Information, Marijuana and the Youth:
evidence from French epidemiological data.
Fabrice Etilé
INRA-CORELA
May 23, 2002
Abstract
Using French epidemiological micro-data on the health and the lifestyle
of teenagers in secondary schools, we measure the impact of a number of
information providers on youth marijuana consumption. Information variables are instrumented so as to pick up the pure preventative e¤ects only,
we control for habit formation and use of Heckman-Singer semi-parametric
techniques allows us to control for unobserved heterogeneity, and to characterise the p opulation by latent classes. There are three latent classes,
which represents 58, 23 and 19% of the sample. We use psychological
variables and information on other risky behaviours to show that groups
do not di¤er by their time preferences but rather by their risk aversions.
A general conclusion is that information policies may have heterogeneous
and counterproductive e¤ects. Further, we provide evidences that information a¤ects both the preferences and the risk perceptions, and thus has
perhaps only transient e¤ects.
J.E.L. Classi…cation : C24,D83,I12,I18
Keywords: Marijuana, Alcohol, Information.
Acknowledgement 1 I have bene…tted from discussions with Andrew
Clark, Patrick Peretti-Watel, Pierre Kopp. I thank seminar participants
at OFDT/MILDT, INRA/CORELA, University of Paris 1/TEAM. I gratefully acknowledge research support for this paper from MILDT/INSERM,
University of Paris-1/TEAM and INRA/CORELA. Usual disclaim applies.
1
“They say, and it is nearly true, that this substance does not cause any physical ill;
or at least no grave one; but can one a¢rm that a man incapable of action and …t only
for dreaming is really in good health, even when every part of him functions perfectly?
Now we know human nature su¢ciently well to b e assured that a man who can with a
spoonful of sweetmeat procure for himself incidentally all the treasures of heaven and
of earth will never gain the thousandth part of them by working for them. Can you
imagine to yourself a State of which all the citizens should be hashish drunkards?”
Charles Baudelaire (1860) in “Arti…cial Paradises...On haschish and wine as means
of expanding individuality”
1
Introduction
Within less than a decade, marijuana use has increased dramatically among
French teenagers. By 1993, annual prevalence among 17-year old male was
21%. Six years later, 47% of this gender-age class reported use in the past
year [(13), (5)]. Marijuana use is associated with a number of physical and
psychological damages such as increased risk of airways diseases, changes in
motivation, cognitive impairments, tolerance e¤ects, withdrawal symptoms for
chronic heavy smokers, and perhaps gateway e¤ect [(40), (31), (32), (26), (15),
(47), (45), (50)]. Non-medical use of cannabis does not only generate social costs
(through increasing utilization of medical services for example, [(40), (11), (47),
(45), (33)])1 , it could also give rise to well-being losses for the consumer. This
is the case when she is not perfectly informed of the e¤ects of consumption on
her future preferences (through habit formation and changes in health capital)2 .
Some recent works emphasize the impact of risks perception and social “climat”
on trends in the prevalence of marijuana use in the U.S.A. [(2), (45)]. In light
of this result, information as well as price could be an e¢cient regulatory tool.
Understanding the e¤ects of information on youth marijuana consumption may
allow us to develop preventative strategies to reduce consumption3 .
The French drug regulation agency (MILDT) has designed its policies on the
assumption that information will impact drug consumption e¢ciently. However,
1 Social costs generated by cannabis consumption are qualitatively similar to those of any
illegal drug: accidents, cardio-vascular and cancerous morbidity, tra¢c-related violence. In
France, some rough estimates of these costs shows that they are greatly inferior to those of
le gal drugs [ (33)]. However, they should sharply increase with the ageing of the former users’
population.
2 This is also the case if she is inconsistent i.e. if she does not do what she planned to
do even though her environment remains stable. This kind of behaviour can be rationalized
by assuming non-stable preferences or hyperbolic discounting. It is always possible to tax
addictive products in such a way that hyperbolic discounters choices are those of exponential
discounte rs facing the true market prices. [(24)].
3 Usual variables to regulate addictive consumptions such as excise taxes, spatial, temporal
or age-based selling/consumption restrictions are likely to be useless for cannabis. Sparse
estimates yield negative price elasticities ofdemand for marijuana [(41),(44), (10)]. As price
data are rarely available, previous studies have been interested in the e¤ect of the full price of
marijuana. Most studies …nd that the legal risks are negatively correlated with prevalence [
(37), (49), (48), (44), (16)]. However, the teenagers seem to be less responsive to higher …nes
than young adults [(12), (21)].
2
in reality the diversity of information providers “may limit the e¤ectiveness of
a well-designed policy initiative, as prevention information for adolescents may
appear confused, is likely not to be believed, and is thus ine¢cient”. [(36)]. This
paper therefore adress the following issue: what type of information provision
will be the most e¢cient for a public health campaign aimed at marijuana?
I use a French epidemiological database about health and lifestyles of highschool teenagers, in which information variables are as following : “has had
or followed discussions about drugs within your family, your school, with your
friends and/or in the medias” (several choices allowed). With these variables,
we can neither be more precise about the content of the provided information,
nor can we be more speci…c about the way a youth understand information
and use it to revise their beliefs. Moreover, as we have cross-section data, the
information variables may be endogeneous or determined simultaneously with
consumption. Accordingly, I only identify the e¤ect of information providers
on marijuana annual prevalence among the youth, and the variables are always
instrumented.
Risks perception and responses to information providers may be very heterogeneous [(45)], and we would like to identify those individuals who are sensitive
to public information campaigns. Furthermore, the dynamic of consumption
may vary according to past consumption history and other idiosyncratic factors.
I therefore control for past use and unobservable heterogeneities in our estimates.
This has led me to use the Heckman-Singer semiparametric technique, which
allows to classify individuals in groups (or types/classes) whose risky behaviours
and reactions to information are clearly di¤erentiated. Besides, it should also
be borne in mind that alcohol use is deeply rooted in French customs and recent
research suggest that strong interdependancies between alcohol and marijuana
consumption are of major importance in the design of public policy [??, ??]. In
an attempt to control for the e¤ects of alcohol regulation and to identify more
accurately teenagers’ typical behaviours, I estimate together equations for marijuana participation in the past year, heavy drinking (drunkenness in the data) in
the past year and the age of initiation into cannabis or heavy drinking, and also
use information variables relating to alcohol4 . To anticipate the main result,
I …nd that the population can optimally be described by three latent classes,
which represents respectively 58, 23 and 19% of the sample. These types are
featured by di¤erent propensities to consume alcohol and marijuana, and by
di¤erent reactions to information. These pieces of statistical evidence point to
the importance of accounting for unobservable heterogeneities when evaluating
drug policies.
The paper is laid out as follows. In the following section, we brie‡y review the
4 We were unable to identify the main parameters of the structural model proposed by
DiNardo and Lemieux (2001) for three reasons : (i) there are too few teenagers (only 3.8%)
having smoked marijuana without having been drunk in the past year ; (ii) we work on microdata and not state-level agreggated data ; (iii) we have any regional price variables which
help identify this model. In line with argument (i), we have assumed that contemporaneous
correlations between heavy drinking and marijuana use are captured by the unobservable
heterogeneities.
3
theoretical literature on information and addiction. The third section describe
the data, and emphasize the role of unobservable heterogeneities and career (or
habit formation) e¤ects. Section 4 presents the empirical model and section
5 contains our results. I show that the observed consumption behaviours are
a probabilistic mixture of well de…ned “pure” types. In section 6, I interpret
the results using some additional estimates. I conclude on the e¢ciency of the
current French policy.
2
2.1
Theoretical analysis
Information, rational addiction and the full price of
consumption
In Becker and Murphy’s rational addiction model, the current consumption is
explained by past consumption history, the market prices, and the current and
future costs arising from consumption through anticipated habit formation and
destructions of human capital. The agent chooses to consume a quantity that
equalizes the current marginal utility of consumption and its full price. Due to
a lack of price data, a majority of empirical studies on cannabis highlight other
components of this full price such as : (i) the legal risk (ii) the short and long
term health risks and (iii) the risk of stigmatization. Following Bachman and al.
(1998), Pacula and al. (2001) provides some strong evidences of the e¤ect of risk
perceptions. Using panel data analysis, they …nd that changes in perceptions
and attitudes, which are captured by a qualitative judgement index, contributed
for 50% up to 80% to changes in the US agreggated marijuana consumption by
high school seniors between 1982 and 1998.
Orphanidès and Zervos (1995) gained a new insight into the modelization
of addiction behaviour, by assuming that information is not perfect. If the
risk of addiction is not known with certainty before initiation, informations
about the side e¤ects of recurrent intoxications become of a great importance.
