THE ECONOMIC RECORD, VOL. 78, NO. 1, MARCH, 2002, 79–96
The Effect of Alcohol Consumption on Earnings*
GARRY F. BARRETT
Department of Economics,
University of New South Wales
Sydney, NSW 2052
The effect of alcohol consumption on the earnings of full-time male
workers is analysed using the Australian National Health Survey
1989–90. A multinomial logit choice model is used to explain
workers’ choice of drinking status and to correct for selection bias in
the earnings equation estimation. It is found that moderate drinking
leads to a significant earnings premium relative to abstention and
heavy drinking. The results are consistent with moderate drinking
improving individual’s health and hence productivity and earnings.
depression and improved cognitive performance
(such as problem solving and short term memory) (Baum-Baicker 1985b). By the same token,
the negative health effects of heavy or excessive
alcohol consumption have long been known. The
harmful health consequences of heavy drinking
and alcohol dependence range from gastrointestinal diseases (such as cirrhosis) to endocrine and
metabolic disorders and behavioural problems
(Last 1998). Recently researchers have begun to
examine whether the health effects of drinking
patterns translate into labour market outcomes.
The potential impact of health status on
earnings represents a straightforward extension
of the human capital framework of earnings
determination2 which has been confirmed in
numerous empirical studies.3 More specifically,
current alcohol consumption can influence contemporaneous health status (such as through the
beneficial psychological effects of moderate
drinking, or the immediate after effects of heavy
drinking), as may drinking behaviour over an
extended period of time (through the physiological effects of long term moderate and heavy
I Introduction
In recent years numerous reports have
appeared in the popular press highlighting the
health benefits of moderate alcohol consumption.
There is a substantial body of medical research
examining the health consequences of drinking
behaviour, and the beneficial effects of moderate
alcohol consumption are well established. For
example, moderate drinking is associated with a
lower risk of coronary heart disease, due to
increases in the level of beneficial cholesterol
(HDL) in the bloodstream, and a lower risk
of mortality in general (Baum-Baicker 1985a;
Shaper 1993)1 Psychological benefits from moderate alcohol consumption have commonly been
found, including reduced stress, anxiety and
*I wish to thank Martin Browning, Bruce Chapman,
Tom Crossley, Catherine de Fontenay, David Green,
Bob Gregory, P.J. O’Reilly, Adrian Pagan, Craig
Riddell, Michael Veall, Jenny Williams, seminar participants at the Australian National University,
McMaster University, the University of Adelaide, the
University of New South Wales, participants at the
Australian Labour Econometrics Workshop and two
anonymous referees for constructive comments on a
previous draft. Research support through the UNSW
ARC Small Grants Scheme is gratefully acknowledged.
The usual disclaimers apply. Email: g.barrett@unsw.
edu.au
1
The American Journal of Public Health 1993
vol.83(6) issue contains a series of editorials reviewing
the literature on the health effects of alcohol consumption.
2
Grossman (1972) was the first to formally incorporate the impact of health status and health risk/
maintenance behaviour in the human capital model of
earnings determination.
3
Smith (1999) provides an overview of recent
research on the relationship between health and
economic status.
79
Ó 2002. The Economic Society of Australia. ISSN 0013-0249.
80
ECONOMIC RECORD
drinking). The health consequences of drinking
may then affect productivity at work (such as
through absenteeism, reliability and overall
job performance). In competitive labour markets
workers receive a wage reflecting their productivity, hence alcohol consumption patterns may
ultimately be reflected in individual earnings.
A number of studies have examined the effect
of alcohol consumption on earnings while treating drinking status as exogenous. Mullahy and
Sindelar (1991, 1993) have shown that extremely
heavy alcohol consumption, as evidenced by
alcoholism, is associated with substantially lower
earnings and income. French and Zarkin (1995)
analysed data from a special company-based
survey in the United States and found a significant wage premium for drinkers, although the
premium declined for the most frequent drinkers.
However, in an attempt to replicate these results
with a nationally representative sample, Zarkin
et al. (1998) found no evidence of an inverse
U-shaped relationship between alcohol use and
wages. They found that drinkers received
approximately 7 per cent higher wages and that
premium was received across a very wide range
of average daily alcohol consumption levels. Lye
and Hirschberg (1999) used the Australian
National Health Survey for 1995 to examine
the effects of drinking, smoking, exercise and
weight gain on earnings. They found that drinking yielded an earnings premium, and the earnings premium for both men and women was
maximised at an average daily consumption level
of approximately four drinks. A limitation of
these studies is that the estimated earnings
impact of drinking may simply reflect the reverse
effect of income on alcohol consumption levels.
Indeed, the finding of higher earnings for drinkers is consistent with the alternative hypothesis
that alcohol is simply a normal good. To isolate
the effect of alcohol consumption on earnings it
is necessary to control for the potential endogeneity of an individual’s drinking status.
Several studies have addressed the endogeneity
of drinking status in analysing the effect of
alcohol on earnings. The first was Berger and
Leigh (1988), which was also the first study to
analyse the link between alcohol consumption
and earnings. Using United States data for 1972–
1973, Berger and Leigh estimated separate earnings equations for drinkers and non-drinkers,
after estimating a first-stage probit equation to
account for the endogeneity of drinking status.
They found that drinkers earned a substantial
MARCH
wage premium relative to non-drinkers, and the
premium did not diminish for the most frequent,
heavy drinkers. Kenkel and Ribar (1994) used
data from the US National Longitudinal Survey
of Youth and implemented a range of estimators
to examine the earnings effect of heavy drinking4
and alcohol abuse.5 In their preferred specification which used instrumental variable methods
they estimated that heavy drinking led to a
12 per cent earnings penalty (and alcohol abuse
to a 30 per cent penalty) for young men.
Hamilton and Hamilton (1997) analysed Canadian data and divided the population of male
workers into non-drinkers, moderate and heavy
drinkers. They estimated separate wage equations after controlling for an individual’s selection of drinking status using a multinomial logit
choice model. Hamilton and Hamilton found
that moderate drinkers received a wage premium
relative to non-drinkers while heavy drinkers
received a substantial wage penalty.
This study contributes to this body of literature by examining earnings differentials across
drinker types using the Australian National
Health Survey for 1989–1990 (NHS89). Following Hamilton and Hamilton (1997) the population of male workers is divided into three groups
(non, moderate and heavy drinkers) to differentiate between the health and hence productivity
and earnings consequences of different drinking
patterns. The multinomial logit model is used to
control for the choice of drinking status.
The empirical framework explicitly addresses
the endogeneity bias that may arise through the
simultaneity of drinking status and earnings,
due to the reverse causation from income to
alcohol consumption or unobserved heterogeneity. The NHS89 contains detailed information
on respondents’ alcohol consumption and socioeconomic backgrounds and is representative of
the national population, unlike the data utilised
by Kenkel and Ribar (1994) which was for a
cohort of youths. The NHS89 has the additional
advantage of providing both a substantial sample
4
Defined as drinking six or more drinks on at least
one day during the reference month.
5
Alcohol abuse was defined as a ‘maladaptive
pattern of alcohol use evidenced by (self-reported)
continued drinking despite a persistent social, occupational, psychological or physical problem related to
drinking.’
2002
81
ALCOHOL CONSUMPTION AND EARNINGS
of observations and a large array of covariates.
In particular, the NHS89 includes a number of
potential instruments for drinking status and
contains detailed information on job characteristics. These attributes make the NHS89 a richer
data source than that examined by Hamilton and
Hamilton (1997), providing potentially stronger
identification of the effect of alcohol consumption on earnings.
