18/11/2014 03:00
Your use of the JSTOR archive indicates your acceptance of the
Terms & Conditions of Use, available at .
.

JSTOR is a not-for-profit service that helps scholars,
researchers, and students discover, use, and build upon a wide
range of
content in a trusted digital archive. We use information
technology and tools to increase productivity and facilitate new
forms
of scholarship. For more information about JSTOR, please
contact [email protected]
.
The University of Chicago Press, The University of Chicago,
The Booth School of Business of the University of
Chicago, The University of Chicago Law School are
collaborating with JSTOR to digitize, preserve and extend
access to Journal of Law and Economics.
http://www.jstor.org
03:00:05 AM
http://www.jstor.org/action/showPublisher?publisherCode=ucpr
ess
http://www.jstor.org/action/showPublisher?publisherCode=chica
gobooth
gobooth
golaw
http://www.jstor.org/stable/10.1086/668812?origin=JSTOR-pdf
333

[Journal of Law and Economics, vol. 56 (May 2013)]
� 2013 by The University of Chicago. All rights reserved.
0022-2186/2013/5602-0011$10.00
Medical Marijuana Laws, Traffic Fatalities,
and Alcohol Consumption
D. Mark Anderson Montana State University
Benjamin Hansen University of Oregon
Daniel I. Rees University of Colorado Denver
Abstract
To date, 19 states have passed medical marijuana laws, yet very
little is known
about their effects. The current study examines the relationship
between the
legalization of medical marijuana and traffic fatalities, the
leading cause of death
among Americans ages 5–34. The first full year after coming
into effect, legal-
ization is associated with an 8–11 percent decrease in traffic
fatalities. The impact
of legalization on traffic fatalities involving alcohol is larger
and estimated with
more precision than its impact on traffic fatalities that do not
involve alcohol.
Legalization is also associated with sharp decreases in the price
of marijuana
and alcohol consumption, which suggests that marijuana and
alcohol are sub-
stitutes. Because alternative mechanisms cannot be ruled out,
the negative re-

lationship between legalization and alcohol-related traffic
fatalities does not
necessarily imply that driving under the influence of marijuana
is safer than
driving under the influence of alcohol.
1. Introduction
Medical marijuana laws (MMLs) remove state-level penalties
for using, pos-
sessing, and cultivating medical marijuana. Patients are required
to obtain ap-
proval or certification from a doctor, and doctors who
recommend marijuana
to their patients are immune from prosecution. Medical
marijuana laws allow
patients to designate caregivers who can obtain marijuana on
their behalf.
We would like to thank Dean Anderson, Brian Cadena,
Christopher Carpenter, Chad Cotti, Ben-
jamin Crost, Scott Cunningham, Brian Duncan, Andrew
Friedson, Darren Grant, Mike Hanlon,
Rosalie Pacula, Henri Pellerin, Claus Pörtner, Randy Rucker,
Doug Young, and seminar participants
at Clemson University, Colorado State University, Cornell
University, and the National Bureau of
Economics and Research Health Economics Program Meeting in
April 2012 for comments and
suggestions.
03:00:05 AM

334 The Journal of LAW & ECONOMICS
On May 2, 2013, Maryland became the nineteenth state, along
with the District
of Columbia, to enact an MML. More than a dozen state
legislatures, including
those of Illinois, New York, and Pennsylvania, have recently
considered medical
marijuana bills. If these bills are eventually signed into law, the
majority of
Americans will live in states that permit the use of medical
marijuana.
Opponents of medical marijuana tend to focus on the social
issues surrounding
substance use. They argue that marijuana is addictive, serves as
a gateway drug,
has little medicinal value, and leads to criminal activity (Adams
2008; Blankstein
2010). Proponents argue that marijuana is both efficacious and
safe and can be
used to treat the side effects of chemotherapy as well as the
symptoms of AIDS,
multiple sclerosis, epilepsy, glaucoma, and other serious
illnesses. They cite clin-
ical research showing that marijuana relieves chronic pain,
nausea, muscle
spasms, and appetite loss (Eddy 2010; Marmor 1998; Watson,
Benson, and Joy
2000) and note that neither the link between the use of medical
marijuana and
the use of other substances nor the link between medical
marijuana and criminal
activity has been substantiated (Belville 2011; Corry et al.