Individuals may be interested in gathering informations about the frequency and
the magnitude of risks. In a recent study, Clark and Etilé (2002) …nd that most
of the past health changes of smokers or other smokers in the same household are
positively correlated with smoking. This may re‡ect the presence of adaptive
behaviors using health changes as information. Last, information modi…es also
the perception of the consumption bene…ts as sketched in Duesenberry (1949).
In harmony with these theoretical and empirical insights, the participation
decision can be formalized very simply. Let Ui be the current well-being of the
agent i, net of any current consumption cost. It is a function of a composite
aggregate y i, the quantity of cannabis consumed, x i, and the level of habit
formation h i. Uncertainty about the taste for a drug experience is summarized
by r~i .We assume that young people cannot access the credit market. The full
price ¦i is a function of the market price p, the past, current and anticipated
career of consumption hi ,and the future costs ci which depends on a risk ~¼ i. If
E(.) is the expectation operator, we observe participation conditionally to the
4
information set when :
¯
©
ª¯
@E fUi(yi ; x i; h i; r~i )g ¯¯
> E ¦i(p; ci(h i; ¼~ i ) ¯x =0
¯
i
@x
xi=0
(1)
Information may impact the probability of participation through changes in
anticipations relating to r~i , ¼~ i , and ci(:). Moreover, in a brilliant ethnographical
study, Howard Becker (1955) showed that information and learning processes
are the catalysts of involvment in drugs, as the drug market is a black market
and the e¤ects of drugs are likely to be unknown a priori and idiosyncratic.
Information may change directly either the full price of the consumption (perception of the risk ~¼ i) or the instantaneous preferences (perception of the taste
parameter ~ri), and it will have an indirect cumulative e¤ect through rational
anticipation of habit formation. In the current paper, I do not study the dynamic of beliefs revision and consumption, as the data set is cross-sectional, but
rather the heterogeneity of the reactions to the contacts with several information providers. For the same reason, I do not control for future consumption,
as required by the rational addiction theory, but only for past consumption
(anticipations are thus adaptive). It is shown in section 6 that information
generally a¤ects participation through changes in full prices and instantaneous
preferences.
2.2
Prevention policies and the heterogeneity of information e¤ects
I investigate neither the information search behaviours of the teenagers 5 nor
the changes in consumers’ attitudes and awareness toward health or addiction.I
focus rather on the preventative e¤ects of information in order to evaluate the
e¢ciency of information-based policies These prevention policies aim on one side
at diminushing the probability of consumption (primary prevention) and, on the
other side, at reducing the health risks generated by consumption (secondary
prevention).
We have some good reasons to think that prevention may have unexpected
e¤ects. Indeed, suppose that an information provider plays the role of a “forecasting center” for a teenager who is not certain of the outcome of a drug
experience. This provider send the following signal: “if you take drugs, it will
bring you a net bene…t x”. The teenager uses this forecast as the virtual outcome of her/his experience if and only if she/he trusts the provider. Hence, this
virtual outcome, weighted by the provider’s credibility, is processed as a virtual
experience and helps forming expectations about the real outcome of the experience. If she/he decides to consume, she/he will compare this latter to x. Then,
if the provider’s forecast has been misleading, she/he will revise her trust in this
5 Information may have been gathered after a search. The ex-ante demand for information
depends on the search cost and the expectation of the e¤ects of information on future decisions
and well-being [(27)]. The more there is uncertainty, the more the consumer nee ds information
to reduce it.
5
provider, whose credibility will fall. The more she/he has experimented drugs,
the lower the credibility of the provider will be if its forecasts are always wrong.
If she/he is bayesian and assuming that the signal is discrete6 , informations that
do not sound credible may have the opposite e¤ect to what was expected 7 .
Preventative information toward teenagers usually underscores only the damages generated by drug consumption (the health risks). But the …rst experiences
are usually the best. If consumers’ anticipations are adaptive and not rational,
the good outcomes of the …rst experiences will shift downwards the anticipated
future costs of consumption. The youths discount also heavily the future costs of
current consumption as their time preferences are more myopic (and time preferences may be shifted endogeneously by drug consumption, see [(7)] and [(43)]).
Further, they may value risk-taking behaviors di¤erently. If the anticipations
of well-being proposed by an information provider are never consistent with the
subjective well-being felt by the teenagers, these latter will rationally choose
to act in opposition to the messages. Juvenile behaviors are often explained
by a will of infringing some taboos, but considering more carefully the way
individuals process information may also help to rationalize these “deviances”.
In the end, unobervables heterogeneities related inter alia to the subjective
discount rate and risk aversion generate interpersonal di¤erences in the perceptions and uses of information. There are also some variations in the way agents
process information, as the gap between the perfect bayesian rationality and the
“practical rationality” (Bourdieu, 1972) implemented by agents is not negligible. In a nutshell, the variables capturing contacts with information providers
may have heterogeneous e¤ects, insofar as we do not observe the beliefs revision
process.
3
3.1
The data
The epidemiological data set INSERM 1993
The current paper exploits French epidemiological data from a 1993’s survey
conducted by the French National Institute of Medical Research (INSERM - unit
n ± 472). The national sample of 12931 teenagers is representative of scholars
enrolled in secondary schools. Those in their …rst two years of secondary school
(about 4000 8 ) did not have to reveal their current drug consumptions. Note also
that (i) going to school before 16 is an obligation in France and (ii) some of the
teenagers in the sample are aged over 21. The analysis exclude these latter, as
they may present speci…c caracteristics (scholars usually achieve their A-levels
6 Like “you ought not to take drugs because you will get into trouble” or “take drugs, it
will be marvelous”.
7 This should not hold anymore if the signal is continuous.
8 About 85% of these 4000 te enagers are aged under 13. As drug-taking behaviours and
school outcomes or drop-outs may be correlated, the remaining 15% and those who left school
after 16 might have had an higher probability of drug use. We can not correct this selection
bias.
6
by 18-year old). Extensive statistics and some notes of methodology may be
found in [(13)]. Some descriptive statistics are also reported in annex A.
The fear of being stigmatized as deviant (cannabis consumption is illegal
in France) or of presenting a bad image of one’s community9 [(29), (1)] and
retrospective questions about the age of initiation and lifetime consumption
can yield measurement errors. However, there are few abnormal answers and
few missing values for the variables describing cannabis or alcohol consumption
(between 4 and 10% for alcohol, and less than 6% for cannabis), which suggests
that the teenagers answered sincerely.
3.2
Cannabis consumption and heavy drinking: some evidences
Grouped count variables give information about the consumptions of several
drugs: cannabis, glue/solvent, pharmaceutical drugs, heroin, cocain, hallucinogens or amphetamins consumed up to 2 times, between 3 and 9 times or more
than 10 times in the life and the last 12 months. Frequencies of participation
are very low, except for cannabis: 85% of the youths had never smoked cannabis
in 1993. 9% had done it between 1 and 9 times, and 6% more than 10 times 10 .
It is not possible to distinguish in the data chronic users from occasional ones.
Moreover, youths may likely underestimate their consumption in order not to
be stigmatized as addicts. I will thus focus on the participation decision.
By using the age of initiation and the age of the individual at time of the
survey, we can bound the duration since initiation in a two-years interval11 . 83%
of the teenagers who had been initiated since more than one year took drugs
during the last 12 month.
The distribution of consumption in the past year, as a function of a proxy for
time elapsed since initiation (age-age of initiation) indicates that there are two
groups: on one hand regular users and on the other hand quitters and casual
users (cf. graphic 1). The proportion of regular users (more than ten times
during the last 12 months) seems to be positively correlated with duration since
…rst use. This stylized fact gives rise to two interpretations.
First, duration since initiation could be a proxy for habit formation e¤ects
(cf. section 2), if we assume that use has been continuous since initiation. We
ought to distinguish regular smokers from the youths having had few experiences
since their initiation. The trend e¤ects generated by habit formation will di¤er
9 Ethnical minorities are not yet institutionalized in France and, at the time of the survey,
urban areas were not yet segregated on an ethnical basis but rather on a …nancial one. However, the youth originated from immigration, which lives in poor suburban areas often regard
themselves as elements of a speci…c social group.
1 0 Straight comparison with a similar survey of 1999 shows that in 6 years the abstinence
rate fell by 20%!
1 1 ]age-age of initiation-1;age-age of initiation+1]. Lifetime consumption includes occsions
of consumptions in the past year. We know the year and month of birth, the year and month
of the survey but just the year of initiation into cannabis and heavy drinking.