Two sets of instruments for identifying individuals’ choice of drinking status are used in the main
part of the analysis presented in this paper. The
instruments are a dummy variable indicating whether the individual smoked when 18 years of age
(providing a retrospective measure of an individual’s attitude toward risk) plus a set of variables
measuring drinking patterns in an individual’s local
region (which capture social influences on drinking
behaviour).6
The major finding from the analysis is that there
is a significant, positive earnings differential for
moderate drinking and a significant earnings
penalty for heavy drinking. These earnings effects
are masked by important selection bias effects in
the raw data which highlights the salience of
controlling for the endogeneity of individuals’
drinking behaviour. The earnings premium for
moderate drinkers, relative to both abstainers and
heavy drinkers, is predominantly due to greater
returns to personal and job characteristics rather
than to differences in the level of those characteristics. This finding is consistent with the hypothesis
that moderate alcohol consumption has beneficial
health and hence labour market productivity
effects. The results are somewhat mixed regarding
the abstention-heavy drinking earnings differential. However, based on the specification of the
earnings equations most strongly supported by the
data, abstainers are found to receive an earnings
premium relative to heavy drinkers that is consistent with the negative productivity and earnings
consequences of excessive alcohol consumption.
This paper is organised as follows. The next
section outlines the empirical model used to
analyse worker selection of drinking status and
to isolate the independent effect of drinking on
earnings. In section III the data used in the
analysis are detailed. The results of the earnings equation estimation, earnings decompositions and specification tests are presented in
section IV. Finally, section V briefly summarises the major results and offers concluding
comments.
II Empirical Model
The objective of the analysis is to test whether
alcohol consumption has an impact on individuals’
earnings while allowing for the endogeneity of
drinking status. Assume that the potential earnings
for individual i with drinking status j is given by
lnwij ¼ x0i bj þ eij ;
j ¼ 1; 2; 3
ð1Þ
where xi is vector of human capital and demographic characteristics and eij Nð0; r2j Þ: This
specification is very general, allowing the labour
market returns to individual characteristics to
differ by drinking status. By comparing the
estimated b across drinker types it is possible
to gauge whether the labour market rewards for
observed productivity-related characteristics are
greatest for moderate drinkers, and lowest for
heavy drinkers, as implied by the medical
research on the health effects of alcohol
consumption.
Individuals are assumed to select the earningsdrinking status combination that maximises
expected utility. The ith individual’s expected
utility from an earnings-drinking status combination is modelled by the index function
Vij ¼ z0i cj þ tij ;
j ¼ 1; 2; 3
ð2Þ
The vector of individual characteristics, zi ,
includes all the variables that may determine
earnings (xi ) plus additional variables that solely
reflect the individual’s preferences over levels of
alcohol consumption. Assuming the tij error
terms are distributed according to the type I
extreme value distribution gives the standard
multinomial logit choice model
PrðIi ¼ jÞ ¼
expðz0i cj Þ
P
expðz0i cs Þ
3
ð3Þ
s¼1
6
It is noted that the variables in the NHS89 used to
construct the instruments for drinking status (age first
commenced smoking, and location by health region)
are not available in the NHS95.
The earnings equations are estimated using an
extension of the generalised two-step procedure
presented in Lee (1982, 1983). The appropriate
specification of the earnings equation conditional
on alternative j being chosen is:
82
ECONOMIC RECORD
lnwij ¼ x0i bj rj qj
/fU1 ½F ðz0i cj Þ g
þ nij
F ðz0i cj Þ
ð4Þ
where F denotes the multinomial logit distribution function, U and / denote the standard
normal distribution and density functions,
respectively, qj is the correlation coefficient
between the unobservables in the earnings and
selection equations and Eðnij jIi ¼ jÞ ¼ 0: The
second term on the right-hand side of equation
(4) controls for the truncated mean of the
observed residual in the earnings equations
arising from individuals selecting their preferred drinking status. The truncated mean is
a generalisation of the standard Heckman
correction term (inverse mills ratio) to the
situation where individuals choose over multiple alternatives.7
In the NHS89 data earnings are censored into
discrete intervals and an indicator variable
denoting that earnings fall into a specific interval
is reported. The income range is divided into
K intervals, the kth given by ðAk1 ; Ak ; with
A0 ¼ 1 and AK ¼ 1: The observed earnings
information is lnwij ¼ ki iff Ak1 < lnwij Ak:
This data structure corresponds to the grouped
regression model examined by Stewart (1983).
The log-likelihood function for the grouped
regression model extended to control for selectivity bias with a polychotomous choice model is
given by:
LLFj ¼
K X
X
ln U ðAk x0i bj þ rj qj
MARCH
tion coefficient qj < 0 ð> 0Þ implies that individuals
who chose drinking status j have unobserved
characteristics which lead them to earn more
(less) than a person randomly drawn from the
population and assigned drinking status j. Alternatively, qj ¼ 0 implies no correlation in the
residuals of the earnings and choice equations
and hence there is no apparent selection bias in
the observed earnings for drinker type j.
III Data
The data analysed in this study are from the
Australian National Health Survey 1989–1990
conducted by the Australian Bureau of Statistics.
The NHS89 is a large, nationally representative
survey which collected information by personal
interview on individuals’ health status, health risk
factors, including detailed information on alcohol
consumption, as well background information on
labour market activity over the previous year. The
sample that is analysed is restricted to men aged
between 25 and 59 years,8 who are full-time9
wage and salary workers10 and whose usual
major activity is working.11
The earnings variable available in the NHS89 is
gross personal income annualised for the year prior
to the survey date.12 The sample restrictions are
to ensure the income measure corresponds to
earnings as closely as possible. Additionally,
focussing on full-time employees will ensure that
the predominant source of variation in the
income measure will be hourly earnings.13 One
advantage of the NHS89 over other data
k¼1 i2k
ð5Þ
/ðU1 ðF ðz0i cj ÞÞ=F ðz0i cj ÞÞÞ=rj
1
U ðAk1 x0i bj þ rj qj /ðU ðF ðz0i cj ÞÞ=
F ðz0i cj ÞÞÞ=rj g; j ¼ 1; 2;3
Estimating equation (5) provides consistent estimates of bj . The first step is to estimate the
multinomial logit model of equation (3) to obtain
^c. In the second step, the ^cj are substituted for c in
(5) and the grouped regression models are then
estimated. It is then possible to formally test for the
presence of selectivity bias. An estimated correla-
7
This empirical model has also been used by Trost
and Lee (1983) to examine the returns to education,
and by Gyourko and Tracy (1988) to analyse union
and public sector wage differentials, while controlling
for sample selection.
8
Prime age men were chosen in order to avoid
having to model participation decisions.
9
The NHS contains a limited amount of information
on hours worked per week, and individuals who
worked less than 35 hours per week were dropped
from the sample.
10
The self-employed are therefore excluded.
11
Students are excluded through this restriction.
12
Gross income per pay period (the latter measured
in weeks) was recorded in the survey and the annualised amount was reported in the public release version
of the NHS89.
13
It is possible that alcohol consumption, especially
extremely heavy drinking and alcoholism, may affect an
individuals’ hours of work. The NHS89 has limited
information on labour supply, and it is not possible to
adequately analyse the effect of drinking on both
earnings and labour supply.
2002
ALCOHOL CONSUMPTION AND EARNINGS
examined in this literature is that is contains
relatively rich information on individuals’ job
characteristics, including industry, occupation
and sector of employment.
The NHS89 reports detailed information on
individuals’ drinking patterns. The survey records
the period since the respondent last had a drink
and, for those who drank in the week prior to the
survey, details the types and number of drinks
consumed on each day of the reference week. The
ABS collected the information on the consumption of specific types of alcoholic beverages in
terms of regular measures and then translated this
into units (ml ) of pure alcohol. Through these
procedures the NHS89 provides reliable information on the quantity of alcohol consumed
during the reference period.
A limitation of the NHS89 is that the
reference period for the detailed information
on drinking behaviour is only 1 week. Ideally,
information on an individual’s history of
drinking behaviour would be available and
used to control for impact of long-term
drinking patterns as well as current behaviour.