2009; Hoeffel 2011).
This study begins by using price data collected from back issues
of High Times,
the leading cannabis-related magazine in the United States, to
explore the effects
of MMLs on the market for marijuana. Our results are
consistent with anecdotal
evidence that MMLs have led to a substantial increase in the
supply of high-
grade marijuana (Montgomery 2010). In contrast, the impact of
MMLs on the
market for low-quality marijuana appears to be modest.
Next, we turn our attention to MMLs and traffic fatalities, the
primary re-
lationship of interest. Traffic fatalities are the leading cause of
death among
Americans ages 5–34.1 To our knowledge, there has been no
previous examination
of this relationship. Data on traffic fatalities at the state level
are obtained from
the Fatality Analysis Reporting System (FARS) for the years
1990–2010. Fourteen
states and the District of Columbia enacted an MML during this
period. The
FARS information includes the time of day the traffic fatality
occurred, the day
of the week it occurred, and whether alcohol was involved.
Using this infor-
mation, we contribute to the long-standing debate on whether
marijuana and
alcohol are substitutes or complements.
The first full year after coming into effect, the legalization of
medical marijuana

is associated with an 8–11 percent decrease in traffic fatalities.
However, the
effect of MMLs on traffic fatalities involving alcohol is larger
and estimated with
more precision than the effect of MMLs on traffic fatalities that
do not involve
alcohol. In addition, we find that the estimated effects of MMLs
on fatalities at
night and on weekends (when the level of alcohol consumption
increases) are
larger, and are more precise, than the estimated effects of
MMLs on fatalities
during the day and on weekdays.
1 These 2010 data on leading causes of fatalities are from the
Centers for Disease Control and
Prevention’s Web-Based Injury Statistics Query and Reporting
System (http://www.cdc.gov/injury/
wisqars).
03:00:05 AM
http://www.cdc.gov/injury/wisqars
http://www.cdc.gov/injury/wisqars
Medical Marijuana Laws 335
Finally, the relationship between MMLs and more direct
measures of alcohol
consumption is examined. Using individual-level data from the
Behavioral Risk
Factor Surveillance System (BRFSS) for the period 1993–2010,

we find that
MMLs are associated with decreases in the probability of
having consumed
alcohol in the past month, binge drinking, and the number of
drinks consumed.
We conclude that alcohol is the likely mechanism through
which the legali-
zation of medical marijuana reduces traffic fatalities. However,
this conclusion
does not necessarily imply that driving under the influence of
marijuana is safer
than driving under the influence of alcohol. Alcohol is often
consumed in res-
taurants and bars, while many states prohibit the use of medical
marijuana in
public. If marijuana consumption typically takes place at home
or other private
locations, then legalization could reduce traffic fatalities simply
because mari-
juana users are less likely to drive while impaired.
2. Background
2.1. A Brief History of Medical Marijuana
Marijuana was introduced in the United States in the early
1600s by Jamestown
settlers who used the plant in hemp production; hemp
cultivation remained a
prominent industry until the mid-1800s (Deitch 2003). During
the census of
1850, the United States recorded more than 8,000 cannabis
plantations of at
least 2,000 acres (Cannabis Campaigners Guide 2011).
Throughout this period,

marijuana was commonly used by physicians and pharmacists to
treat a broad
spectrum of ailments (Pacula et al. 2002). From 1850 to 1942,
marijuana was
included in the United States Pharmacopoeia, the official list of
recognized me-
dicinal drugs (Bilz 1992).
In 1913, California passed the first marijuana prohibition law
aimed at rec-
reational use (Gieringer 1999); by 1936, the remaining 47 states
had followed
suit (Eddy 2010). In 1937, the Marihuana Tax Act (Pub. L. No.
75-238, ch. 553,
50 Stat. 551 [1937]) effectively discontinued the use of
marijuana for medicinal
purposes (Bilz 1992), and marijuana was classified as a
Schedule I drug in 1970.2
According to the Controlled Substances Act, a Schedule I drug
must have a
“high potential for abuse” and “no currently accepted medical
use in treatment
in the United States” (Eddy 2010, p. 3).
In 1996, California passed the Compassionate Use Act, which
removed crim-
inal penalties for using, possessing, and cultivating medical
marijuana. It also
provided immunity from prosecution to physicians who
recommended the use
of medical marijuana to their patients. Before 1996, a number of
states allowed
doctors to prescribe marijuana, but this had little practical
effect because of

2 The Marihuana Tax Act imposed a registration tax and
required extensive record keeping and
thus increased the cost of prescribing marijuana as compared to
other drugs (Bilz 1992).
03:00:05 AM
Table 1
Medical Marijuana Laws, 1990–2010
State Effective Date
Alaska March 4, 1999
California November 6, 1996
Colorado June 1, 2001
District of Columbia July 27, 2010
Hawaii December 28, 2000
Maine December 22, 1999
Michigan December 4, 2008
Montana November 2, 2004
Nevada October 1, 2001
New Jersey October 1, 2010
New Mexico July 1, 2007
Oregon December 3, 1998
Rhode Island January 3, 2006
Vermont July 1, 2004
Washington November 3, 1998