7
100%
80%
More than 10
60%
Between 3 and 9
1 or 2
40%
Abstinence
20%
0%
0
1
2
3
4
5
6
Time elapsed since initiation
Figure 1: Consumption in the past year vs. mean duration since initiation
in magnitude according to this unobservable heterogeneity. Nevertheless, ceteris
paribus, the earlier the initiation occurs, the higher the participation probability seems to be. However, once again, partially observable heterogeneities or a
third factor (such as the marital status of the parents, family dysfunctions or
poverty) could explain both the precocity of involvment in drugs and the intensity of lifetime consumption. As the sample does not include youths that had
ended up their studies in secondary school, it is not possible to test correctly one
explanation against the other. In some preliminary estimates, we found that,
after controlling for age, the age of initiation is not correlated with the participation probability, by contrast with the duration since initiation. Thereby,
I use this latter as a proxy for habit formation and control for the unobservable heterogeneities generated by the diversity of individual careers. Besides,
epidemiological researchs show that there is a progression in drug involvment,
from cigarette and alcohol to marijuana and sometimes other drugs [??, ??].
Whether this progression is explained by sociological factors (cf. the learning
theory proposed by Howard Becker), pharmacological ones, or a third factor is
beyond the scope of this paper. What really matters is that adolescents are very
unlikely to experiment with marijuana without prior experiences with alcohol.
Consequently, unobservable heterogeneities that explain cannabis consumption
should also carry weight in alcohol participation.
It is well-known that wine drinking is deeply rooted in French culture. Alcohol (and often binge) drinking is not considered as an unhealthy activity as
it is in North America, so that it would be a nonsense from a comprehensive
point of view, to compare alcohol and cannabis consumption1 2 . Accordingly, I
1 2 France
is the country where the average alcohol consumption per inhabitant has decreased
8
have rather considered the life and yearly numbers of heavy drinking occasions
(in the data, drunkenness which is also a grouped count data variable), as well
as the age of initiation to drunkenness. drunkennesses are positively correlated
with usual drinking behaviour.
97.8% of the cannabis-initiated youths had still drunk alcohol and 89.2% had
still been drunk. Furthermore, past-year cannabis consumption and drunkenness occasions are highly correlated. Whilst 96% of those who were not drunk
did not smoke cannabis, 70% of those who were drunk more than ten times did
it. The assumption of independance between both variables is rejected by a
 2 test1 3 . Otherwise, only 3.8% of the youths took drugs and were not drunk
in the past year14 . These …gures imply that we can not analyze cannabis consumption without accounting for heavy drinking. This reason has led me to
use informations about heavy drinking and the age of …rst alcoholic/mariholic
drunkenness in order to identify the unobservable heterogeneities (cf. section 4).
To be consistent, I use information variables related to alcohol as explanative
variables.
3.3
Information variables
The information variables are labelled as following : “has had or followed discussions about drugs/alcohol within your family/at school/ with your friends/in
the medias” (several choices allowed, 8 variables). Hence, we know the identity
of some information providers but not the content of the informational signal.
We expect a priori that parents talk their children out of taking drugs. But
some adults may be very liberal if, by instance, they are themselves current or
former drug-users. Furthermore, lifetime cannabis use is negatively correlated
with variables that capture the quality of the relationship between the children
and their parents15 . The absence of discussion about drugs within the family
may therefore be correlated with cannabis use through a third factor, namely
family dysfunctions. Many parents begin to talk about drugs with their children
after having found out that she/he takes drugs. Likewise, until 1990/1996 the
ma jority of schools used to set up information-based preventative campaigns
after the police had to intervene against a tra¢c. Note also that young drug
users may be more involved in seeking informations about drugs in the medias.
However, the French teen press has always been quite hostile to drugs.
the most, going from 17.7 litres of pure alcohol in 1961 to 10.7 litres in 1999. However, in
1996, the average consumption in France was 11.2 litres, to be compard to 7.9 in the UK, 6.6
in the U.S.A. and 6.2 in Canada. Note also that a simple glass of alc ohol does not provide
the same rapture as heavy drinking and cannabis smoking do.
ni: n:j 2
)
1 3 The test statistic is: d2 = P P (nij¡
n
ni:n:j
1 4 Note
n
that these youth do not have particular ethnical caracteristics such as being originated from North-Africa or Middle East.
1 5 Thre e questions labelled as follows: “Family life is to your opinion, rather tense (y/n),
pleasant (y/n), to be sought after (y/n)”. And one question about the sujective feeling of
having thoughtful parents: “My parents are interested in what I do/ would be interested if
they had time for/ take too much interest in what I do/have little interest in what I do”.
9
Friends are costless information providers. 78% of cannabis users still talked
about drugs with their friends, whereas it is the case for only 48% of nonusers. But information gathered by observation or cheap-talk, or the will to get
a status among one’s peers can generate herd behaviour [(9), (19), (4)]. The
identi…cation of the e¤ect of peer information requires some instruments in order
to control for the re‡ection e¤ect [(34)]. Where did friends get their information?
If they are themselves consumers, they will tend to justify their consumption to
their friends. Last, some common caracteristics (the socio-economic status of the
parents, the ethnical origin, the living area and a number of other environmental
factors) may explain both the formation of a peer group and the decision of its
member to consume or not.
As information variables are likely to be endogeneous, I instrument them.
3.4
Income, prices and other variables
Occasionnal cannabis users often consume for free as the drug is bought by
those of the peers who are regular smokers[(30), (1)]. The market price may
thus have little e¤ect on the probability of participation. Otherwise, the price
of a “standard” quantity of cannabis is constant since 20 years, whereas weight
and quality of this unit can vary according to the place and the moment of the
transaction (about 15 euros for 2 to 4 grammas16 ). There were no centralized
and regional statistics of drug-related crimes until the early nineties, even though
the enforcement of drug laws varies across the areas of jurisdiction. I just build
from the data an indicator for the avalaibility of cannabis. It is the rate of
young scholars initiated into cannabis and attending school in the same o¢cial
educational area and living in the same kind of area (town or country)1 7 . Due to
network e¤ects and stigmatization, it is less costly to buy and consume cannabis
where there are more users. This variable may be endogeneous as there are not
many observations in some of the geographical cells. The rate of users varies
from 9% up to 18% percent for a population of 188 up to 1395 youngs. There is at
least 24 initiated youths per cell. I build a similar indicator for drunkennesses.
While estimating the empirical model, I have noticed that introducing these
variables meliorate the identi…cation of the heterogeneities. Regional prices of
alcohol are not available. Moreover the administrative (and economic) areas of
residence are not in our data. However, I use in section 6 the price of alcohol
to estimate duration models for the age of cannabis initiation. It is the yearly
price index of alcoholic beverages de‡ated with the consumer price index (base
100 in 1980, INSEE price indices). This index is not quite appropriate as a
representative youth consume more beer and less wine than a representative
French household. Their average shopping basket is also di¤erent.
Few French youths have a job. Hence, their income consists mainly of the
pocket money given by their parents, which is known. Otherwise, I used dum1 6 The
main quality sold in France is the haschisch from Morocco, which is often blended
with para¢na, a kin of haircare powder called henné, and residuals from tyres.
1 7 More precisely, it is the number of the initiated youths who were surveyed before {age of
initiation+1} as we do not know precisely the date of initiation.
10
mies for pubescence (to control for biological changes) and gender.
4
4.1
Empirical modelling
Basic speci…cation
The main dependent variable is CANyr : “Did you consume cannabis during
¤
the last 12 months”. Let YiC
be a latent variable for the past-year cannabis
participation decision of agent i. The observed dependent binary variable is
Y iC , which is equal to 1 if the teenager consumed, and 0 otherwise. Let XiC be
a vector of explanative variables, the probit model reads :
½
¤
0 if YiC
= XiC ¯ C + ~º iC < 0
Y iC =
(2)
¤
1 if YiC
= XiC ¯ C + ~º iC > 0
and its likelihood is :
LC (Y iC jXiC ) = [1 ¡ ©(XiC ¯ C )]1¡Y iC [©(XiC ¯ C )]YiC
(3)
where © is the cumulative of the standard normal law.
4.2
Heterogeneity
We know that unobservable heterogeneities can bias the correlation inferred between past use history (captured by trend variables) or the way information is
processed and consumption. A common antidote to the omission of possibly
unobservable variables is to model the unoberved heterogeneity as individualspeci…c random e¤ects. But inference can be sensitive to the assumed distribution of the unobserved heterogeneities. An alternative way to deal with this
problem, is the semi-parametric estimator of Heckman et Singer (1984).
Assume that the population consists of a possibly unknown number S of
subpopulations (or components/classes/types). Some decision parameters may
vary from one class to another. Hence, the coe¢cients ¯ C of the covariates
vary across the sample according to some distribution, which is supposed to be
discrete. As the component membership is not known, the likelihood of observation i is a mixture of the probability densities of this observation, when sampled
from components s = 1; :::; S. All observations have the same unconditionnal
probability ®s to belong to the latent class s = 1; :::S. . We have therefore to
condition the individual likelihood by ®s . The likelihood of the sample becomes
:
" S
#
N
Y
X
L=
® s LC j s(Y iC jXiC )
(4)
i=1
s=1
where :
LC j s (Y iC jXiC ) = [1 ¡ ©(XiC ¯ C s )]
11
1¡YiC
[©(XiC ¯ C s )]YiC
(5)
Using this …nite mixture model, we can interpret the point masses of the
distribution of the coe¢cients as latent classes. Each class is representative of
a type of consumption behaviour, and a type of reaction to information.