Because the NHS89 measures only contemporaneous drinking behaviour, the short-term
effects of drinking on productivity and earnings should be captured in the empirical
analysis. To the extent that there is persistence
in drinking behaviour over time, contemporaneous drinking behaviour will also proxy
longer-term drinking patterns and the empirical analysis may capture any systematic relationship between long-term drinking behaviour
and earnings.14
The sample of workers is divided into three
drinker types: non-drinkers, moderate drinkers
and heavy drinkers. Non-drinkers are defined
as those individuals who have never drank or
who did not have a drink in the month prior
14
Cook and Moore (2000) examined the persistence of
drinking patterns using the NLSY panel which surveyed
alcohol consumption patterns in 4 consecutive years
1982–1985. There was strong evidence of persistence in
drinking behaviour. For instance, the probability an
individual abstained in 1985 given 3 consecutive years of
abstinence was 0.84. The probability of drinking (bingeing) in 1985 given 3 successive years of drinking (bingeing)
was 0.90 (0.45). Although these data are from a youth
cohort in the USA, they suggest that the contemporaneous drinking information in the NHS89 may reflect longterm drinking patterns.
83
to the survey.15 The demarcation between moderate and heavy drinking adopted in this study
follows that used by Hamilton and Hamilton
(1997) drawn from the medical research literature. This research has shown that the intensity
of drinking (the amount consumed on 1 day or
at a single sitting) is more strongly related to
problem drinking and tangible health effects than
either the average amount consumed over a
period of time or simply drinking frequency.
As Hilton (1987: 172) found, ‘‘high maximum
[binge] drinking bears a much stronger relationship to drinking problems than does the frequency of drinking’’ which supported Knupfer’s
conclusion (Knupfer 1984) that it is the incidence
of intoxication rather than drinking per se that
places an individual at risk of drinking problems.
Based on this body of work, a heavy drinker is
defined as someone who drank eight or more
standard drinks16 on at least one day during the
reference week. Therefore moderate drinkers are
defined as anyone who had a drink in the last
month and did not drink more than seven
standard drinks on any given day during the
reference period.
There are a range of alternative definitions
of heavy versus moderate drinking available in
the literature. For instance, Kenkel and Ribar
(1994) also associate heavy drinking with binge
drinking but define a lower threshold for heavy
drinking at six or more drinks on any one day.
Alternatively, the National Health and Medical
Research Council (NHMRC) (1992) has published guidelines for ‘responsible daily alcohol
consumption levels’. The NHMRC guidelines
identify low, medium and high risk average daily
consumption levels.17 The medium risk consumption level is defined as four to six standard drinks
per day on a regular basis, and high risk is
defined as more than six drinks per day on a
regular basis. The NHMRC definition of high
risk requires both intense drinking and a high
15
This is the standard definition of non-drinker used
in the literature: see, e.g., Zarkin et al. (1998).
16
One standard drink corresponds to 10 grams (or
14.286 mL) of pure alcohol.
17
The NHMRC (1992, 2) defines the medium and
high risk categories as synonymous with ‘‘hazardous’’
and ‘‘harmful’’ consumption levels, respectively. The
NHMRC recommendations correspond to those of the
World Health Organization.
84
ECONOMIC RECORD
average consumption level, whereas in the classification system adopted in this study, intense
drinking on an infrequent basis (with possibly
low average consumption levels) is sufficient for
categorising someone as a heavy drinker. To
check the sensitivity of the results to the classification of drinking patterns, the analysis was
also implemented using these alternative categorisations of drinking patterns and the results are
discussed in section IV.
The explanatory variables assumed to influence
earnings include age, educational attainment,
marital status, state of residence, industry and
occupation of employment and an indicator for
whether an individual works in the public or
private sector. In order to control for the effect of
income on drinking behaviour, all the variables
that determine earnings are also included in the
drinking status choice equation.
To identify the selectivity correction terms
(SCT) in the earnings equations it is necessary
to include additional variables in the drinking
status choice model that are excluded from the
earnings equations.18 Two sets of variables are
used as instruments for drinking status. The first
is motivated by the idea that alcohol is usually
consumed in a social context and that the
drinking behaviour of an individual may be
influenced by their social environment. The
importance of social influences on drinking
behaviour has long been recognised by behavioural scientists (Last 1998; NHMRC 1992) but
has only recently begun to be incorporated into
economic analyses (Cook and Moore 1999,
2000). An important avenue of social influence
is the local, or neighbourhood, network19
through which beliefs, norms and practices are
communicated. These factors help determine the
‘wetness’ of the local social environment which
may influence individual’s drinking behaviour.
For example, a preponderance of neighbours
18
The SCT may be identified through the nonlinearity of its functional form rather than through
exclusion restricitions. This strategy is not followed
here because it is based on ad-hoc distributional
assumptions and empirically such identification often
proves to be very weak.
19
Social networks may be based on other bonds,
such as religion, ethnicity or occupation. Last (1998)
provides a detailed discussion of these factors in
relation to health behaviours and specifically alcohol
consumption.
MARCH
who are heavy drinkers may increase the probability an individual is a heavy drinker.20 To
capture these social influences, the proportion of
sample members in the individual’s local area
(excluding that individual) who are abstainers
and who are heavy drinkers are calculated and
included in the estimation of the choice model.21
The NHS89 records an individual’s location in
one of 47 health regions. The health regions are
based on contiguous local government areas and
are taken as an approximation to an individual’s
local area.
The second instrument for drinking behaviour is
a variable indicating whether the individual
smoked at age 18.22 The rationale for this
instrument is that, like alcohol consumption,
smoking is a health risk behaviour that, in part,
reflects an individual’s attitude toward risk
(because the activity affects the probability of
different health outcomes). In a similar vein,
Hersch and Viscusi (1990) used contemporaneous
20
Manski (1993) examines the related problem of the
identification of endogenous social effects and shows
that it is difficult to distinguish such effects from
exogenous contextual or correlated effects (eg. common
responses to the relative price of alcohol in the local
product market). Although the presence of the latter
social effects are important for the interpretation of the
local area variables in the drinking choice equation,
they do not affect the consistent estimation of the
earnings equations unless they capture components of
individual unobserved heterogeneity. Tests of the
validity of excluding the local area variables from the
wage equations should safeguard against this
possibility.
21
Formally, for individual i in region k, the proportion of the population in thePlocal region who are
abstainers is calculated as:
j2Nk ;j6¼i 1( j=abstainer)=
ðNk 1Þ, where 1(.) is the indicator function which is
equal to 1 whenever the condition (that observation j is an
abstainer) is true and is equal to 0 otherwise, and Nk is the
number of individuals in local region k . The proportion of the population in the individual’s local area
who are moderate and heavy drinkers is calculated
analogously. Note that by omitting an individual’s own
drinking status from the calculation of the local area
(reference group) variables they are not definitionally
related to the individual’s choice of drinking status.
22
Evans and Montgomery (1994) provide an indepth analysis of the validity of the ‘smoked at age 18’
indicator variable as an instrument for educational
attainment of men. Evans and Montgomery (1994, 33)
also suggest the potential usefulness of it as an
instrument for heavy drinking.
2002
ALCOHOL CONSUMPTION AND EARNINGS
smoking behaviour as a proxy for individuals’
attitudes toward risk in estimating wage differentials for risk of lost work-day injury. Consistent with smoking behaviour reflecting an
individual’s willingness to bear risk, they found
that smokers received lower wage compensation
per unit of risk relative to non-smokers.
In the NHS89 data, whether a person
smoked at age 18 is strongly correlated with
current drinking status. However, to be a valid
instrument, whether a person smoked at age 18
must not directly affect their current earnings.
Note that this is a retrospective measure of
tobacco consumption and even if current smoking behaviour affected current earnings, whether
a person smoked in the distant past is unlikely to
influence current earnings. Indeed there is considerable churning of individuals’ smoking status
between age 18 and the survey date, with
approximately 43.2 per cent who reported to be
smokers at age 18 having quit by the time of the
survey while 27.1 per cent of individuals who
were smokers at the time of the survey had
started after age 18.23 The substantial changes in
smoking status between age 18 and the date of
the survey suggests that past smoking is not
directly related to current earnings; this conclusion is confirmed by specification tests presented
below.
The sensitivity of the results to the choice of
instruments and sample composition are reported
in section IV. Because two sets of instruments
are used to identify individuals’ choice of drinking status, the SCT in the earnings equation are
overidentified and the overidentification restrictions are used to formally test (and confirm) the
validity of each instrument. The consequences of
using alternative instruments and model specifications are also reported in section IV.