Note. Arizona, Connecticut, Delaware, Maryland, and
Massachusetts legalized medical marijuana after 2010.
federal restrictions.3 Since 1996, 18 other states and the
District of Columbia
have joined California in legalizing the use of medical
marijuana (Table 1),
although it is still classified as a Schedule I drug by the federal
government.4
2.2. Studies on Substance Use and Driving
Laboratory studies have shown that cannabis use impairs
driving-related func-
tions such as distance perception, reaction time, and hand-eye
coordination
(Kelly, Darke, and Ross 2004; Sewell, Poling, and Sofuoglu
2009). However,
neither simulator nor driving-course studies provide consistent
evidence that
these impairments to driving-related functions lead to an
increased risk of col-
lision (Kelly, Darke, and Ross 2004; Sewell, Poling, and
Sofuoglu 2009), perhaps
because drivers under the influence of tetrahydrocannabinol
(THC), the primary
psychoactive substance in marijuana, engage in compensatory
behaviors such as
reducing their velocity, avoiding risky maneuvers, and
increasing their following
distances (Kelly, Darke, and Ross 2004; Sewell, Poling, and
Sofuoglu 2009).
Like marijuana, alcohol impairs driving-related functions such
as reaction time
and hand-eye coordination (Kelly, Darke, and Ross 2004;

Sewell, Poling, and
Sofuoglu 2009). Moreover, simulator and driving-course studies
provide une-
3 Federal regulations prohibit doctors from writing
prescriptions for marijuana. In addition, even
if a doctor were to illegally prescribe marijuana, it would be
against federal law for pharmacies to
distribute it. Doctors in states that have legalized medical
marijuana avoid violating federal law by
recommending marijuana to their patients rather than
prescribing its use.
4 Information on when medical marijuana laws (MMLs) were
passed was obtained from a Con-
gressional Research Services Report by Eddy (2010). Although
the New Jersey medical marijuana
law went into effect on October 1, 2010, implementation has
been delayed (Brittain 2012). Coding
New Jersey as a state without medical marijuana in 2010 has no
appreciable impact on our results.
03:00:05 AM
quivocal evidence that alcohol consumption leads to an
increased risk of collision
(Kelly, Darke, and Ross 2004; Sewell, Poling, and Sofuoglu
2009). Even at low
doses, drivers under the influence of alcohol tend to

underestimate the degree
to which they are impaired (MacDonald et al. 2008; Marczinski,
Harrison, and
Fillmore 2008; Robbe and O’Hanlon 1993; Sewell, Poling, and
Sofuoglu 2009),
drive at faster speeds, and take more risks (Burian, Liguori, and
Robinson 2002;
Ronen et al. 2008; Sewell, Poling, and Sofuoglu 2009). When
used in conjunction
with marijuana, alcohol appears to have an “additive or even
multiplicative”
effect on driving-related functions (Sewell, Poling, and
Sofuoglu 2009, p. 186),
although chronic marijuana users may be less impaired by
alcohol than infre-
quent users (Jones and Stone 1970; Marks and MacAvoy 1989;
Wright and Terry
2002).5
2.3. The Relationship between Marijuana and Alcohol
Although THC has not been linked to an increased risk of
collision in simulator
and driving-course studies, MMLs could impact traffic fatalities
through the
consumption of alcohol. While a number of studies have found
evidence of
complementarity between marijuana and alcohol (Pacula 1998;
Farrelly et al.
1999; Williams et al. 2004), others lend support to the
hypothesis that marijuana
and alcohol are substitutes. For instance, Chaloupka and
Laixuthai (1997) and
Saffer and Chaloupka (1999) found that marijuana
decriminalization led to
decreased alcohol consumption, while DiNardo and Lemieux

(2001) found that
increases in the minimum legal drinking age were positively
associated with the
use of marijuana.
Two recent studies used a regression discontinuity approach to
examine the
effect of the minimum legal drinking age on marijuana use but
came to different
conclusions. Crost and Guerrero (2012) analyzed data from the
National Survey
on Drug Use and Health (NSDUH). They found that marijuana
use decreased
sharply at 21 years of age, evidence consistent with
substitutability between
alcohol and marijuana. In contrast, Yörük and Yörük (2011),
who drew on data
from the National Longitudinal Survey of Youth 1997
(NLSY97), concluded that
alcohol and marijuana were complements. However, these
authors appear to
have inadvertently conditioned on having used marijuana at
least once since the
last interview. When Crost and Rees (2013) applied Yörük and
Yörük’s (2011)
research design to the NLSY97 data without conditioning on
having used mar-
ijuana since the last interview, they found no evidence that
alcohol and marijuana
were complements.
5 A large body of research in epidemiology attempts to assess
the effects of substance use on the
basis of observed tetrahydrocannabinol and alcohol levels in the
blood of drivers who have been in
accidents. For marijuana, the results have been mixed, while the