Furthermore, unobservable heterogeneities should not change a lot along
time. We thus have to incorporate other informations about past and current
behaviours of the youths in order to identify more accurately the latent classes.
Otherwise, they would capture the unobservable heterogeneities that are solely
correlated with cannabis use in the past year. To circumvent this problem,
I model cannabis participation together with heavy drinking in the past year
(ALCyr)18 and the age of …rst cannabis use or …rst drunkenness (AGEmin).
Hence, the latent classes that I will …nd out will not just mimic the observed
classi…cation between users and non-users.
As the empirical hazard function of initiation is not monotonous, I assume
that AGEmin follows a loglogistic law (cf. the graphic in annex A). However,
some individuals will never be alcohol or haschisch drunkards, and we should
thus use a split-population duration model [(17)]. We can not identify properly
such a model, insofar as we do not observe youths on the whole period at risk for
initiation (from the age of 10 up to about 30). Note T i min the variable AGEmin,
and ± i min an indicator which values 1 if the teenager has yet been initiated (and
0 by default). XiD is a vector of explanative variables. The likelihood reads for
this model :
LD (T i minjXiD ) = [S(T i min jXiD )]1¡±i min [f (Ti minjXiD )]±i min
(6)
where S et f are respectively the loglogistic survival and density functions.
Last, ALCyr is modelled through a probit speci…cation. The likelihood is :
LA (Y iAjXiA) = [1 ¡ ©(XiA ¯ iA )]
1¡YiA
[©(XiA¯ iA )]YiA
(7)
Conditionning each likelihood by the latent class, we get the following full
likelihood :
8
93
2
N
S
<
=
Y
X
Y
4
L=
®s LD j s(T i minjXiD )
Lj j s (Y ij jXij ) 5
(8)
:
;
i=1
s=1
j= A;C
It is estimated by an E-M algorithm following Dempster and al. (1977).
To …nd out the optimal number of classes, I just compare the …nal likelihood
penalized by an ad hoc function, for the estimates of the model with 2, 3 or 4
classes. All parameters vary between latent classes, so that adding one class may
1 8 We were unable to identify the semiparametric bivariate probit model for cannabis use
and heavy drinking, as one of the alternative (smoking and no heavy drinking) is rarely chosen. Indeed, in the maximisation step of the EM algorithm, we have to estimate equations for
each class, by weighting individual contributions to the overall likelihood with the individual
probability of belonging to the class. The choice smoking/no drinking may thus be undersampled...Hence, we implicitely assume that any contemporaneous correlation between both
decisions is picked up by the mixture distribution of the constant in each equation.
12
be costly and useless 19 . To ensure the reproducibility of the results, I choose
initial values by a two-step procedure 20 . First, I separate the sample in S groups
using a cluster analysis, on the basis of several psychological variables (selfesteem, optimism, suicidal thougts, impatience, inconsistency). These variables
might capture variations in the pure discount rate, that is the capability to
make plans for the future and to follow them (Masson, 1995). In a second
step, I estimate a multinomial logit for the probabilities of belonging to one or
another group resulting from the …rst step. This logit was conditionned by the
explanative variables of the model. Then, starting values for the E-M algorithm
were set at the predicted probabilities of belonging.
4.3
Identi…cation
Following Etilé (2002), I use the socioeconomic status (SES) of both parents to
instrument the information variables. A simple probit analysis shows that father’s SES is not correlated with past year use, whereas mother’s SES generally
is. The instrument set also includes dummies for the number of brothers and
sisters, the marital status of the parents, the geographical localization (educational area and type of area: country/suburbs/center), the ethnicity, and some
diseases (asthma, allergy, physical disability). Using bivariate probit regressions,
I instrument simultaneously informations about alcohol and cannabis obtained
from a same provider. I then introduce the predicted values of the probabilities of having had information in participation equations for drunkenness and
cannabis 21 .
According to the main predictions of the Economic of the Family, family
information should be well instrumented2 2 . This is curiously not the case (cf.
table A2 in annex A). Instrumentation does not solve the problem of contextual and correlated e¤ects2 3 , as parental SES may explain both the composi1 9 There is no test of the optimal number of classes as 0 is at the boundary of parameter
space. Following Baker & Melino (2000), we use the following criterion : ln(L) ¡p ¤ ln(ln(N)
where L is the …nal likelihood, p is the total number of parameters and N is the sample size .
2 0 The iterative Expected-Maximisation algorithm is monotoneously convergent, but it is
possible to reach di¤erent maxima depending on the starting values. I tried several starting
values and …nd that it converges toward the same maximum, at a slower speed than it did
with predetermined starting values.
2 1 Assuming that there is no unobservable heterogeneity, usual methods for dealing with
the endogeneity of information variables use the Heckman’s approach (as advocated by Vella,
1993): the model should be estimated from equations augmented with Mills ratios estimated
from bivariate probit regressions (see for an example , Variyam and al., 1999). I did not
investigate the compatibility of this approach and the Heckman and Singer’s te chnique.
Moreover, in any two-step procedure, the variance of the coe¢cients of the instrumented
variables are biased (Murphy and Topel, 1985). We have not yet corrected for these bias, and
therefore the variances are probably downward biased.
I will try to solve these problems in a future revision of this paper.
2 2 As there is a the trade-o¤ between quality and quantity of children, the preventative e¤ort
should decrease with the number of children and increase with parental socioeconomic status.
2 3 Manski (1995, p.127) gives the following de…nitions of the three hypotheses which can
explain the similarity of behaviours in a group :
1. “endogenous e¤ect, wherein the propensity of an individual to behave in some way varies
with the prevalence of that behavior in the group.”
13
tion of the peer group, the quality of scho ol drug prevention programs and the
propensity to take drugs. I should therefore control for parental SES in our
main equations. However, the Heckman-Singer technique allows to use these
variables as instruments only. The correlations between the instrumented information variables and past-year participation may therefore be interpreted as
the measures of the primary preventative e¤ect of contacts with information
providers. Last, note that identi…cation relies also on the non-linearity of the
probit of instrumentation.
5
Results
Results are reported in the tables 1 to 3 of annex B. In the probit equations, a
…rst group of variables controls for trend e¤ects (or habit formation) generated
by past uses of alcohol and cannabis. These variables are DURCAN et DURALC, which are built as the age at the time of the survey minus the age at the
time of initiation into cannabis or drunkenness. When the teenager has never
tried cannabis or heavy drinking, these variables are set at 0. When they value
1, I recode them at 0, as initiation may have occured during the last 12 months
(cf. section 3.2). A second group of variables comes to control for gender, age,
puberty and income. Last, a third group helps to identify the e¤ect of the full
price: availability of cannabis on the one hand; informations about cannabis and
alcohol on the other hand. Explanative and dependent variables are described
in tables A1 of annex A.
Explanative variables for the duration equation are dummies for generation
e¤ects, gender, and a dummy which indicates if the natural parents lives still
together2 4 .
5.1
A behavioral typology based on latent classes
Individual heterogeneities are captured by 3 types, which represent respectively
58, 23 and 19% of the population. Given these estimates, we can use Bayes rule
2. “contextual e¤ect, wherein the propensity of an individual to behave in some way varies
with the distribution of background characteristics in the group.”
3. “correlated e¤ect, wherein individuals in the same group tend to behave similarly because
they face similar institutional environments or have similar individual characteristics.”
2 4 For a better inference, AGEmin is recoded as following : AGEmin = age at the time of
the survey- 9 if the teenager has not yet been initiated into c annabis or drunkeness; AGEmin
= (age at the time of the survey + age of initiation)/2 - 9 if the teenager has yet been initiated
and age of initiation is equal to the whole value of the age at the time of the survey; AGEmin
= (age of initiation + 0.5) - 9 if the teenager has yet bee n initiated and this age of initiation
is equal to the whole value of the age at the time of the survey.
Hence, I eliminate from the estimation sample individuals aged under 9. I could have adress
the imprecision of our duration data by using a duration model, in which the second term of
the likelihood, f (Ti min jXiD ), would be replaced by (S(Ti¤ jXiD ) ¡ S(min(Ti¤ + 1; T i )jXiD )),
where Ti¤ is the age of initiation and T i the age at the time of the survey. Preliminary
estimates have shown that this model does not yield di¤erent results and takes much more
time to be estimated than our simpler speci…cation.
