Discussion Paper No.

581

AN EMPIRICAL STUDY OF
ALCOHOLIC CONSUMPTION AND
LABOR PRODUCTIVITY IN JAPANMasayo Satoand
Yasushi Ohkusa

May 2003

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Discussion Paper No.

581

AN EMPIRICAL STUDY OF
ALCOHOLIC CONSUMPTION AND
LABOR PRODUCTIVITY IN JAPANMasayo Satoand
Yasushi Ohkusa

May 2003

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

An Empirical Study of Alcoholic Consumption
and Labor Productivity in Japan
Masayo Sato
National Institute of Population and Social Security ResearchandYasushi Ohkusa
Institute of Social and Economic Research, Osaka University

JEL Classifications:I12,I18,J31
Keywords: Alcoholic Consumption, Labor Productivity, Addiction, Income Effect, Health Promotion Policy, Health Japan 21, Policy Evaluation
Correspondence: Yasushi Ohkusa, ISER , Osaka University, 6-1 Mihogaoka Ibaraki
Osaka, Japan
Tel:+81-6-6879-8566

Fax:+81-6-6878-2766e-mail:ohkusa@iser.osaka-u.ac.jp

Abstract
This paper examines the relationship between labor productivity and alcohol
consumption based on research conducted with a limited sample of workers who
drink alcohol. Estimation results show that in the case of males, the amountof alcohol consumed significantly raises labor productivity, with an elasticity of
approximately 0.13. In females, we cannot reach the firm conclusion. Conversely,
the reverse relationship between labor productivity and alcohol consumption cannot be confirmed. Moreover, an awareness of appropriate alcohol consumption
supports the sixth strategy of the Health Japan 21 policy, which is to reduce
national alcohol consumption by about 20%.

1

Introduction

The study of alcohol consumption is a contentious issue in the research field of
addictive behaviors other than smoking. The main problem is in the relationshipbetween labor supply and drinking behavior. There are currently a large numberof investigations into this problematic relationship in the United States.
Unfortunately, the results are mixed. That is, problem drinking does not seemto influence employment (Benham and Benham(1982), Kenkel and Ribar(1994),
Feng, Zhou, Butler, Booth and French(2001)). Regarding the eﬀect of problem
drinking on income, Berger and Leigh(1988), Bryant, Samaranayake and Whilhite(1993), French and Zarkin(1995), and Zarkin, French and Mroz et al.(1998)
found that drinkers earned more than non-drinkers. Conversely, Kenkel andRibar(1994), French and Zarkin(1995), and Mullahy and Sindelar(1993) foundthat heavy drinkers’ wages or income were lower than those of light drinkers.
Moreover, from the macroeconomic perspective, Ruhm(1995) and Freeman(1999)
confirmed that alcohol consumption moves pro-cyclically; i.e., it increases during
an economic boom. On the other hand, Thomas(2001) concluded that the stressfrom anxiety over being unemployed in a period of recession raises the levels of
alcohol consumption. The diﬀerent conclusions of these two research projects areprobably attributable to the type of data used. That is, the former used macro
data and the latter used micro data.
The relationship between labor productivity and drinking is emphasized in the
Health Japan 21 health promotion policy in Japan (The Committee for HealthJapan 21(2000)). It is similar to Healthy People 2010 in the United States. This
policy declares the following targets in a bid to reduce problem drinking:• a 20% reduction in the number of heavy drinkers,

1• the elimination of drinking among minors, and• the spread of public health knowledge about moderate and appropriate
drinking.
This paper evaluates the Health Japan 21 policy from the viewpoint of healtheconomics. Firstly, it checks the reliability of the relationship between laborproductivity and drinking. As mentioned above, it has been investigated in many
countries, but is yet to be studied in Japan. For our research, we focus on a sample
of workers who drink alcohol.
It is a truism to say that labor productivity and drinking are simultaneously
determined. That is, alcohol consumption may decrease (or rise) labor productivity ceteris paribus, but labor productivity, which is measured by wage or labor
income, raises alcohol consumption if it is a normal good. Hence, we have toestimate these relationships by using the simultaneous equation system.
Secondly, this paper checks the eﬀectiveness of the third point emphasizedin the policy targets. In other words, we investigate how the diﬀusion of suchknowledge reduces alcohol consumption. It is well known that knowledge of theharm of smoking does not aﬀect the behavior of smokers in Japan (Sato andOhkusa(2002)). Hence, it may also be the case that knowledge of moderate andappropriate drinking will not aﬀect drinking behavior. Incidentally, Health Japan
21 also declares its intention to spread the knowledge of harm from smoking asa policy target.