likelihood of an accident occurring
clearly increases with blood alcohol concentration (BAC) levels
(Sewell, Poling, and Sofuoglu 2009).
03:00:05 AM
3. Medical Marijuana Laws and the Marijuana Market
Medical marijuana laws should, in theory, increase both the
supply of mar-
ijuana and the demand for marijuana, unambiguously leading to
an increase in
consumption (Pacula et al. 2010). They afford suppliers some
protection against
prosecution and allow patients to buy medical marijuana
without fear of being
arrested or fined, which lowers the full cost of obtaining
marijuana.6 Because it
is prohibitively expensive for the government to ensure that all
medicinal mar-
ijuana ends up in the hands of registered patients (especially in
states that permit
home cultivation), diversion to nonpatients almost certainly
occurs.7
The NSDUH is the best source of information on marijuana
consumption by
adults living in the United States. However, the NSDUH does
not provide

individual-level data with state identifiers to researchers and
did not publish
state-level estimates of marijuana use prior to 1999.8 Because
five states (including
California, Oregon, and Washington) legalized medical
marijuana during the
period 1996–99, we turn to back issues of High Times magazine
in order to
gauge the impact of legalization on the marijuana market.
Begun in 1975, High
Times is published monthly and covers topics ranging from
marijuana cultivation
to politics. Each issue also contains a section entitled “Trans
High Market Quo-
tations” in which readers provide marijuana prices from across
the country. In
addition to price, a typical entry includes information about
where the marijuana
was purchased, its strain, and its quality.
We collected price information from High Times for the period
1990–2011.
Jacobson (2004), who collected information on the price of
marijuana from High
6 The majority of MMLs allow patients to register on the basis
of medical conditions that cannot
be objectively confirmed (for example, chronic pain and
nausea). In fact, chronic pain is the most
common medical condition among patients seeking treatment
(see Table A1). According to recent
Arizona registry data, only seven of 11,186 applications for
medical marijuana have been denied
approval. Sun (2010) described “quick-in, quick-out mills,”
where physicians provide recommen-
dations for a nominal fee. Cochran (2010) reported on doctors

providing medical marijuana rec-
ommendations to patients via brief Web interviews on Skype.
7 Aside from Washington, D.C., and New Jersey, all MMLs
enacted during the period 1990–2010
allowed for home cultivation, and eight of 15 allowed patients
or caregivers to cultivate collectively
(see Table A2). A recent investigation concluded that thousands
of pounds of medical marijuana
grown in Colorado are diverted annually to the recreational
market (Wirfs-Brock, Seaton, and
Sutherland 2010). Thurstone, Lieberman, and Schmiege (2011)
interviewed 80 adolescents (15–19
years of age) undergoing outpatient substance abuse treatment
in Denver. Thirty-nine of the 80
reported having obtained marijuana from someone with a
medical marijuana license. Florio (2011)
described the story of four eighth graders in Montana who
received marijuana-laced cookies from
a registered medical marijuana patient.
8 Using these estimates, Wall et al. (2011, p. 714) found that
rates of marijuana use among 12–
17-year-olds were higher in states that had legalized medical
marijuana than in states that had not,
but they noted that “in the years prior to MML passage, there
was already a higher prevalence of
use and lower perceptions of risk” in states that had legalized
medical marijuana. Using NSDUH
data for the years 2002–9, Harper, Strumpf, and Kaufman
(2012) found that legalization was associated
with a small reduction in the rate of marijuana use among 12–
17-year-olds. Using data for the period
1995–2002 from Denver, Los Angeles, Portland, San Diego, and
San Jose, Gorman and Huber (2007)
found little evidence that marijuana consumption increased

among adult arrestees as a result of
legalization.
03:00:05 AM
Table 2
Medical Marijuana Laws and the Price of High-Quality
Marijuana, 1990–2011
(1) (2) (3) (4) (5)
MML �.304**
(.037)
�.103�
(.058)
3 Years before MML .022
(.074)
(.075)
1 Year before MML �.037
(.076)
Year of law change �.117�

(.061)
�.059
(.069)
�.060
(.096)
1 Year after MML �.156**
(.044)
�.082
(.070)
�.084
(.097)
2 Years after MML �.203**
(.074)
�.110
(.082)
�.113
(.120)
(.062)
�.128
(.084)
�.130
(.118)

(.123)
�.283*
(.115)
�.286*
(.125)
5� Years after MML �.439**
(.048)
�.257*
(.116)
�.262�
(.145)
R2 .224 .310 .241 .315 .315
State-specific linear time trends No Yes No Yes Yes
Note. The dependent variable is equal to the natural log of the
median price of marijuana in state s and
year t. Standard errors, corrected for clustering at the state
level, are in parentheses. Year fixed effects, state
fixed effects, and state covariates are included in all
specifications. MML p medical marijuana law. N p
920.
� Statistically significant at the 10% level.
* Statistically significant at the 5% level.
** Statistically significant at the 1% level.
Times for the period 1975–2000, distinguished between high-
quality (a category
that included Californian and Hawaiian sinsemilla) and low-
quality (a category
that included commercial grade Colombian and Mexican weed)