14
to calculate for each observation i the probabilities of belonging to classes 1, 2,
or 3:
Pr (i
2 sjY
=
; Y iA ; T i min; X iC ; X iA; X iD )
Pr (Y iC ; Y iA ; T i min ji 2 s; X iC ; X iA ; X iD ) Pr(i 2 s)
(9)
PS
s=1 Pr (Y iC ; Y iA; T i min ji 2 s; X iC ; X iA; X iD ) Pr (i 2 s)
iC
The probability in the numerator is the likelihood of observation i, conditionnal to being of type s. Furthermore, we know the mean caracteristics of an
agent who would belong to one class s. The sample mean ex-post probability
of belonging to the class s for agent i with caracteristic xi = 1 is given by :
X
1
Pr (i 2 sjxi = 1) =
Pr (i 2 sjY iC ; Y iA ; T i min; X iC ; X iA ; X iD )
#fi=x i= 1g
i=xi =1
(10)
Conversely, the sample mean type-conditional probability that x i = 1 is :
1 X Pr (i 2 sjY iC ; Y iA ; T i min; X iC ; X iA; X iD )
Pr(xi = 1ji 2 s) =
xi
(11)
N i
®s
Using other variables of risky behaviours, health status and socioeconomic
caracteristics help to caracterize the behavioral features of each latent class.
Some of the descriptive statistics I have computed for this analysis are presented
in the tables of annex A.
Youths of the …rst type25 are close to the pure type “abstinent”. Actually,
41% of them have still experienced tobacco whereas they are 64% and 80% in
the two other classes. Less than 10% smokes at least one cigarette per day.
Heavy drinking is rare. While 69% had still drinked alcohol, only 10% had still
been drunk, and 2.3% had still used marijuana.
Individuals from classes 2 and 3 di¤er essentially with respect to their drinking behavior. The mean age does not vary across classes (between 16 and 16.5)
but those from the third type drink much more, with a rate of yearly drunkenness higher than 80%, whilst it is only 4.3% in the …rst class and 40.7% in the
second. However, both second and third components have an high proportion
of cannabis users, around 30%. The rate of risky consumptions (more than 10
marijuana uses or drunkennesses during last year) is also the same. But conversely, youths with risky consumption behaviours have a 50% probability of
belonging to class 2, whilst it is only 30% for class 3. Last, the circumstances
surrounding heavy drinking occasions are not the same across these two groups.
Youths who drink more during family celebrations or with friends tend to belong
to the third group. At the opposite, the teenagers who drink more when they
are in a bad mood have an higher probability to belong to the second group.
Thus, individuals who have non-social or dangerous uses of alcohol and marijuana are more likely to be of the second type, which could be caracterized as a
“risky user” type, whereas the third type represents broadly “happy users” 26 .
2 5 In reality, a youth does not belong entirely to one class: this is an abuse of language as
we consider indeed statistical entities not individuals.
2 6 This may re‡ect, of course, an european point of view wherein the lack of social and
15
5.2
Types, time preferences and risk aversion
Do types di¤er with respect to their time preferences and risk aversion? If
it were true, given that these factors are good predictors of risky behaviours,
then probabilities of belonging should also predict these behaviours. Actually,
youths from the second type tend to have more often sexual intercourses without
preservatives and with di¤erent parters. Likewise, the conditional probabilities
to have a sport or a road accident increase with the type. Last, a youth from
the second and the third type get more pocket money (with a mean of about 31
euros/month to be compared to 22 euros for the …rst type), and we know that
absolute risk aversion decrease with wealth. Hence, the second and the third
type should be less risk-adverse.
Preference for the future is said to be positively correlated with human
capital and wealth, and negatively related to the psychological health status and
lifecycle negative shocks [(35), (7)]. There are little di¤erences in the parental
socio-economic status between types. Teenagers whose mother is blue or white
collars have an higher probability of belonging to classe 2 or 3, whereas teenagers
whose mother is at home or father is a worker tend to belong to class 1. School
satisfaction and outcomes are about the same across all classes.
I eventually use several questions bringing on the psychological pro…le and
the lifecycle shocks of the teenager. These variables indicate if she/he is impatient, has lost heart, depressed or able to achieve the tasks she planned to
do (which is a good proxy for dynamically inconsistent behaviours), and the
marital status of her parents. They summarize the capability to make projects
for the future (thus re‡ecting an high preference for the future, (35)) or shocks
on the discount rate. Types 2 and 3 are more impatient and depressed, and
less consistent. But variations of time preferences might be caused by consumption. Being depressed or impatient does not seem to be correlated with the
probability of belonging to classes at odds for consumption. Conversely, having
separated or deceased parents increases this probability. In the end, the link
between types and the time preferences is fuzzy. As emphasized by sociologists
[??], di¤erences in risk aversion play a more important role: in the referential of
the teenage subcultures, risky behaviours often generate bene…ts that one can
not ignore. Failure to take this into account may lead to overstate di¤erences
in the time preferences across agents.
6
6.1
Interpreting the results
Assessing the impact of information providers
At …rst glance, an information provider will have a preventative e¤ect if it
decreases the propensity to consume cannabis. But, as there are unobservable
heterogeneities (cf. section 2.2), the results reveal a sharp distinction in the
time boudaries around drug experiences rather than the level of consumption is seen as the
main symptom of a true addiction. From a sociological point of view, the medical norms for
addiction are not objective, but a cultural product of each society.
16
e¤ect of information providers, according to the group. Then, it might be
thought that individuals use the same pieces of objective informations in many
various ways. According to theory (equation 1 and DiNardo and Lemieux, 2001),
information a¤ects participation probabilities by two channels, which consist of
:
² modifying instantaneous utility (i.e. taste perception). Remember that
trend variables control for any indirect lagged e¤ect through habit formation.
² changing the full prices (i.e. risk perception). Increasing the full price
of one good should decrease participation and induce a change in other
goods’ participation through changes in the relative prices.
Assume that market prices and information have the same e¤ect on the full
price (e.g. a “bad” information about cannabis increases its full price). Then,
knowing the cross and direct price e¤ects, we can show that information e¤ect
through full price is often counterbalanced by variations of instantaneous preferences. Using additional assumptions about the e¤ect of information providers
on the full prices of alcohol and cannabis, it is then worth wondering wether
our information dummies are correlated with participation solely through their
e¤ect on full prices. Teenagers’ preferences are quite unstable, because adolescence is a period of role transition from childhood to adulthood 27 . Therefore,
one may suppose that the more an information provider a¤ects participation
through changes of full prices, the more its e¤ect will last as it means that
perception of the true health risks have been modi…ed. By contrast, the e¤ect
of an information on instantaneous preferences shall vanish quickly. One can
also argue that it is important to keep youths’ preferences “under pressure” as
they are more likely to be in‡uenced by peers during periods of role transition.
But from a …nancial point of view, such a strategy is more expensive than a
“once-for-all” one.
In the next subsection, I attempt to estimate indirectly the signs of the
price e¤ects. I use these results to assess the e¢ciency of public information
providers on the one hand, and private ones on the other hand.
6.2
The e¤ect of full prices
In the absence of cross-sectional prices, I estimate for each class (by using probability weights) a duration model for the age of initiation into cannabis (table
4, annex B). It is quite clear that such a model performs po orly on our data, as
we face sample selection problems 28 . I use the time series of year relative price
indices of alcohol and tobacco to infer the sign of the correlation between prices
2 7 Phenomenological consumer researchs show that compulsive consumption acts are intensi…ed during periods of role transition (see for an e xample Hirschman, 1992).
2 8 As noticed previously, we do not observe teenagers on the whole period where they are at
risk for initiating consumption.
17
Alc. Bev.
19
97
19
94
19
91
19
88
19
85
19
82
19
79
19
76
Tobacco
19
73
Relative price index
19
70
1,6
1,4
1,2
1
0,8
0,6
0,4
0,2
0
Year
Figure 2: Price index of alcoholic beverages and tobacco de‡ated by the consumer price index (source: INSEE)
and the age of initiation (cf. graphic 2). I also control for generation e¤ects,
puberty and gender.
The price e¤ects are strongly positive for the …rst type : age of initiation
increases with prices. Hence, the cross-price e¤ect of alcohol on cannabis participation is negative for class 12 9 . As the e¤ect of income on participation is
null, this implies that heavy drinking and cannabis smoking are hicksian complements. Cross-price e¤ects are negative but somewhat unsigni…cant for types
2 and 3. Goods are likely to be hicksian substitutes for the latter, but complement for the former. Table 5 in annex C show the signs of the marshallian
direct and cross-price e¤ects obtained from our estimates. I use it to predict the
e¤ect of full prices on cannabis and alcohol participation, although the sign of
some price e¤ects is not well determined (as emphasized by the question marks
in the tables).