Summary statistics for the sample are presented in Table 1.24 The sample consists of 5705
individual observations, of which 14 per cent are
23
Interestingly, Evans and Montgomery (1994, 9)
report very similar changes in smoking status between
age 18 and the survey date for respondents to the USA
1987 National Medical Expenditure Survey.
24
The NHS89 data file contains record weights
derived by the ABS, based on the sampling frame,
which make the data representative of the Australian
population. The summary statistics in Table 1, and all
of the subsequent analysis, take the record weights into
account.
85
non-drinkers, 67 per cent are moderate drinkers
and 19 per cent are heavy drinkers. Moderate
drinkers earn approximately 4 per cent more
than heavy drinkers who, in turn, earn 8 per cent
more than abstainers.25 Compared to moderate
drinkers, the group of non-drinkers has a greater
proportion of older workers while there is a
concentration of younger workers in the heavy
drinker category.26 Moderate drinkers tend to
have higher levels of educational attainment,
while trade qualifications are most common
among the heavy drinkers. Moderate drinkers
also have the greatest probability, and heavy
drinkers the lowest probability, of being
married.27 Non-drinkers are more likely to be
employed in manufacturing industries and as
plant operators or manual labourers. Moderate
drinkers tend to be employed in manufacturing
and service industries and in administrative or
professional occupations, while heavy drinkers
are relatively more likely to work in the construction and trade industries as either tradesmen
or in other blue-collar occupations. The smoked
at age 18 variable indicates that a large portion
of the full sample were smokers at 18, the
incidence of which was lowest among current
non-drinkers and highest among heavy drinkers.
The sample means for the proportion of the
local area population in each drinker type
indicates that abstainers reside in areas where
there is a slightly above average fraction of
abstainers. Similarly, moderate drinkers tend to
be located in areas where there is a marginally
higher proportion of moderate drinkers,
and heavy drinkers tend to be located in areas
with an above average proportion of heavy
drinkers.
25
The descriptive statistics for income were derived
by taking the log of the midpoint of an individual’s
earnings category. The analysis that follows takes into
account the censoring of earnings into groups.
26
By taking the midpoint of the age category for an
individual, the average age of non-drinkers is
40.1 years, for moderate drinkers is 38.0 years and for
heavy drinkers is 35.2 years.
27
It is possible that marital status is endogenous to
the drinking decision. The analysis was also conducted
excluding marital status from the drinking choice and
wage equations: all of the results reported were not
sensitive to the treatment of marital status.
86
ECONOMIC RECORD
MARCH
TABLE 1
Descriptive statistics
Variable
Ln Earnings
Age
Age
Age
Age
Age
Age
Age
25–29*
30–34
35–39
40–44
45–49
50–54
55–59
Not complete high school
Complete high school*
Trade qualification
Post-secondary certificate
University degree
Abstainer
Drinker-type
Moderate
Heavy
10.1812
10.2982
10.2571
0.1601
0.1676
0.1754
0.1536
0.1527
0.1096
0.0811
0.1860
0.2006
0.1668
0.1579
0.1292
0.0905
0.0690
0.3243
0.2360
0.1669
0.1148
0.0842
0.0447
0.0291
0.1085
0.3463
0.2610
0.1754
0.1088
0.0807
0.3282
0.2958
0.1604
0.1349
0.0586
0.3970
0.3308
0.1474
0.0662
Married
0.7530
0.8179
0.6610
NSW*
Vic
Qld
SA
WA
Tas
NT
ACT
0.3541
0.3024
0.1581
0.0730
0.0643
0.0275
0.0126
0.0080
0.3379
0.2671
0.1573
0.0933
0.0928
0.0286
0.0071
0.0159
0.3686
0.2290
0.1957
0.0762
0.0742
0.0231
0.0197
0.0135
Primary
Manufacturing*
Utilities
Construction
Trade
Transportation
Communications
Finance
Public Administration
Community Services
Recreation
0.0496
0.3063
0.0273
0.0727
0.1257
0.0909
0.0466
0.0523
0.0742
0.1261
0.0283
0.0539
0.2532
0.0371
0.0815
0.1416
0.0799
0.0375
0.0746
0.0747
0.1293
0.0368
0.0694
0.2145
0.0310
0.1141
0.1566
0.0791
0.0417
0.0816
0.0795
0.0864
0.0460
Administration
Professional
Para-professional
Tradesperson
Clerical
Sales and personal services
Plant operator
Labourer*
0.0560
0.1184
0.0671
0.2379
0.0878
0.0351
0.1773
0.2205
0.1044
0.1356
0.0862
0.2422
0.0805
0.0744
0.1292
0.1475
0.0917
0.0768
0.0786
0.2616
0.0841
0.0783
0.1558
0.1731
Public Sector
0.3491
0.3344
0.2834
Local abstainers
Local moderate drinkers*
Local heavy drinkers
0.1409
0.6644
0.1947
0.1364
0.6670
0.1966
0.1336
0.6571
0.2092
Smoke18
0.3544
0.4416
0.6199
777
3824
1104
Observations
Note: An * indicates the omitted category in the estimation of the earnings equations.
2002
ALCOHOL CONSUMPTION AND EARNINGS
IV Empirical Results
(i) Drinking Status Choice Model Estimates
The estimates for the drinking status choice
model are presented in Table 2. To aid interpretation, the marginal effect of the covariates on the
predicted probability of each drinker-type are also
presented in Table 2. The estimates show that age is
strongly related to the choice of drinking status.
Older men are significantly more likely to be nondrinkers, with individuals in the 45–54 age groups
having the highest probability of abstaining. Similarly, older men are progressively less likely to be
heavy drinkers. The relationship between age and
drinking status reflects an important life-cycle
pattern of drinking behaviour – young men, other
things equal, are more likely to drink heavily and
that likelihood decreases as they age.
Education does not appear to be strongly
related to drinker-type, except that individuals
with a university degree are significantly less likely
to be heavy drinkers. Being married is associated
with a significantly greater probably of being a
moderate drinker, and lower probabilities of being
either a non-drinker or heavy drinker. Employment in the utility (electricity, water and gas)
supply industries is associated with a significantly
lower probability of being a non-drinker while
working in communications, finance and public
administration is associated with a greater probability of being a heavy drinker. There is a strong
relationship between occupation of employment
and drinking status. Individuals in the omitted
occupation of manual labour, other things equal,
have the highest probability of being non-drinkers
while individuals in white collar occupations are
most likely to be moderate drinkers. Other things
equal, public sector employees are significantly less
likely to be heavy drinkers compared to their
private sector counterparts.
Included in the drinking status choice model is
the smoked at age 18 indicator variable. The coefficient estimates are large and precisely estimated
(with z-statistics of 5.306 and 9.891 in the nondrinker and heavy-drinker equations respectively).28
28
The likelihood ratio test statistic for the null
hypothesis that coefficients on the smoked at age 18
dummy are jointly equal to 0 is 155.5, which is
distributed chi-squared with two degrees of freedom.
The critical value at the 1 per cent level of significance
is 9.21 and hence the hypothesis is decisively rejected.
87
Individuals who smoked at age 18 are substantially less likely to be non-drinkers and much
more likely to be heavy drinkers, which is
consistent with this variable proxying an individual’s attitude toward risk.
The drinking status choice model also
included the proportion of the population in
the individual’s local area who were abstainers
and who were heavy drinkers. Living in an
area with a higher concentration of heavy
drinkers is associated with a lower probability
that the individual abstained and an increased
probability of heavy drinking, which is consistent with a demonstration effect of heavy
drinking within an individuals local area. The
coefficient on the proportion of the local
population who were abstainers was individually insignificant. However, the hypothesis that
the two measures of neighbourhood drinking
behaviour are jointly insignificant is strongly
rejected.29 Both the set of variables measuring
neighbourhood drinking behaviour and the
smoked at age 18 dummy variable are highly
significant and represent potentially strong
instruments for identifying the effect of drinking
status on earnings.