marijuana.9
Following Jacobson (2004), we classified marijuana purchases
by quality and
calculated the median per-ounce price by state and year.10
Table 2 presents
9 The plant variety (that is, strain), which part of the plant is
used, the method of storage, and
cultivation techniques are all important determinants of quality
and potency (McLaren et al. 2008).
In recent decades, there has been a marked trend toward indoor
cultivation and higher potency in
the United States (McLaren et al. 2008). Jacobson (2004)
argued that, ideally, prices would be deflated
by a measure of potency. Unfortunately, information on potency
is not available in the High Times
data.
10 A total of 8,271 purchases were coded. Of these, 7,029 were
classified as high quality and 1,242
were classified as low quality. Prior to 2004, information on the
seller was occasionally included in
the “Trans High Market Quotations” section of High Times.
Although dispensaries were never men-
tioned, they are a relatively recent phenomenon. The number of
dispensaries in California expanded
rapidly after 2004 (Jacobson et al. 2011), and the number of
dispensaries in Colorado and Montana
expanded rapidly after 2008 (Smith 2011, 2012). We compared
High Times price data for 2011–12
with price data posted on the Internet by 84 dispensaries located
in seven states. In four states
(California, Michigan, Nevada, and Washington), the prices
charged by dispensaries were statistically

03:00:05 AM
estimates of the following equation:
ln(Price of high-quality marijuana ) p b � b MML � X bst 0 1
st st 2 (1)
� v � w � � ,s t st
where s indexes states and t indexes years. The variable MMLst
indicates whether
medical marijuana was legal in state s and year t, and b1
represents the estimated
relationship between legalization and the per ounce price of
high-quality mar-
ijuana. The vector Xst includes controls for the mean age in
state s and year t,
the unemployment rate, per capita income, whether the state had
a marijuana
decriminalization law in place, and the beer tax. State fixed
effects, represented
by vs, control for time-invariant unobservable factors at the
state level; year fixed
effects, represented by wt, control for common shocks to the
price of high-quality
marijuana.11
The baseline estimate suggests that the supply response to
legalization is larger

than the demand response. In particular, legalization is
associated with a 26.2
percent ( ) decrease in the price of high-quality
marijuana.�.304e � 1 p �.262
When we include state-specific linear time trends, intended to
control for omitted
variables at the state level that evolve at a constant rate,
legalization is associated
with a 9.8 percent decrease in the price of high-quality
marijuana.
Lagging the MML indicator provides evidence that the effect of
legalization
on the price of high-quality marijuana is not immediate.
Controlling for state-
specific linear time trends, we see that the estimated
coefficients of the MML
indicator lagged 1–3 years are negative but not statistically
significant. There is
indistinguishable from the prices provided by High Times
readers. In Arizona, Colorado, and Oregon,
the prices charged by dispensaries were significantly lower than
the prices provided by High Times
readers; however, these differences were generally not large in
magnitude. The greatest difference
was in Colorado, where dispensaries, on average, charged 24.4
percent less per ounce ($72.80) than
the prices provided by High Times readers. In Arizona,
dispensaries, on average, charged 10.3 percent
less per ounce ($36.60) than the prices provided by High Times
readers; in Oregon, dispensaries, on
average, charged 14.9 percent less per ounce ($37.20) than the
prices provided by High Times readers
(for dispensary price data, see WeedMaps.com, Dispensaries
[http://www.legalmarijuanadispensary

.com]).
11 Standard errors are corrected for clustering at the state level
(Bertrand, Duflo, and Mullainathan
2004). Descriptive statistics are presented in Table A3. The
mean age in state s and year t was
calculated using census data. The data on beer taxes are from
Brewers Almanac (Beer Institute 1990–
2010). The unemployment and income data are from the Bureau
of Labor Statistics and the Bureau
of Economic Analysis, respectively. The data on
decriminalization laws are from Model (1993) and
Scott (2010). During the period under study, the
decriminalization indicator captures only two policy
changes: Nevada and Massachusetts decriminalized the use of
marijuana in 2001 and 2010, respec-
tively. The majority of decriminalization laws were passed prior
to 1990. Following Jacobson’s ap-
proach (2004), the estimates presented in Tables 2 and 3 are
unweighted. When the regressions are
weighted by the number of observations used to calculate the
median price and state-specific linear
time trends are included on the right-hand side, estimates of the
relationship between legalization
and price are smaller and less precise than those reported in
Tables 2 and 3. Nevertheless, they
continue to show that legalization is associated with a
statistically significant reduction in the price
of high-quality marijuana after 4 years. When the regressions
are weighted by the number of ob-
servations used to calculate the median price but state-specific
linear time trends are not included
on the right-hand side, estimates of the relationship between
legalization and price are similar to
those reported in Tables 2 and 3.