6.3
School and the medias
Suppose that the medias and school increase the full prices of both alcohol
and cannabis. It is easily seen that the observed and predicted signs of the
cross-price e¤ects of alcohol price on cannabis participation are opposite for
2 9 There
is consumption when the ratio of the relative marginal utilities of cannabis and
heavy drinking is higher than the ratio of relative full prices. If zero consumption has no
speci…c de terminants, this condition is tantamount to x¤ (¼A ; ¼C ; X C (t)) > 0 where x¤ ()
is the latent marshallian demand function (i.e. the solution of the …rst-order optimisation
condition in the absence of Kuhn and Tucker’s multiplicators).
18
the …rst group (Tables 6 and 7). This means that media information about
alcohol a¤ects the instantaneous preferences of these teenagers. However, school
information could also decrease the full price of alcohol and increases cannabis
price. Using arguments developped in section 2, we can eventually state that
either school information about alcohol is not credible or it impacts both youths’
preferences and (anticipation of) full prices. In either case, it may be partially
ine¢cient. Nevertheless, the cumulate e¤ect of school information about alcohol
and cannabis is negative on both alcohol and cannabis participations. Hence,
school informations about drugs and alcohol shall not be delivered separately,
even if evidence are mixed about their e¤ect on full prices and, as a consequence,
perceptions of the true health risks. We can achieve the same conclusion for the
medias.
The second group is more responsive to information. School information
incites globally these teenagers to substitute alcohol and cannabis in favour of
this latter. Moreover, it is not signi…cantly correlated with heavy drinking. If we
interpret our results strictly, we should conclude that this substitution e¤ect is
not a price e¤ect and consequently, that for the second type, school information
is totally ine¢cient. However, if we are less bothered about the signi…cativity of
our results (and ignoring the question marks in the tables), we see that school
information may increase the full prices of both goods. The total e¤ect of school
information about drugs and alcohol is positive. Hence, school information may
have a preventative and lasting e¤ect (through changes of risks perceptions), if
it concerns only cannabis. This result contradicts our …nding for the …rst group.
The medias necessarily a¤ect preferences for cannabis. They have an overall
(perhaps not durable) negative impact on heavy drinking and cannabis use.
Compared to the second group, information has less e¤ect on the behaviours
of the youths from the third group. It turns out that school information could
change the participation probability through full prices if it lowered them (cf.
tables 10 and 11 ). I think that it is unlikely to be case, which means that
this information provider modi…es especially the preferences. It has however a
preventative e¤ect, like the medias, which also induces variations of preferences.
6.4
Friends and the family
Information from friends should decrease the full prices. For the …rst group,
its impact on cannabis participation is slim, probably because youths from this
group have little accointances with drug users. It has also no e¤ect on youths
from the third class. For these two groups, straight comparisons between price
and information e¤ects show that information from friends alters mainly the
preferences. This is not true for the teenagers of the second group. Peer information increases their odds for both heavy drinking and cannabis. This e¤ect
is compatible with a decrease of full prices, although friends may also in‡uence
preferences. Such …ndings are not surprising, as there are more users in the
second class and as the individuals from a same class are likely to match in the
19
same peer groups. What we observe could just be the correlated or context
e¤ects described by Manski (1995).
For the …rst group, family information is not correlated with consumption,
even though this kind of youths has as many discussions within family as other
types do. Perhaps information from the family is not credible. But, we shall
interpret this result carefully, as there are few drug users in the …rst class.
For the second group, there is no doubt that family information has a counterproductive e¤ect (table 9). As emphasized in section 2.2, just arguing that
cannabis is a “bad”, may result in a lost of credibility. However, using table 8,
we see that our results are not compatible with any kind of price e¤ect. A more
traditional interpretation is that the teenagers are often reluctant to do what
their parents expect them to do. This “psychological” will to break taboos could
push them to act in contrast with parental opinions and desires, by changing
their preferences. I have investigated the relationship between class belonging
and qualitative variables capturing family dysfunctions. It is not surprising that
the youths from the second and the third groups are more likely to have relational problems with their parents (little interest of the father as felt by the
youth, family life that looks rather stressing or unpleasant). However, family
information has little e¤ect on the behaviours of the third group.
7
Concluding comments
Using cross-section data about the health and lifestyle of French teenagers, I
investigated the correlations between information and cannabis consumption.
I proposed a latent class probit model that accounts for the heterogeneity in
the coe¢cients of interest. I assumed that this heterogeneity arises because the
true model is a mixture of di¤erent probit models. Results show that there were
in 1993 three latent classes, which represented respectively 58%, 23% and 19%
of the population. The …rst class gathered teenagers that were less prone to
smoke tobacoo, cannabis or drunkenness. They were less risk adverse, because
less involved in risky behaviours. Both other classes contained more consumers
(about 30% each). But the third class was more alcohol-oriented, in the sense
that the propension to heavy drinking is much higher. Moreover, they drunk
during social events (with friends or within family), whereas the youths who
belonged to the second class were more likely to drink alone and when they feel
depressed.
Two general conclusions can be drawn from this study. First, the e¤ect
of public policies is heterogeneous which impede the design of e¢cient general
public policy. By instance, recurrent campaigns at school against drugs and
alcohol would have a preventative e¤ect for the …rst and the third groups (the
participation probability will fall respectively from -0.3% and -52%), but an
incitative one on the second group(+23.5%)3 0 . The “real” e¤ect would be a
3 0 For each group, we can simulate the e¤ect of …nite changes in school informations about
drugs and alcohol, by computing the di¤erences in probability as the value of the two variables
20
decrease of 4.8% in prevalence with unexpected consequences for the youths
who are more likely to belong to the second group. Second, information does
not a¤ect only the full prices of alcohol and drugs (i.e. perceptions of future
health risks) but also the instantaneous preferences of users. Thus, information
at school must be provided every year which may reveal costly.
Information delivered within family may have surprising and unexpected
e¤ects, especially when there are family dysfunctions. Last, peer groups have
an in‡uence on the risks perceptions of individuals from the second groups,
who are more likely to have socially unbounded consumption behaviors. This
suggests that, conversely, targetting peer groups could be an e¢cient strategy
for disseminating information
In addition to …nding evidence of the great heterogeneity of information
e¤ects, we have found that alcohol and cannabis are economic complements for
the major part of the population and merely substitutes for the other. Therefore,
future studies on drugs regulation will have to account for the heterogeneity of
preferences.
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24
A
Descriptive statitistics
0,18
0,16
0,14
0,12
0,1
0,08
0,06
0,04
0,02
0
9
10
11
12
13
14
15
16
17
18
19
20
21
Empirical hazard of initiation into cannabis or drunkeness
Tables A2: % of good prediction from the bivariate probits for
instrumentation
Information variable
%
Family / drugs
School /drugs
Friends / drugs.
Medias / drugs
Family / alcohol
School / alcohol
Friends / alcohol
Medias / alcohol
58.4%
70.3%
61.6%
66.8%
58.6%
76.3%
75.9%
72.4%
25
Table A1: description of the variables
Variable name
Variable label
CANAN
ALCAN
Sex
Pub.
Age
Inc1000
=1 if initiated into cannabis
=1 if initiated into drunkenness (heavy drinking)
=1 if male
=1 if pubescent
Age
Monthly pocket money (in 1993 FF)/1000
Rate of initiation into cannabis in the same geographical cell
(educationnal area * town/country)
Rate of drunkennesses in the same geographical cell
=0 if not initiated into cannabis
else = (age at time of the survey- min(age of initiation+1;age at time of the survey)
=0 if not initiated into heavy drinking
else = (age at time of the survey- min(age of initiation+1;age at time of the survey)
Has had or followed a discussion about drugs within the family
Has had or followed a discussion about drugs at school
Has had or followed a discussion about drugs with friends
Has had or followed a discussion about drugs in the medias
Has had or followed a discussion about alcohol within the family
Has had or followed a discussion about alcohol at school
Has had or followed a discussion about alcohol with friends
Has had or followed a discussion about alcohol in the medias
RateCan
RateDrunk
DURCAN
DURDRUNK
INFCANfa
INFCANsc
INFCANpe
INFCANmed
INFALCfa
INFALCsc
INFALCpe
INFALCmed
Mean (N=6534)
12.8%
27.9%
47.6%
90.2%
16.17 (se: 1.96)
0.17 (se: 0.46)
0.12 (se: 0.08)
0.23 (se: 0.06)
0.20 (se: 0.72)
0.56 (se: 1.16)
46.8%
29.9%
52.6%
34.4%
49.2%
23.7%
54.9%%
27.6%
In the cell (row=X, column=Y), one reads Pr(XjY).
Sociodemographic
caracteristics
Total
sample
(N=6534)
Type 1
Type 2
Typ e 3
Fath.
farmer
Fath.
Fath.
crafts./ret.worker
Moth.
farmer
Moth.
up.
occ.