(ii) Grouped Earnings Regression Estimates
The grouped regression model for earnings
was first estimated using the pooled sample with
separate dummy variables for abstainers and
heavy drinkers included as control variables and
assuming exogenous selection. The estimates
show that after controlling for differences in
age, human capital and other demographic and
job characteristics, non-drinkers earn 5.9 per cent
less,30 and heavy drinkers 1.6 per cent more,31
than moderate drinkers. These results replicate
the findings of Berger and Leigh (1988) and
Zarkin et al. (1998) who found no evidence of a
wage penalty for the highest levels of alcohol
consumption. However, the higher earnings
associated with greater levels of drinking may
reflect the reverse effect of income on alcohol
29
The likelihood ratio test statistic is 19.80. The
critical value of the test statistic at the 1 per cent level
of significance is 13.28.
30
The coefficient on the non-drinker dummy variable
is significant at the 1 per cent level.
31
The coefficient on the heavy-drinker dummy is only
significant at the 15 per cent level.
88
ECONOMIC RECORD
MARCH
TABLE 2
Multinomial Logit drinking status choice model
Model estimates1
Variable
Abstainers
Coefficient
Std.err.
Constant
)0.3776
(0.3516)
)0.7349**
(0.3072)
Age 30–34
Age 35–39
Age 40–44
Age 45–49
Age 50–54
Age 55–59
Not complete high sch.
Trade qualification
Post-secondary cert.
University degree
Married
Vic
Qld
SA
WA
Tas
ACT
NT
Primary
Utilities
Construction
Trade
Transportation
Communications
Finance
Public administration
Community Services
Recreation
Administration
Professional
Para-professional
Tradesperson
Clerical
Sales and personal serv.
Plant operator
Public Sector
Smoked at age 18
Local abstainers
Local heavy drinkers
0.0705
0.3330*
0.2533
0.4496**
0.4333**
0.3408
0.0422
0.1426
)0.1963
)0.1170
)0.4119**
0.0238
0.0066
)0.3407*
)0.4139*
)0.1181
)0.5491
0.8353*
)0.2559
)0.5913*
)0.2611
0.0208
)0.1561
)0.0073
)0.3498
)0.2751
)0.1679
)0.2855
)1.0971**
)0.6078**
)0.7459**
)0.3401**
)0.4143**
)1.2142**
)0.0811
0.1931
)0.4521**
)0.3681
)1.5895
(0.1394)
(0.1404)
(0.1461)
(0.1479)
(0.1635)
(0.1803)
(0.1516)
(0.1243)
(0.1155)
(0.1746)
(0.0983)
(0.1042)
(0.1244)
(0.1700)
(0.1809)
(0.2528)
(0.4402)
(0.4211)
(0.1972)
(0.2702)
(0.1660)
(0.1419)
(0.1655)
(0.2360)
(0.1962)
(0.2059)
(0.1747)
(0.2487)
(0.1932)
(0.1878)‘
(0.1872)
(0.1321)
(0.1735)
(0.2323)
(0.1344)
(0.1276)
(0.0852)
(1.2052)
(0.9005)
)0.2981**
)0.4387**
)0.7015**
)0.9015**
)1.1861**
)1.3421**
)0.1407
)0.0507
0.0677
)0.5438**
)0.7103**
)0.1929*
)0.0134
)0.3103*
)0.4070**
)0.2340
)0.4154
0.5518
0.2123
0.3775**
0.3911**
0.2078
0.1585
0.4969*
0.4540**
0.5271**
0.2353
0.1801
0.0195
)0.1872
)0.0236
)0.0833
0.0367
)0.0986
0.1486
)0.2469*
0.7328**
)1.4717
1.8141*
(0.1010)
(0.1109)
(0.1230)
(0.1360)
(0.1707)
(0.2039)
(0.1582)
(0.1124)
(0.0964)
(0.1687)
(0.0818)
(0.0979)
(0.1027)
(0.1459)
(0.1526)
(0.2360)
(0.3152)
(0.3353)
(0.1613)
(0.2340)
(0.1350)
(0.1258)
(0.1557)
(0.2207)
(0.1559)
(0.1889)
(0.1711)
(0.1907)
(0.1524)
(0.1810)
(0.1643)
(0.1217)
(0.1601)
(0.1668)
(0.1291)
(0.1209)
(0.0741)
(1.0690)
(0.7558)
LLF
Heavy drinkers
Coefficient Std.err.
Marginal effect on
probabilities2
Moderate
Heavy
Abstainers drinkers
drinkers
Predicted Probability
0.2332
0.4907
0.2761
0.0314
0.0941
0.0905
0.1443
0.1516
0.1349
0.0166
0.0299
)0.0372
0.0085
)0.0354
0.0163
0.0020
)0.0401
)0.0472
)0.0071
)0.0664
0.1234
)0.0549
)0.1069
)0.0667
)0.0104
)0.0364
)0.0363
)0.0824
)0.0772
)0.0431
)0.0573
)0.1416
)0.0832
)0.1062
)0.0509
)0.0677
)0.1477
)0.0236
0.0527
)0.1109
)0.0090
)0.2168
Marginal Effect
0.0281
0.0029
0.0381
0.0160
0.0343
0.0602
0.0132
)0.0106
0.0111
0.0809
0.1377
0.0220
0.0010
0.0805
0.1015
0.0448
0.1169
)0.1652
)0.0061
)0.0108
)0.0357
)0.0315
)0.0066
)0.0734
)0.0404
)0.0586
)0.0176
0.0017
0.0865
0.0889
0.0727
0.0481
0.0362
0.1150
)0.0125
0.0053
)0.0864
0.2091
)0.0745
)0.0595
)0.0970
)0.1286
)0.1604
)0.1859
)0.1951
)0.0298
)0.0193
0.0261
)0.0894
)0.1023
)0.0382
)0.0031
)0.0404
)0.0543
)0.0377
)0.0504
0.0419
0.0610
0.1178
0.1024
0.0419
0.0430
0.1098
0.1228
0.1358
0.0607
0.0556
0.0551
)0.0057
0.0335
0.0028
0.0315
0.0327
0.0361
)0.0581
0.1973
)0.2002
0.2913
)4596.43
Note 1. * and ** denote significance at the 5 percent and 1 percent levels, respectively.
2. The reference group (omitted category) for the estimation and calculation of the marginal effects corresponds to males aged
25–29 years, who had completed high school, were single, resided in NSW, were employed in the private sector, in
manufacturing as a labourer, did not smoke prior to age 18 and the proportion of abstainers (moderate) and heavy drinkers in
the local area corresponded to the sample average.
2002
ALCOHOL CONSUMPTION AND EARNINGS
consumption or unmeasured worker characteristics correlated with drinking status. The objective
of the analysis is to test whether the earnings
premium for drinkers persists after controlling
for the endogenous relationship between alcohol
consumption and earnings.
The grouped regression models where then
estimated separately by drinker type allowing for
self selection of drinking status. The estimation
results for the separate earnings equations are
presented in Table 3.32 These models allow the
returns to age, human capital characteristics and
job attributes to differ by drinker type. The
likelihood ratio test rejects the restrictions imposed by pooling across all drinker types in
support of the models where all the coefficients
are allowed to vary by drinking status.33 The
age-earnings profiles for each drinker type generally have the usual concave shape, although the
profile for non-drinkers is relatively flat and not
precisely estimated. The age-earnings profile is
steepest for heavy drinkers, peaking at age
30–34 years and declining steeply over older age
intervals. The age-earnings profile is smoother
for moderate drinkers, with a lower peak occurring at the 45–49 year age bracket, and a slower
decline over later years. The returns to education
are relatively uniform across the drinker-types,
with a significant earnings premium for having a
university degree for all drinker types. There is
also a uniform, positive marriage premium for
each drinker type.
The lower panel of Table 3 reports the
returns to job characteristics by drinking status. The coefficients on the industry, occupation and public sector dummy variables may
capture the returns to industry and occupational specific human capital or perhaps job
rents. There are significant differences in earnings across industries for each drinker type.