03:00:05 AM
http://www.legalmarijuanadispensary.com
http://www.legalmarijuanadispensary.com
Table 3
Medical Marijuana Laws and the Price of Low-Quality
Marijuana, 1990–2011
(1) (2) (3) (4) (5)
MML �.096
(.105)
�.075
(.150)
(.197)
(.108)
1 Year before MML �.088
(.200)
Year of law change �.035
(.154)

�.056
(.193)
�.013
(.196)
1 Year after MML �.250�
(.146)
�.182
(.176)
�.106
(.136)
2 Years after MML �.058
(.176)
�.016
(.190)
.053
(.166)
3 Years after MML �.244*
(.098)
�.114
(.141)
�.028
(.138)
4 Years after MML .032
(.403)

.046
(.373)
.131
(.429)
5� Years after MML �.038
(.073)
.271
(.335)
.370
(.267)
R2 .720 .748 .723 .751 .753
State-specific linear time trends No Yes No Yes Yes
Note. The dependent variable is equal to the natural log of the
median price of marijuana in state s and
year t. Standard errors, corrected for clustering at the state
level, are in parentheses. Year fixed effects, state
fixed effects, and state covariates are included in all
specifications. MML p medical marijuana law. N p
483.
� Statistically significant at the 10% level.
* Statistically significant at the 5% level.
a statistically significant 24.6 percent reduction in the price of
high-quality mar-
ijuana in the fourth full year after legalization. This pattern of
results is consistent
with state registry data from Colorado, Montana, and Rhode
Island showing
that patient numbers increased slowly in the years immediately

after legalization.12
Adding leads to the model with state-specific linear time trends
produces no
evidence that legalization was systematically preceded by
changes in tastes or
policies related to the market for high-quality marijuana.
Estimates of the relationship between legalization and the price
of low-quality
marijuana are presented in Table 3. The majority of these
estimates are negative.
However, with two exceptions, they are statistically
insignificant. Given that much
of the medicinal crop is grown indoors under ultraviolet lights
and that high-
12 Table A1 presents registry information by state. Montana
legalized medical marijuana in No-
vember 2004. Two years later, only 287 patients were
registered; 7 years later, 30,036 patients were
registered. The number of registered patients in Colorado
increased from 5,051 in January 2009 to
128,698 in June 2011. Patient numbers also appear to be
growing rapidly in Arizona, which passed
the Arizona Medical Marijuana Act on November 2, 2010. A
total of 11,133 patient applications had
been approved as of August 29, 2011; 40,463 patient
applications had been approved by June 30,
2012.
03:00:05 AM

potency and high-quality strains such as Northern Lights and
Super Silver Haze
are favored by medical marijuana cultivators, this imprecision is
not surprising.
4. Medical Marijuana Laws and Traffic Fatalities
The estimates discussed above suggest that legalization leads to
a substantial
decrease in the price of high-quality marijuana and, presumably,
a correspond-
ingly large increase in consumption.13 In this section, we test
whether the impact
of legalization extends to traffic fatalities.
4.1. Data on Traffic Fatalities
We use data from FARS for the period 1990–2010 to examine
the relationship
between MMLs and traffic fatalities. These data are collected
by the National
Highway Traffic Safety Administration and represent an annual
census of all
fatal injuries suffered in motor vehicle accidents in the United
States. Information
on the circumstances of each crash and the persons and vehicles
involved is
obtained from a variety of sources, including police crash
reports, driver licensing
files, vehicle registration files, state highway department data,
emergency medical
services records, medical examiners’ reports, toxicology

reports, and death certifi-
cates.
Table 4 presents descriptive statistics and definitions for our
outcome mea-
sures. The variable Fatalities Totalst is equal to the number of
traffic fatalities
per 100,000 people of state s in year t.14 The variables
Fatalities (BAC 1 0)st and
Fatalities (BAC ≥ .10)st allow us to examine the effects of
legalization by alcohol
involvement. The variable Fatalities (BAC 1 0)st is equal to the
number of traffic
fatalities per 100,000 people resulting from accidents in which
at least one driver
had a positive blood alcohol concentration (BAC). The variable
Fatalities (BAC
≥ .10)st is defined analogously, but at least one driver had to
have a BAC greater
than or equal to .10. The variable Fatalities (No Alcohol)st is
equal to the number
of fatalities per 100,000 people in which alcohol involvement
was not reported.15
13 If we assume, conservatively, that legalization has a
negligible impact on demand, then the
change in marijuana consumption is equal to the elasticity of
demand multiplied by the percentage
change in price. Only a handful of researchers have estimated
the price elasticity of demand for
marijuana. Using data on University of California, Los Angeles,
undergraduates, Nisbet and Vakil
(1972) estimated a price elasticity of demand between �1.01
and �1.51; using data from Monitoring
the Future on high school seniors, Pacula et al. (2001) estimated