Moth.
worker
Moth.
at
home
Inc. 0
Inc. 0100
Inc.
100500
Inc.
500 +
Type 1
Type 2
Type 3
Male
Age
Father farmer
Fath. craftsman or
retailer
Fath. upper occupations
Fath. int. occ.
Fath. clerks
Fath. worker
Fath. retired
Fath. other occ.
Mother farmer
Moth. craft./ret.
Moth. upper occ.
Moth. int. occ.
Moth. clerks
Moth. worker
Moth. at home
Moth. other occ.
Monthly
income
(1993 french francs)
58,2%
22,7%
19,1%
47,6%
16,178
1,9%
9,8%
1
0
0
45.6%
16.02
1.7%
9.2%
0
1
0
51.5%
16.26
2.0%
10.0%
0
0
1
49.3%
16.56
2.4%
11.4%
52,3%
24,1%
23,7%
54,4%
23,3%
22,3%
63,1%
22,2%
14,7%
53,3%
25,2%
21,4%
52%
23,8%
24,2%
62,2%
20,9%
16,9%
63,7%
20,6%
15,8%
63,6%
21,0%
15,4%
50,0%
24,8%
25,1%
42,2%
33,2%
24,5%
14,5%
13.9%
15.5%
15.3%
17,0%
13,5%
26,7%
3,2%
14,6%
1,2%
3,7%
6,1%
14,5%
29,8%
4,4%
29,0%
11,8%
170
16.6%
13.4%
28.0%
3.3%
15.0%
1.3%
3.4%
5.6%
14.0%
29.2%
3.9%
31.0%
12.0%
144
17.8%
13.2%
25.5%
3.0%
14.2%
1.2%
4.0%
6.7%
16.2%
30.2%
4.6%
26.6%
11.0%
209
17.4%
13.9%
24.0%
2.9%
13.8%
0.9%
4.4%
6.8%
14.1%
31.1%
5.6%
25.6%
12.1%
202
61,0%
21,7%
17,2%
Family structure
Total
sample
(N=6534)
Type 1
Type 2
Typ e 3
Par.
mar.
Par.
déc.
Par.
sep.
Monop.
fam.
Recomp. Not
family
living
with
parents
Type 1
Type 2
Type 3
Parents are married
One of both natural
parents deceased
Parents are separated
58,2%
22,7%
19,1%
79,4%
4,0%
1
0
0
81.5%
3.7%
0
1
0
79.0%
3.8%
0
0
1
75.9%
4.4%
59,4%
22,5%
18,2%
55,8%
22,3%
21,9%
52,8%
24,1%
23,2%
54,8%
23,5%
21,7%
51,7%
23,6%
24,7%
16,6%
14.8%
17.2%
19.7%
Single-parent family
13,4%
12.3%
13.5%
14.8%
Family has been recomposed
Vit hors-foyer
5,8%
5.1%
6.0%
7.4%
2,6%
2.1%
2.6%
2.7%
Type 1
Type 2
Type 3
Is patient
Ends what she has begun
to do (time consistency)
Has often a positive state
of mind
Has rarely had thoughts
of suicide
Is rarely desesperate
when thinking about the
future
Has sexual intercourses
without
sheath
and
changes of partner are
frequent
Number of road accidents
Nb. of sport accidents
Family life is unpleasant
52,1%
25,2%
22,7%
Total
sample
(N=6534)
Type 1
Type 2
Type 3
Patient
Consistent State of
mind
DesesperateRisky
for the sexual
fut.
intercourses
Has had
a road
acc.
Has had
a sport
acc.
58,2%
22,7%
19,1%
60,9%
68,1%
1
0
0
64.1%
69.6%
0
1
0
58.4%
65.3%
0
0
1
54.3%
66.9%
61,1%
21,8%
17,1%
59,4%
21,8%
18,8%
59,9%
22,4%
17,7%
49,5%
26,0%
24,6%
54,0%
24,1%
21,9%
86,5%
87.2%
86.5%
84.4%
89,9%
91.8%
88.4%
86.1%
75,4%
77.7%
74.4%
69.7%
2,1%
1.2%
3.7%
2.9%
17,0%
0.11
0.22
0.27
57,2%
14,3%
0.51
11.7%
0.61
16.2%
0.72
19.8%
58,6%
22,8%
18,7%
33,1%
40,6%
23,3%
Informations
Family / drugs
School /drugs
Friends / drugs
Medias / drugs
Family / alcohol
School / alcohol
Friends / alcohol
Medias / alcohol
Type 1
46.1%
30.0%
46.8%
33.9%
47.8%
22.7%
48.1%
26.9%
Type 2
47.3%
29.2%
57.9%
34.7%
50.7%
25.1%
59.9%
28.6%
Type 3
48.3%
30.4%
63.7%
35.6%
51.4%
25.3%
69.4%
28.7%
Drinking
&
Smoking Behaviours
Total
sample
(N=6534)
Type 1
Type 2
Typ e 3
ALCAN
Type 1
Type 2
Type 3
Smoking initiation
Regular smoker
Has still smoked
cannabis
CANAN
CANAN ¸ 10
Drinking initiation
58,1%
22.7%
19,2%
54,0%
19,7%
15,0%
1
0
0
41.3%
9.9%
3.5%
0
1
0
64.0%
29.2%
30.5%
0
0
1
80.8%
38.5%
31.4%
9.5%
33.1%
57.3%
12,8%
4,2%
77,8%
36,2%
2.3%
1.4%
68.9%
10.3%
27.0%
9.6%
83.0%
53.4%
27.6%
6.5%
98.9%
94.8%
27,9%
3,7%
4.6%
1.1%
40.7%
8.4%
83.8%
6.1%
Has
still
drunk
ALCAN
ALCAN ¸ 10
been
ALCAN
¸ 10
17.8%
51.1%
31.1%
CANAN
10.6%
48.1%
41.3%
CANAN
¸10
Heavy
drinking
/
peers
Heavy
drinking
/
alone
18.9%
51.6%
29.5%
12.8%
38.8%
48.4%
23.7%
43.8%
32.5%
Heavy
drinking
/
bad
state of
mind
22.4%
45.9%
31.7%
Heavy
drinking
/
family
HEavy
drinking
/
annoyed
27.8%
31.8%
40.4%
24.0%
39.7%
36.3%
B
Estimates
Table 1: Semiparametric mo del for cannabis use in the past year
Variables
Probit / CANAN – type 1
PROBIT / CANAN – type 2
PROBIT / CANAN – type 3
Sex
0,385* (0,233)
0,710*** (0,096)
0,407*** (0,096)
Pub.
-0,360 (0,244)
0,139 (0,153)
-0,020 (0,174)
Age
3,511*** (0,785)
4,707*** (0,476)
0,249 (0,418)
2
Age
-0,112*** (0,023)
-0,129*** (0,014)
-0,013 (0,012)
Inc/1000
-0,074 (0,122)
-0,275*** (0,093)
0,190** (0,096)
RateCan
-3,312* (1,413)
1,622*** (0,547)
2,374*** (0,551)
DURCAN
1,714*** (0,127)
1,288*** (0,093)
2,463*** (0,186)
DURCAN2
-0,154*** (0,019)
-0,220*** (0,025)
-0,707*** (0,070)
DURDRUNK
0,895*** (0,096)
0,017 (0,063)
0,214** (0,091)
DURDRUNK 2
-0,072*** (0,016)
-0,012 (0,014)
-0,136*** (0,031)
INFCANfa
-0,306 (1,909)
3,462*** (0,854)
0,672 (0,812)
INFCANsc
-8,637*** (1,966)
-2,809*** (0,858)
0,081 (0,843)
INFCANpe
2,649 (1,931)
4,929*** (1,001)
1,655* (0,944)
INFCANmed
-2,728 (2,477)
-4,254*** (1,076)
1,924* (1,023)
INFALCfa
-0,357 (1,517)
-1,903** (0,814)
-1,020 (0,801)
INFALCsc
6,119*** (1,986)
3,551*** (0,899)
-2,311** (0,961)
INFALCpe
-2,126 (1,600)
-2,162*** (0,813)
1,020 (0,776)
INFALCmed
4,757** (2,189)
1,759* (1,004)
-2,028** (1,009)
Constant
-29,762*** (6,564)
-45,104*** (4,068)
-2,700 (3,520)
Probability weights
58,14%
22,74%
19,12%
Number of observations
6534
6534
6534
Wald Chi-2
643.77
969.25
349.09
Standard errrors in parentheses: *=signi…cant at the 10% level; **=signi…cant at the 5% level; ***=signi…cant at the 1% level
Table 2: Semiparametric model for heavy drinking in the past year
Variables
PROBIT / ALCAN – type 1
PROBIT / ALCAN – type 2
PROBIT / ALCAN – type3
Sex
0,326** (0,138)
0,327*** (0,086)
0,567*** (0,083)
Pub.
