Table 2 Ž .
Summary of drug use by employment status Men
Women In work
Unemployed In work
Unemployed Recreational drugs
Ž .
Ž .
Ž .
Ž .
Ever used 33.00 0.58
43.55 1.72 22.39 0.53
30.56 2.32 Ž
. Ž
. Ž
. Ž
. Recently used
11.44 0.40 26.54 1.53
7.53 0.33 15.40 1.82
Dependency drugs Ž
. Ž
. Ž
. Ž
. Ever used
3.88 0.24 9.77 1.03
2.49 0.20 4.80 1.08
Ž .
Ž .
Ž .
Ž .
Recently used 1.01 0.12
4.34 0.71 0.60 0.10
1.51 0.61 Observations
6467 829
6216 577
Standard errors in parentheses.
the occupation in question. It is thus to be interpreted as a measure of the labour market status of the individual’s occupation, rather than as an indicator of his or
her actual wages, or of success within an occupation. We return to this issue in Section 5 when interpreting the results.
We calculate the mean hourly wage associated with each occupation using Ž
. pooled data from the UK Quarterly Labour Force Survey QLFS for 1993, 1994
Ž .
and 1995 12 quarterly surveys in all . The QLFS codes occupation to the
three-digit level of the Standard Occupational Classification, which gives 899 possible occupation categories. These occupational codes are also used in the
BCS, allowing us to map the mean hourly wage from each occupational category in the QLFS to individual occupations in the BCS. Given that there are nearly 900
occupations defined in the survey, we treat the associated mean hourly wage as a continuous variable in our analysis.
A casual look at the distribution of occupational status by drug use status reveals an interesting feature in the current sample, which reflects findings from
other data. Average occupational status for those who have ever used drugs is higher than the wage for those who report no drug use ever. This result holds for
men and women, for the younger and older cohorts, and for any category of drug use. This last observation suggests two potentially opposing outcomes of drug use:
unemployment or enhanced occupational attainment. These two associations may represent opposite causal links: drug use may raise the risk of unemployment,
Ž .
whereas occupational success and high income may raise the demand for drugs. We consider these outcomes further in the following sections, focusing on the
difference between past and current drug use.
4. The empirical model
We consider an individual’s life as consisting of two periods. The past period finishes 12 months before the survey interview, and the current period covers the
12 months preceding the interview.
4
These two periods allow us to define past and current drug use. The levels of drug use in these two periods are represented by a
Ž .
pair of trichotomous indicators d t
s 1,2 , where d s 0 indicates no drug use,
t t
d s 1 indicates use the use of ‘soft’ drugs only and d s 2 indicates the use of
t t
Ž .
‘hard’ or hard and soft drugs. We present results below for two alternative definitions of ‘soft’ and ‘hard’ drugs. We also define two indicators of labour
market outcomes: current unemployment u and, for those in work, occupational attainment a. Both relate only to the current period. The variable u is a binary
indicator, and the occupational attainment variable a is treated as continuous.
Before we can define our empirical model, we first have to confront a serious observational problem stemming from the design of the questionnaire used in the
Ž .
BCS and in other US and European surveys . The respondent is asked only whether or not he
rshe has ever used drugs, and, if so, whether or not within the last year. This questionnaire structure has the unfortunate feature that if a
respondent reports drug use in the current period, we do not know whether or not there was any use in the past period. Thus, d
and d are only partially
1 2
observable. Nevertheless, it is possible to estimate a suitable model by means of an iterative maximum likelihood method.
First, consider the determination of past drug use. We define a latent variable d
which represents an individual’s past propensity to consume drugs. This drives
1
the observed indicator of actual drug use, d , through an ordered probit mecha-
1
nism: d
s x b q ´ , 1
Ž .
1 1
1 1
d s r
F C F d - C
, r
s 0,1,2, 2
Ž .
Ž .
1 1 r
1 1 r
q1
Ž .
where F J
is the indicator function, equal to 1 if the event J occurs and 0 otherwise. C
s y`, C s q` and C , C are unknown threshold parameters; x
3 1
2 1
is a row vector of personal and demographic attributes, b is the corresponding
1
Ž .
vector of parameters, and ´ is a N 0,1 random error.