32
Wald tests, based on pairwise comparisons of the
coefficients estimates in Table 3 (excluding the intercept, the coefficient on the selection term and the standard deviation of the residual), reject the hypothesis that
the returns to individual characteristics and job attributes are equal for any two drinker types. The P-values
for the test statistics are all less than 1 per cent.
33
The test statistic for null hypothesis that the
returns to the demographic, human capital and job
characteristics are the same for the three drinker types
is 130.75 (P-value ¼ 0.000 17). The test statistic is
distributed chi-squared with 78 degrees of freedom.
89
However, the differences by occupation are
more economically important. Indeed, the
coefficients on the occupation dummy variables
are almost an order of magnitude greater than
the coefficients on the industry dummies. The
general pattern of occupational earnings differentials are consistent across the three drinker
types; administrative and professional occupations receive the highest earnings, and clerical
and service occupations pay higher earnings
than the blue-collar jobs. The magnitude of the
occupational earnings differences are substantially lower among non-drinkers while the
returns to white-collar occupations are greatest
for moderate drinkers. After conditioning on
other observable factors, non-drinkers receive a
substantial public sector earnings premium
while heavy drinkers receive a large public
sector earnings penalty.
The final set of controls are the state dummy
variables that capture differences in earnings across
the states of Australia. The earnings differentials by
state are less pronounced among the abstainers.
For moderate and heavy drinkers, Queensland and
South Australia workers earn less than their
counterparts in New South Wales.
A noticeable finding is that differences in
returns to observable characteristics are generally
less pronounced among non-drinkers. This is
consistent with non-drinkers being risk-averse
and tending to work in jobs where there is less
variation in compensation by personal characteristics or job attributes. The lower average
earnings of non-drinkers may partly reflect the
payment of an insurance premium by nondrinkers.
The earnings equations included the SCT
which correct for endogeneity bias arising from
individuals choosing their drinking status. The
coefficients on SCT in the non-drinker and
moderate drinker earnings equations are statistically insignificant.34 The coefficient on SCT in
the heavy drinker earnings equation is significant35 and indicates that workers who self select
34
The P-value for the coefficient on the SCT for nondrinkers is 0.236 and for moderate drinkers is 0.705.
Note the equations are consistently estimated even if
the coefficients on the selectivity correction terms are
insignificant.
35
The P-value for the coefficient on the selection
correct term for heavy drinkers is 0.029.
90
ECONOMIC RECORD
MARCH
TABLE 3
Earnings equation estimates with selectivity corrections
Variable
Constant
SCT
Non-Drinkers
Coefficient
Std.err.
9.7399
(0.1332)
Moderate Drinkers
Coefficient
Std.err.
9.9470
Heavy Drinkers
Coefficient
Std.err.
(0.0761)
9.7885
(0.0643)
)0.1072
(0.0905)
0.0321
(0.0847)
)0.1184
(0.0541)
0.0283
0.0772
0.0051
0.0512
)0.0891
)0.0457
(0.0421)
(0.0450)
(0.0469)
(0.0497)
(0.0533)
(0.0575)
0.0712
0.0782
0.1015
0.1188
0.0921
0.0350
(0.0165)
(0.0172)
(0.0192)
(0.0201)
(0.0235)
(0.0267)
0.0347
0.1231
0.0707
0.0584
0.0882
)0.0524
(0.0270)
(0.0311)
(0.0383)
(0.0437)
(0.0563)
(0.0668)
Not complete high sch.
Post-secondary cert.
Trade qualification
University degree
)0.0838
0.0985
0.0952
0.2806
(0.0443)
(0.0373)
(0.0350)
(0.0538)
)0.0668
0.1056
0.0633
0.2345
(0.0203)
(0.0156)
(0.0140)
(0.0223)
)0.0436
0.1385
0.0754
0.2288
(0.0438)
(0.0305)
(0.0260)
(0.0496)
Married
Vic
Qld
SA
WA
Tas
NT
ACT
0.0541
0.0242
)0.0279
0.0288
0.0190
)0.0291
)0.0438
0.0902
(0.0303)
(0.0298)
(0.0365)
(0.0497)
(0.0525)
(0.0739)
(0.1102)
(0.1364)
0.0562
0.0029
)0.0743
)0.0607
0.0371
)0.0112
0.0570
0.0198
(0.0209)
(0.0131)
(0.0150)
(0.0197)
(0.0206)
(0.0306)
(0.0626)
(0.0408)
0.0451
)0.0384
)0.0555
)0.0970
)0.0193
)0.0245
0.1490
0.0233
(0.0249)
(0.0265)
(0.0268)
(0.0390)
(0.0393)
(0.0648)
(0.0712)
(0.0854)
Primary
Utilities
Construction
Trade
Transportation
Communications
Finance
Public administration
Community services
Recreation
0.0991
0.0765
0.0969
)0.0697
)0.0584
)0.1526
0.0764
)0.0973
)0.1172
0.0036
(0.0621)
(0.0859)
(0.0521)
(0.0421)
(0.0499)
(0.0711)
(0.0624)
(0.0639)
(0.0513)
(0.0772)
0.0815
0.0498
0.0821
)0.0619
0.0708
)0.0079
0.0910
)0.0411
)0.0830
)0.1183
(0.0237)
(0.0306)
(0.0202)
(0.0178)
(0.0212)
(0.0314)
(0.0220)
(0.0260)
(0.0217)
(0.0279)
0.0680
0.0634
0.1321
)0.0282
0.1246
0.1424
0.0978
0.1190
0.0677
)0.0619
(0.0429)
(0.0634)
(0.0371)
(0.0344)
(0.0425)
(0.0617)
(0.0427)
(0.0525)
(0.0485)
(0.0510)
Administration
Professional
Para-professional
Tradesperson
Clerical
Sales and pers. services
Plant operator
0.3747
0.2829
0.1925
0.0740
0.1977
0.2225
0.1531
(0.0728)
(0.0599)
(0.0622)
(0.0401)
(0.0548)
(0.0842)
(0.0391)
0.4818
0.3752
0.3212
0.1277
0.2118
0.2199
0.1043
(0.0242)
(0.0252)
(0.0246)
(0.0184)
(0.0231)
(0.0283)
(0.0190)
0.4420
0.2767
0.3243
0.1606
0.2069
0.2101
0.1470
(0.0422)
(0.0506)
(0.0442)
(0.0325)
(0.0427)
(0.0447)
(0.0345)
Public Sector
0.1025
(0.0382)
)0.0475
(0.0160)
)0.1366
(0.0341)
0.3188
(0.0086)
0.2941
(0.0036)
0.3063
(0.0070)
Age
Age
Age
Age
Age
Age
30–34
35–39
40–44
45–49
50–54
55–59
Sigma
LLF
)1450.24
)6973.03
Note: The standard errors are calculated using White’s (1980) method.
)2054.36
2002
ALCOHOL CONSUMPTION AND EARNINGS
into heavy drinking earn 15.12 per cent more on
average than what an individual with identical
observable characteristics drawn at random from
the workforce would earn as a heavy drinker.36
This positive selection into heavy drinking
reveals that individuals with unobserved characteristics associated with a higher probability of
heavy drinking are also associated with higher
earnings as a heavy drinker. This suggests that,
other things equal, individuals with a stronger
preference for high levels of alcohol consumption
also tend to be individuals who are better able to
handle the health effects of heavy drinking
thereby minimising the apparent adverse effects
of heavy drinking on earnings.
The earnings equations were also estimated
separately assuming exogenous selection.37 The
exclusion of SCT from the non- and moderate
drinkers earnings equations affected the estimate
of the constant terms and had little affect on the
estimated returns to personal or job characteristics. However, the exclusion of the SCT
from the heavy drinker earnings equation also
affected the coefficient estimates for age. Specifically, the definitive early peak and concavity of
the heavy drinking age-earnings profile in
Table 3 was masked in this model (due to the
correlation between age and selection into heavy
drinking).