a 30-day participation elasticity
between �.002 and �.69; using data from the Harvard College
Alcohol Study, Williams et al. (2004)
estimated a 30-day participation elasticity of �.24.
14 For population data, see National Cancer Institute, US
Population Data—1969–2011 (http://
seer.cancer.gov/popdata/index.html). According to Eisenberg
(2003), traffic fatalities in the Fatality
Analysis Reporting System (FARS) are measured with little to
no error. We experimented with scaling
traffic fatalities by the population of licensed drivers and by the
number of miles driven in state s
and year t rather than by the state population. These estimates,
which are similar in terms of magnitude
and precision to those presented here, are available on request.
15 The numerator for Fatalities (No Alcohol)st was determined
from two sources in FARS. First,
either all drivers involved had to have registered a BAC of zero
or, if BAC information was missing,
the police had to report that alcohol was not involved.
03:00:05 AM
http://seer.cancer.gov/popdata/index.html
http://seer.cancer.gov/popdata/index.html
Table 4

Dependent Variables for the Fatality Analysis Reporting System
Analysis
Dependent Variable Mean Description
Fatalities Total 14.58 (5.05) Fatalities per 100,000 people
Fatalities (No Alcohol) 9.67 (3.45) Fatalities per 100,000
people with no indication of
alcohol involvement
Fatalities (BAC 1 0) 3.97 (1.74) Fatalities per 100,000 people
for which at least one
driver involved had a blood alcohol
concentration (BAC) 1 .00
Fatalities (BAC ≥ .10) 3.13 (1.43) Fatalities per 100,000 people
for which at least one
driver involved had a BAC 1 .10
Fatalities, 15–19 24.55 (9.75) Fatalities per 100,000 people 15–
19 years of age
29 years of age
39 years of age
49 years of age
59 years of age
Fatalities, 60� 17.39 (5.28) Fatalities per 100,000 people 60
years old and above
Fatalities Males 20.48 (7.15) Fatalities per 100,000 males
Fatalities Females 9.03 (3.29) Fatalities per 100,000 females
Fatalities Weekdays 8.32 (2.88) Fatalities per 100,000 people
on weekdays

Fatalities Weekends 6.22 (2.25) Fatalities per 100,000 people
on weekends
Fatalities Daytime 7.04 (2.59) Fatalities per 100,000 people
during the day
Fatalities Nighttime 7.42 (2.60) Fatalities per 100,000 people
during the night
Note. The data are weighted means based on the Fatality
Analysis Reporting System state-level panel for
1990–2010. Standard deviations are in parentheses.
Alcohol involvement is likely measured with error (Eisenberg
2003), and the
possibility exists that some states collected information on BAC
levels more
diligently than others.16 Focusing on nighttime and weekend
fatal crashes can
provide additional insight into the role of alcohol and help
address the mea-
surement error issue. As noted by Dee (1999), a substantial
proportion of fatal
crashes on weekends and at night involve alcohol.
As of 2011, 75 percent of the patients on the Arizona medical
marijuana
registry were male; 69 percent of the patients on the Colorado
registry were
male. There is also evidence that many medical marijuana
patients are below
the age of 40. Forty-eight percent of registered patients in
Montana and 42
percent of registered patients in Arizona were between the ages
of 18 and 40;
the average age of registered patients in Colorado was 40.17 To
the extent that
registered patients below the age of 40 are more likely to use

medical marijuana
recreationally, heterogeneous effects across the age distribution
might be
expected.
Figures 1–3 compare pre- and postlegalization traffic fatality
trends by age
16 We also experimented with calculating the alcohol-related
fatality rates with the imputed BAC
levels available in the FARS data. These estimates, which are
similar in terms of magnitude and
precision to those presented here, are available on request. See
Adams, Blackburn, and Cotti (2012)
for a discussion of the BAC imputation method.
17 For links to state registry data, see NORML, Medical
Marijuana (http://norml.org/index.cfm
?Group_IDp3391).
03:00:05 AM
http://norml.org/index.cfm?Group_ID=3391
http://norml.org/index.cfm?Group_ID=3391
Figure 1. Pre- and postlegalization trends in traffic fatality
rates, ages 15–19
rates, ages 20–39

group.18 In each figure, the solid line represents the average
traffic fatality rate
for the treated states (those that legalized medical marijuana).
The dashed line
represents the average fatality rate for the control states (those
that did not
legalize medical marijuana). Year 0 on the horizontal axis
represents the year in
which legalization took place. Control states were randomly
assigned a year of
legalization between 1996 and 2010.
18 Figures 1–3 are based on FARS data for the period 1990–
2010. Fatality rates are expressed relative
to year �1 and are weighted by the relevant population in state
s and year t.
03:00:05 AM
rates, ages 40 and older
Among teenagers (ages 15–19), young adults (ages 20–39), and
older adults
(ages 40 and above), average traffic fatality rates in the treated
states closely
follow those in the control states through year �1. This finding
is important