-0,073 (0,224)
-0,141 (0,139)
0,018 (0,121)
Age
0,795* (0,449)
6,038*** (0,345)
1,291*** (0,301)
Age2
-0,030** (0,013)
-0,166*** (0,010)
-0,037*** (0,009)
Inc1000
-0,189* (0,102)
-0,056 (0,077)
-0,205** (0,102)
RateDrunk
-0,467 (1,356)
3,810*** (0,809)
1,187 (0,832)
DURCAN
0,669*** (0,116)
-0,116 (0,090)
-0,052 (0,101)
DURCAN2
-0,059*** (0,022)
0,057** (0,025)
-0,052* (0,029)
DURDRUNK
1,505*** (0,066)
0,868*** (0,054)
-0,026 (0,067)
DURDRUNK 2
-0,121*** (0,010)
-0,145*** (0,013)
-0,105*** (0,020)
INFCANfa
0,930 (1,221)
-2,263*** (0,718)
0,096 (0,692)
INFCANsc
-3,525** (1,580)
-0,469 (0,902)
-2,471*** (0,904)
INFCANpe
2,459* (1,283)
1,698** (0,808)
1,417* (0,814)
INFCANmed
-5,009*** (1,476)
0,825 (0,911)
-0,245 (0,879)
INFALCfa
-1,184 (1,132)
2,106*** (0,711)
1,866*** (0,687)
INFALCsc
2,893* (1,645)
-1,079 (0,995)
2,916*** (0,971)
INFALCpe
2,596** (1,071)
1,254* (0,644)
-0,769 (0,660)
INFALCmed
1,058 (1,397)
-2,828*** (0,848)
-0,788 (0,831)
Constant
-9,002** (3,679)
-56,313*** (2,918)
-11,379*** (2,470)
Number of observations
6534
6534
6534
Wald Chi-2
1170.47
1748.92
461.65
Standard errrors in parentheses: *=signi…cant at the 10% level; **=signi…cant at the 5% level; ***=signi…cant at the 1% level
Table 3: Age of iniation into cannabis or drunkenness: semiparametric duration model
Variables
Duration - typ e 1
Sex
-0,406*** (0,047)
Parents not separated
0,169*** (0,055)
Generation 80-81
-0,221* (0,119)
Generation 79-80
0,059 (0,092)
Generation 77-78
0,176** (0,079)
Generation 76-77
0,281*** (0,083)
Generation 75-76
-0,001 (0,079)
Generation 74-75
-0,073 (0,084)
Generation 73-74
-0,327*** (0,097)
Generation 72-73
-0,059 (0,132)
Constant
3,183*** (0,082)
Ancillary parameter - loglogistic law
0,521*** (0,017)
Number of observations
6534
Wald Chi-2
120.14
Standard errrors in parentheses: **=signi…cant at the 5%
Duration - type 2
Duration - type 3
-0,077*** (0,010)
0,001 (0,006)
0,026** (0,012)
0,013* (0,007)
-0,142*** (0,035)
-0,294*** (0,026)
-0,007 (0,026)
-0,156*** (0,014)
-0,336*** (0,019)
0,148*** (0,009)
-0,246*** (0,019)
0,276*** (0,009)
-0,152*** (0,018)
0,375*** (0,009)
-0,119*** (0,020)
0,446*** (0,011)
-0,062*** (0,021)
0,522*** (0,014)
0,052** (0,025)
0,545*** (0,025)
2,110*** (0,018)
1,693*** (0,009)
0,137*** (0,003)
0,077*** (0,002)
6534
6534
621.73
4707.91
level; ***=signi…cant at the 1% level
Table 4: Age of initiation into cannabis - weighted duration analysis
Variables
Type 1
Sex
-0,695*** (0,125)
Pub.
-0,469*** (0,137)
Parents not separated
0,266*** (0,087)
Generation 80-81
0,355 (0,365)
Generation 79-80
0,397 (0,247)
Generation 77-78
0,225 (0,140)
Generation 76-77
0,468*** (0,144)
Generation 75-76
0,082 (0,124)
Generation 74-75
-0,015 (0,141)
Generation 73-74
-0,285* (0,156)
Generation 72-73
0,044 (0,205)
Relative price index of alcohol
11,212*** (1,835)
Relative price index of tobacco
1,835** (0,778)
Constant
-9,266*** (1,944)
Ancillary parameter - loglogistic law
0,461*** (0,061)
Standard errrors in parentheses: **=signi…cant at the 5%
Type 2
Type 3
-0,126*** (0,022)
-0,017 (0,018)
-0,044 (0,028)
-0,075** (0,029)
0,076*** (0,021)
0,072*** (0,021)
0,194 (0,191)
-0,240*** (0,076)
0,374*** (0,111)
-0,100** (0,039)
-0,365*** (0,059)
0,072** (0,032)
-0,337*** (0,065)
0,215*** (0,033)
-0,276*** (0,071)
0,293*** (0,037)
-0,248*** (0,083)
0,393*** (0,046)
-0,195** (0,090)
0,513*** (0,051)
-0,072 (0,104)
0,461*** (0,091)
-1,488 (1,117)
-2,467* (1,318)
-0,877 (0,651)
-0,669 (0,540)
5,005*** (1,830)
5,313*** (1,872)
0,183*** (0,013)
0,162*** (0,016)
level; ***=signi…cant at the 1% level
C
Price and information e¤ects
@xC
@pA
²
is the derivative of the marshallian cannabis demand with respect to
the price of alcohol (obtained from table 4).
²
@xC
@y
is the income e¤ect for cannabis participation (obtained from table
@h C
@pA
is the derivative of the hicksian cannabis demand with respect to the
²
1).
price of alcohol. By Slutski equation:
@hC
@p A
=
@xC
@p A
C
+ x A @x
@y .
² As the matrix of hicksian price e¤ects is symmetric:
²
²
@xA
@y
@hC
@ pA
=
@hA
@ pC
.
is the income e¤ect for drunkenness participation (obtained from table
2).
@xC
@pA
is the derivative of the marshallian alcohol demand with respect to
A
A
A
price of cannabis: @x
= @h
¡ x C @x
.
@p C
@ pC
@y
Table 5: Signs of direct and cross price e¤ects
@xC
@xC
@hC
@h A
@xA
@xA
@p A
@y
@ pA = @ pC
@y
@ pC
Type 1
-***
0
-*
?
Type 2 0 (+?) -***
-?
0
-?
Type 3
+*
+**
+
-**
+
Let ¼ A and ¼ C be respectively the full prices of alcohol and cannabis:
i
sign( @@¼xji ) = sign( @x
);
i; j = A; C. The following tables show the predicted
@p j
signs of correlations between information and consumption if informations a¤ect
consumptions only through changes of full prices.
Table 6: Predicted signs of the direct and cross information e¤ects:
type 1
Participation decision
Information about...
¼ A %; ¼ C %
¼ A &; ¼ C %
¼ A %; ¼ C &
¼ A &; ¼ C &
ALCAN
Alcohol Cannabis
?
+
?
?
+
?
CANAN
Alcohol Cannabis
+
+
+
+
Table 7: Observed signs of the direct and cross information e¤ects:
type 1
33
Participation decision
Information about...
from school
from friends
from the family
from the medias
ALCAN
Alcohol Cannabis
+*
-**
+**
+*
+
+
-***
CANAN
Alcohol Cannabis
+***
-***
+
+**
-
Table 8: Predicted signs of the direct and cross information e¤ects:
type 2
Participation decision
ALCAN
CANAN
Information about...
Alcohol Cannabis Alcohol Cannabis
¼ A %; ¼ C %
-?
+?.
¼ A &; ¼ C %
+
-?
-?
¼ A %; ¼ C &
+?
+?
+
¼ A &; ¼ C &
+
+?
-?
+
Table 9: Observed signs of the direct and cross information e¤ects:
type 2
Participation decision
ALCAN
CANAN
Information about...
Alcohol Cannabis Alcohol Cannabis
from school
+***
-***
from friends
+*
+**
-***
+***
from the family
+***
-***
-**
+***
from the medias
-***
+
+*
-***
Table 10: Predicted signs of the direct and cross information effects: type 3
Participation decision
Information about...
¼ A %; ¼ C %
¼ A &; ¼ C %
¼ A %; ¼ C &
¼ A &; ¼ C &
ALCAN
Alcohol Cannabis
+
+
+
+
-
CANAN
Alcohol Cannabis
+
+
+
+
Table 11: Observed signs of the direct and cross information effects: type 3
Participation decision
ALCAN
CANAN
Information about...
Alcohol Cannabis Alcohol Cannabis
from school
+***
-***
-**
+
from friends
+*
+
+*
from the family
+***
+
+
from the medias
-**
+*
34