Overall, important differences in the returns to
demographic, human capital and job characteristics by drinker type were found. However, it is
not clear from the coefficient estimates what
effect the differences in returns imply for earnings
differentials. In order to draw out these effects,
earnings decompositions are presented next.
(iii) Earnings Differentials
The grouped regression results in Table 3 are
used to decompose the predicted earnings differentials between drinker types. Following the
empirical literature on wage differentials, two
earnings decompositions are considered. The first
is the unconditional earnings differential which is
36
The selection effect is calculated as the selection
coefficient, -ðrj qj Þ, multiplied by the mean of the
selectivity correction term, /j ð:Þ=Fj ð:Þ.
37
Due to space constraints the detailed estimation
results for this specification of the earnings equations
are not presented, but are available on request from the
author.
91
calculated by the following extension of Oaxaca’s
method (Oaxaca 1973):
E½ln wj jxj E½ln wk jxk
bj þ bk
xj þ xk
¼ ðxj xk Þð
Þ
Þ þ ðbj bk Þð
2
2
ð6Þ
The unconditional earnings differential measures the difference in earnings between two
workers who have observable characteristics
identical to the average person of each drinker
type. This earnings differential is unconditional
in that the predicted earnings are calculated
independently of the worker’s actual choice of
drinking status, and hence the earnings differences are independent of selection effects
(equivalently, the effects of unobservables).
The earnings differential may be divided into
a component due to differences in characteristics between the two drinker-types (corresponding to the first term on the right-hand side of
(6)) and a component due to differences in the
labour market returns to those observable
characteristics (corresponding to the second
term on the right-hand side of (6)).38 It is this
latter component of the unconditional earnings
differential which isolates the effect of alcohol
consumption on earnings.
The second earnings differential of interest is
the conditional earnings differential which is
calculated as:
bj þ bk
Þ
2
/j
xj þ xk
/
þ ðbj bk Þð
Þ ðrj qj rk qk k Þ
Fk
2
Fj
E½ln wj jxj E½ln wk jxk ¼ ðxj xk Þð
ð7Þ
For this decomposition earnings are predicted
taking into account the self-selection by workers
of their preferred drinking status, and hence
expected earnings includes the product of the
mean of the unobserved characteristics and the
returns to those unobserved characteristics. This
38
In the decomposition presented in equation (6) the
differences in mean characteristics are weighted by the
arithmetic average of the returns to the two drinkertype, and the differences in returns are similarly
weighted by the arithmetic average of the characteristics of the two drinker-types. This is only one of the
many possible weighting schemes. The results of the
wage decompositions are not sensitive to the reference
group/weighting scheme adopted.
92
MARCH
ECONOMIC RECORD
TABLE 4
Earnings decompositions (and standard errors)
Wa – Wm
Wh – Wm
Wa – Wh
1. Endogenous selection: Unconditional earnings decompositions
Total difference
)0.2893 (0.0050)
)0.2049 (0.0022)
Total characteristics
)0.0481 (0.0002)
)0.0415 (0.0002)
Total returns
)0.2413 (0.0052)
)0.1634 (0.0024)
)0.0844 (0.0054)
)0.0118 (0.0003)
)0.0726 (0.0056)
2. Endogenous selection: Conditional earnings decompositions
Total difference
)0.1085 (0.0004)
Total characteristics
)0.0481 (0.0002)
Total returns
)0.2413 (0.0052)
Total selection
0.1808 (0.0050)
)0.0717
)0.0118
)0.0726
0.0127
)0.0368
)0.0415
)0.1634
0.1681
(0.0003)
(0.0002)
(0.0024)
(0.0022)
3. Preferred specification: Unconditional earnings decompositions
Total difference
)0.1086 (0.0117)
)0.1880 (0.0002)
Total characteristics
)0.0536 (0.0029)
)0.0428 (0.0002)
Total returns
)0.0549 (0.0120)
)0.1452 (0.0022)
(0.0004)
(0.0003)
(0.0056)
(0.0054)
0.0795 (0.0118)
)0.0180 (0.0044)
0.0975 (0.0127)
Notes: 1. Asymptotic standard errors are in parentheses.
2. The preferred specification corresponds to the earnings models based on exogenous selection into the nondrinker and moderate
drinker groups, and endogenous selection into the heavy drinker group.
method decomposes the earnings differences
observed in the raw data and includes an
additional component due to differences in
worker self-selection of drinking status (the third
term on the right-hand side of (7)).39
An additional earnings differential is also
calculated. The SCTs were insignificant in the
earnings equations for non-drinkers and moderate drinkers. Inclusion of the SCT in these two
models mainly affected the estimate of the
constant term which is reflected in the component of the earnings differentials (involving these
two drinker types) due to differences in returns.
Because the data supported exogenous selection
for the non-drinker and moderate drinker earnings equations, the unconditional earnings differentials are recalculated using the estimates based
on exogenous selection for these two groups.
Because the data also supported the hypothesis
of endogenous selection for heavy drinkers, the
estimates of the heavy drinker earnings equation
in Table 3 are used. These specifications of the
earnings equations are referred to as the Ôpreferred specificationsÕ.
39
Papers which discuss the interpretation and merits
of unconditional and conditional wage decompositions
include Duncan and Leigh (1980), Trost and Lee
(1984), Gyourko and Tracy (1988) and Idson and
Feaster (1990).
The results of the earnings decompositions
(and asymptotic standard errors40) are reported
in Table 4. The unconditional earnings decompositions reveal a large earnings premium for
moderate drinkers relative to both abstainers and
heavy drinkers.41 The earnings differentials, and
each component, are statistically significant.The
predominant proportion of the earnings premium
is due to differences in the labour market reward
to worker characteristics, which is consistent
with the hypothesis that moderate drinking is
associated with better health and hence greater
worker productivity and earnings. However,
heavy drinking is found to yield an earnings
premium over abstaining, mostly due to differences in returns, which is not consistent with the
health and productivity-earnings hypothesis.
It is possible to break down the contribution
of overall differences in characteristics into differences in specific sets of worker characteristics.
40
The asymptotic standard errors are calculated
using the delta method: Rao (1973, 388).
41
Although the unconditional wage differentials are
quite large, they are smaller than the 45 per cent
unconditional wage differential between drinkers and
non-drinkers found by Berger and Leigh (1988) and the
77 per cent unconditional wage differential between
moderate and heavy drinkers implied by the results in
Hamilton and Hamilton (1997: 147).
2002
ALCOHOL CONSUMPTION AND EARNINGS
Although not reported in Table 4, disaggregating
the differences in characteristics reveals that
differences in the distribution of workers across
occupations is by far the most important factor
contributing to the differences due to characteristics. Even so, the aggregate effect of differences
in characteristics is minor compared to differences in returns.42
The second panel of Table 4 reports the
conditional earnings decompositions. The conditional earnings differences across drinker-types
are much smaller than the unconditional earnings differences implying that selection bias
substantially masks the earnings effects of alcohol consumption. This is confirmed in the
bottom line of the panel, where the total effect
of worker self-selection substantially decrease the
observed earnings differential between moderate
drinkers and the other two drinker-types. The
direction of these selection effects reinforce each
other in reducing the observed earnings differential between moderate drinkers and both abstainers and heavy drinkers. However, the
selection bias effects for abstainers and heavy
drinkers counteract each other, accounting for
the smaller contribution of the total selection
effect to the conditional earnings difference
between abstainers and heavy drinkers.
The bottom panel of Table 4 reports the
unconditional earnings decomposition based on
the preferred specification of the earnings equations. The earnings premium for moderate
drinkers over abstainers is approximately
11 per cent, half of which is due to differences
in returns and hence may be attributed to the
health and productivity enhancing effects of
moderate drinking. A much larger earnings
penalty for heavy drinkers relative to moderate
drinkers (19 per cent) and a significant earnings
penalty for heavy drinking relative to abstention
(8 per cent) is found. These earnings penalties
for heavy drinkers are predominately due to
differences in returns rather than worker characteristics. The latter finding of an earnings
penalty for heavy drinking relative to abstention
42
Jones (1983) showed that the disaggregation of the
wage differential due to differences in specific sets of
coefficients will be sensitive to the reference category
chosen for the dummy variables. Given the inherent
arbitrariness of such a disaggregation, it is not
performed here.