because it suggests that legalization was not preceded by, for
instance, new anti-
drunk-driving policies, increased spending on law enforcement,
or highway im-
provements. In the years immediately after legalization, average
traffic fatality
rates in MML states fall faster than average traffic fatality rates
in the control
states. This divergence is most pronounced among those 20–39.
Among teenagers
and older adults, average traffic fatality rates in the MML states
converge with
average traffic fatality rates in the control states 4–5 years after
legalization.
4.2. The Empirical Model
To further explore the relationship between legalization and
traffic fatalities,
we estimate the following baseline equation:
ln(Fatalities Total ) p b � b MML � X b � v � w � � , (2)st 0
1 st st 2 s t st
where s indexes states and t indexes years. The coefficient of
interest, b1, represents
the effect of legalizing medical marijuana.19 In alternative
specifications, we re-
place Fatalities Totalst with the remaining outcomes listed in
Table 4.
The vector Xst is composed of the controls described in Table
5, and vs and
wt are state and year fixed effects, respectively. Previous
studies provide evidence
that a variety of state-level policies can impact traffic fatalities.

For instance,
graduated driver-licensing regulations and stricter seat belt laws
are associated
19 This specification is based on Dee (2001), who examined the
relationship between .08 BAC laws
(making it illegal for drivers to have a BAC of .08 percent or
higher) and traffic fatalities.
03:00:05 AM
T
ab
le
5
In
d
ep
en
d
en
t
V
ar
ia

b
le
s
fo
r
th
e
F
at
al
it
y
A
n
al
ys
is
R
ep
o
rt
in
g
S

ys
te
m
A
n
al
ys
is
In
d
ep
en
d
en
t
V
ar
ia
b
le
M
ea
n
D

es
cr
ip
ti
o
n
M
M
L
a
.1
30
(.
3
34
)
E
q
u
al
s
o
n
e

if
a
st
at
e
h
ad
a
m
ed
ic
al
m
ar
ij
u
an
a
la
w
in
a
gi
ve

n
ye
ar
an
d
ze
ro
o
th
er
w
is
e
M
ea
n
A
ge
3
5
.9
0
(1
.6

6
)
M
ea
n
ag
e
o
f
th
e
st
at
e
p
o
p
u
la
ti
o
n
U
n

em
p
lo
ym
en
t
5
.8
7
(1
.8
7)
St
at
e
u
n
em
p
lo
ym
en
t
ra

te
In
co
m
e
1
0
.2
7
(.
15
6
)
N
at
u
ra
l
lo
ga
ri
th
m
o

f
st
at
e
re
al
in
co
m
e
p
er
ca
p
it
a
(2
00
0
$
)
M
il
es

D
ri
ve
n
1
4
.1
3
(2
.0
5
)
V
eh
ic
le
m
il
es
d
ri
ve
n
p

er
li
ce
n
se
d
d
ri
ve
r
(t
h
o
u
sa
n
d
s
o
f
m
il
es
)

D
ec
ri
m
in
al
iz
ed
a
.3
30
(.
4
70
)
E
q
u
al
s
o
n
e
if

a
st
at
e
h
ad
a
m
ar
ij
u
an
a
d
ec
ri
m
in
al
iz
at
io
n
la

w
in
a
gi
ve
n
ye
ar
an
d
ze
ro
o
th
er
w
is
e
D
ru
g
P
er
Se

.1
42
(.
3
45
)
E
q
u
al
s
o
n
e
if
a
st
at
e
h
ad
a
d
ru

g
p
er
se
la
w
in
a
gi
ve
n
ye
ar
an
d
ze
ro
o
th
er
w
is
e
G

D
L
a
.5
22
(.
4
93
)
E
q
u
al
s
o
n
e
if
a
st
at
e
h
ad

a
gr
ad
u
at
ed
d
ri
ve
r-
li
ce
n
si
n
g
la
w
w
it
h
an
in
te

rm
ed
ia
te
p
h
as
e
in
a
gi
ve
n
ye
ar
an
d
ze
ro
o
th
er
w
is

e
P
ri
m
ar
y
Se
at
B
el
ta
.4
61
(.
4
94
)
E
q
u
al
s
o
n

e
if
a
st
at
e
h
ad
a
p
ri
m
ar
y
se
at
b
el
t
la
w
in
a
gi

Is Marijuana a Gateway DrugAuthor(s) Jeffrey DeSimoneSou.docx

Is Marijuana a Gateway DrugAuthor(s) Jeffrey DeSimoneSou.docx

Recommended

Recommended

More Related Content

Similar to Is Marijuana a Gateway DrugAuthor(s) Jeffrey DeSimoneSou.docx

Similar to Is Marijuana a Gateway DrugAuthor(s) Jeffrey DeSimoneSou.docx (20)

More from priestmanmable

More from priestmanmable (20)

Recently uploaded

Recently uploaded (20)

Is Marijuana a Gateway DrugAuthor(s) Jeffrey DeSimoneSou.docx