1
The second stage of the model determines current drug use, current unemploy- ment and occupational attainment jointly, but conditional on past drug use. This is
Ž .
achieved through a system of three latent variables d , u , and a representing
2
the individual’s unobserved current propensities to consume drugs, to be unem-
4
We could elaborate this and work with three periods: up to 1 month ago; 1–12 months ago; and more than 12 months ago. However, the 1-month period gives few additional observations between
drug use categories, and it greatly complicates the analysis. Thus, we lose little by ignoring the 1-month response.
ployed, and to do well when employed. These are generated by the following multivariate regression structure:
d s x b q j d q j d q ´ ,
3
Ž .
2 2
2 1
21 2
22 2
u s x b q j d q j d q ´ ,
4
Ž .
3 3
1 31
2 32
3
a
s x b q j d q j d q ´ ,
5
Ž .
4 4
1 41
2 42
4
where x . . . x are row vectors of personal and demographic attributes, b . . . b
2 4
2 4
are the corresponding vectors of parameters, and ´ . . . ´ are errors with a
2 4
trivariate normal distribution with zero means, unit variances and unrestricted 4
correlations, conditional on x s x , x , x
and d . The variables j and j
are
1 2
3 1
1 2
Ž .
binary indicators defined as j s
F d s r . Thus, e.g., the total impact of ‘hard’
r 1
drug use on the tendency for unemployment is represented by d .
32
The observable counterparts of these latent variables are the indicators of current drug use d , unemployment u and occupational achievement a. The latent
2
Ž Ž .
Ž .. variables Eqs. 3 and 4
are assumed to generate the observed states by means of the following relationships:
d s r
F C F d - C
, r
s 0,1,2, 6
Ž .
Ž .
2 2 r
2 2 r
q1
u s
F u 0 ,
7
Ž .
Ž .
where the C are threshold parameters subject to normalising restrictions as
2 r
before. Unfortunately, we cannot observe the nine joint outcomes for d
and d
1 2
directly, owing to the questionnaire design. Instead, there are six possible observa- tional outcomes in terms of drug use. These are summarised in Table 3.
Table 3 Possible observable outcomes from the BCS questionnaire
Observational regime Probability
Ž . Ž
. Ž
. 1 No use of drugs ever
Pr d -C
Pr d -C , u, aN d s 0
1 11
2 21
1
Ž . Ž
. Ž
. 2 No current use, only
Pr C - d -C
Pr d -C , u, aN d s1
11 1
12 2
21 1
soft drugs in the past Ž .
Ž .
Ž .
3 No current use, hard Pr C
- d Pr d
-C , u, aN d s 2
12 1
2 21
1
drugs in the past Ž .
Ž .
Ž .
4 Current use of soft drugs, Pr d
-C Pr C
- d -C , u, aN d s 0
1 11
21 2
21 1
Ž .
Ž .
no hard drugs ever qPr C
- d -C Pr C
- d -C , u, aN d s1
11 1
12 21
2 22
1
Ž . Ž
. Ž
. 5 Current use of soft drugs,
Pr d C
Pr C - d -C , u, aN d s 2
1 12
21 2
21 1
past use of hard drugs Ž .
Ž .
Ž .
6 Current use of hard drugs Pr d
-C Pr d
C , u, aN d s 0
1 11
2 22
1
Ž .
Ž .
qPr C - d -C
Pr d C , u, aN d s1
11 1
12 2
22 1
Ž .
Ž .
qPr d C
Pr d C , u, aN d s 2
1 12
2 22
1
Ž Ž . Ž ..
Given the linear rnormal structure Eqs. 1 – 7 , the outcome probabilities are
Ž .
Ž readily but very tediously constructed see MacDonald and Pudney, 2000 for
. Ž
.
5
more details . They require evaluation of at most bivariate normal probabilities. These probabilities are then used to construct the following log-likelihood func-
tion, which we maximised numerically using GAUSS MAXLIK software:
n
ln L s
lnPr d x
q lnPr d ,u , a x , x , x , d .
8
4
Ž .
Ž .
Ž .
Ý
1 i 1 i
2 i i
i 2 i
3 i 4 i
1 i
i s1
5. Results