93
is consistent with the health and productivity
implications of heavy drinking. The counterintuitive earnings premium for heavy drinking
over abstention found for the unconditional
earnings differential in the top panel of Table 4
reflects the sensitivity of the estimate of the
constant term in the earnings equation for nondrinkers when the (insignificant) SCT is included
in that model. Overall, the earnings decompositions do reveal a significant earnings premium
to moderate drinking, and a significant earnings
penalty to heavy drinking.
(iv) Specification Tests
The results in Tables 3 and 4 show that there
is a positive labour market return to moderate
drinking and a penalty to heavy drinking.
Because the magnitude of these effects were
masked by selection effects in the raw data, the
sensitivity of the findings to the identification of
the drinking status selection model was examined. The identification of the drinking choice
equation was based on two sets of variables.
Because only one instrument is required to
formally identify the drinking choice model,
the model is overidentified and hence it is
possible to test the validity of each instrument
separately. Under the maintained assumption
that the proportion of the local population who
are abstainers and who are heavy drinkers are
validly excluded from the earnings equation, the
validity of the smoked at age 18 dummy
variable as an instrument for drinking status
can be tested. The earnings equations were
estimated with the smoked at age 18 variable
included43 and Durbin-Wu-Hausman (DWH)
specification tests were used to test the validity
of their exclusion. The DWH test statistics of
0.102, 0.009 and 1.496 for the non-, moderate
and heavy drinking earnings equations, respectively, were well below the chi-squared critical
values at conventional levels of significance44
43
The point estimates (and asymptotic standard
errors) for the smoked at age 18 dummy variable were
0.0098 (0.0358), 0.0046 (0.0109) and -0.0366 (0.0301)
for the non, moderate and heavy drinker earnings
equations respectively.
44
The test statistics have a chi-squared distribution
with 1 degree of freedom, and the critical value at the
1 per cent level of significance is 6.635 and at the
10 per cent level is 2.706.
94
ECONOMIC RECORD
supporting the exclusion of the smoked at age 18
dummy variable from the earnings equations.45
The validity of the instrument measuring
drinking patterns in the individual’s local area
was also tested. Assuming that the smoked at age
18 variable is a valid instrument, the earnings
equations were estimated with the proportion of
the local population who abstained and who
drank heavily included as explanatory variables.46 The DWH test statistics for the null
hypothesis that these variables are validly excluded from the earnings equations were 0.575,
3.471 and 1.676 for the non-, moderate and
heavy drinking earnings equations, respectively.
These test statistics were well below the chisquared critical values at conventional levels of
significance47 supporting the validity of these
instruments.
Alternative instruments for identifying the
drinking status choice equation were also examined. The NHS89 records whether an individual
had private health insurance, which may also
proxy an individual’s attitude toward risk. The
likelihood ratio test failed to reject the hypothesis
that the coefficients on the private health insurance variable in the drinking status choice
equations were jointly equal to zero. Conse-
45
The selection model and earnings equations were
also estimated with the inclusion of an indicator
variable for current smoking status. With this specification the validity of the smoked at age 18 variable as
an instrument for drinking behaviour status was
supported by DWH specification tests, and the same
pattern of wage differentials by drinker type (as
discussed in the text) was evident. Therefore it may
be concluded that the smoked at age 18 dummy
variable is not capturing current smoking status. The
models which include a dummy variable indicating
current smoking status are not presented in the main
set of results due to the potential endogeneity of this
variable.
46
The point estimates for the two variables were
individually statistically insignificant in all the wage
equations except for the estimated coefficient on the
proportion of abstainers in the local area in the wage
equation for moderate drinkers. In that model, the
point estimate (and standard error) for the proportion
of abstainers in the local area was )0.653 (0.511),
which has a P-value of 0.100.
47
The test statistics have a chi-squared distribution
with two degrees of freedom, and the critical value at
the 1 per cent level of significance is 9.210 and at the
10 per cent level is 4.605.
MARCH
quently, this variable is not used because it is at
best a weak instrument.48 The NHS89 contains
detailed information on additional health risk
behaviours such as whether the respondent is
under/overweight and whether the respondent
exercised regularly. Inclusion of these variables in
the drinking status choice model magnified the
estimated selection effects found in Table 3 and
increased the estimated moderate drinking earnings premium. However, because these measures
are contemporaneous with earnings, they are not
included in the preferred specification since they
are potentially correlated with the error term in
the earnings equations.
The models were also estimated using alternative definitions of drinking status. Following
Kenkel and Ribar (1994), a lower threshold for
heavy drinking at six drinks on any one day
during the reference week was considered. This
led to a reclassification of 572 observations from
the moderate to heavy drinking category. The
model estimates and earnings differentials were
very similar to those reported in Tables 3 and 4.
The NHMRC (1992) definition of drinker
types was also used. The NHMRC categories
for medium and high risk drinking require
frequent intense drinking. This led to a substantial reclassification of the sample, with 84 per
cent classified as low risk drinkers, 10 per cent
as medium risk and 6 per cent as high risk
drinkers. The sample means indicate that low
risk drinkers earn 1.5 per cent less, and high risk
drinkers 11.5 per cent less, than medium risk
drinkers. After controlling for individual and job
characteristics and self selection of drinking
status, the unconditional earnings differentials
indicate a statistically insignificant earnings premium for low risk relative to medium risk
drinking of 10 per cent, and an insignificant
earnings penalty of 2 per cent for high risk
relative to medium risk drinking. These findings
may be reconciled with the results in Tables 3
and 4. The NHMRC low risk drinker group
approximates the combined non-drinker-moderate drinker groups used above, and the NHMRC
demarcation between medium and high risk
drinking splits the heavy drinker group in two
48
In addition, this is a contemporaneous variable
that is highly correlated with current earnings: see
Cameron and Trivedi (1991) and Barrett and Conlon
(2000).
2002
ALCOHOL CONSUMPTION AND EARNINGS
based on the average intensity of drinking. The
results using the NHMRC classification are then
consistent with a 10–12 per cent earnings penalty
for ‘harmful’ or ‘hazardous’ drinking.
A distinctive feature of the NHS89 is the
detailed information on job attributes, which
were found to be important determinants of
earnings. To gauge the consequences of not
controlling for these characteristics, the selection
model and earnings equations were re-estimated
excluding the industry, occupation and public
sector dummy variables. The selection effects
were still well identified, however, the coefficients
on the educational attainment variables in the
moderate and, to a lesser extent, heavy drinking
earnings equations were substantially larger. This
result is due to the correlation between educational attainment and occupational status. Therefore, the large differences in the returns to
education by drinking status reported by Hamilton and Hamilton (1997) may be overstated due
to the omission of controls for job attributes,
particularly occupation.
V Conclusion
This paper examined the effect of alcohol
consumption on individual earnings. Treating
drinking status as exogenous, and entering separate dummy variables indicating abstention and
heavy drinking into a standard earnings equation, indicated that earnings increased with
greater levels of alcohol consumption. However,
allowing for the endogeneity of drinking status
due to the effects of income and unobservables
on alcohol consumption levels revealed a significant positive earnings premium for moderate
drinking and a substantial penalty for heavy
drinking. These effects of alcohol on earnings
were masked in the raw data by worker selfselection of drinking status. Estimates of the
selectivity corrected earnings functions were
consistent with the health-productivity-earnings
nexus suggested by the medical literature on the
effects of alcohol consumption patterns.
There are several directions in which it may be
fruitful to extend this research. The present
analysis has focussed on the earnings effects of
drinking among a sample of full-time male
workers. Alcohol consumption may have consequences for individuals’ labour supply which
could be important in assessing the private and
social costs of different drinking patterns. It
would also be useful to analyse the effects of
drinking behaviour on the earnings of women.
95
Furthermore, the results of this analysis suggest
that it may be interesting to explore the consequences of health risk behaviours more generally,
in addition to the consumption of legal and illicit
drugs, for labour market outcomes.
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