2

Data

The data were obtained from a survey conducted in May 2001 in Japan. Of
the total 1300 questionnaires distributed, 1024 were completed and returned.
Concerning alochol consumtion , the questionnaires ask how much they drink
2alochol in average by unit of pure alcohol. Other important information collecting
in this survey are as follows.
Knowledge of the harm of alcohol depends on the answer to the following
question:”How many drinks a day do you think is harmful for your health generallywith respect to sake? One drink of sake (180ml) is equivalent to a medium bottleof beer (500ml), double shots of whiskey or brandy (60ml), one drink (180ml) of
shochu of 35%, or one glass of wine (120ml).”
The level cited as harmful for health in Health Japan 21 is one drink of sake,
which is equivalent to 20g pure alcohol (Tsugane, Fahey, Sasaki, et al.(1999),
Holman, English, Milne, et al.(1996)).
Two levels of knowledge about the eﬀects of alcohol on health are defined
here. The first definition is rigorous; that is, only those who answer exactly one
drink of sake have the required knowledge. The second definition is somewhat
broad; that is, those who answer less than or equal to one drink of sake havethe required knowledge. Hereafter, we call the former definition rigorous and thelatter definition broad. The rigorous definition means that those who excessively
evaluate the harm of alcohol do not have the required knowledge, although thebroad definition includes them together with well-informed persons.
The death cause or morbidity of their parents are dichotomous variables andare defined to be one if they either died from or have liver cancer, hepatitis or
cirrhosis, and zero otherwise. Genetic characteristics which are not controlled byindividuals may be captured and thus make ideal instruments.
Following Barsky, Juster, Kimball and Shapiro (1997), we construct the variables for time discount factor and risk aversion as follows. The question for time
discount factor is: ”What amount of money will you gain in ten years that isequivalent to the one million yen you currently get? And the question for risk
3

aversion is: ”What amount of money will you gain with 50% probability andwith 50% probability you will not gain that is equivalent to the one million thatyou get now with certainty?” Let the response for the latter question be x andassume utility function be in the exponential form. In this case, the relationship
100α = 0.5xα holds, and this indicates α = log 2/(log x − log 100). Thus risk
aversion is defined by −u”(c)c/u0 (c) = 1 − α. Time discount factor is calculated
by 100α = β 10 y α and thus β = (100/y)α/10 where y is the response for the former
question.

3

Estimation Model

Let Ai be the level of alcohol consumption and Ii be the labor productivity whichis measured by labor income for the ith individual. Xi denotes independent
variables which explain both the alcohol consumption and labor productivity,
and Yi denotes instrument variables for alcohol consumption and that do not
explain the labor productivity. Conversely, Zi denotes instrument variables forlabor productivity and that do not aﬀect alcohol consumption. Thus, we estimate

log Ai = α + αX Xi + αI log Ii + αY Yi + εilog Ii = β + βX Xi + βA log Ai + βZ Zi + vi

(1)

by using a three stage least square method (hereafter, we refer to it as 3SLS).
In particular, we use the quadratic function of age, income other than labor income, educational attainment, time discount factor, risk aversion rate, andself-assessed health. The quadratic function of age and self-assessed health isoften used as an indicator for health condition and implies general human capitaland health capital, respectively, in labor productivity. Income other than labor
income determines marginal utility of income; i.e., the higher the other income,4the lower the marginal utility of income. This other income captures the pure income eﬀect excluding price eﬀect. Educational attainment implies general humancapital in labor productivity and level of knowledge about alcohol consumption.
According to the serial health capital research literature (Grossman(2001)), it
does imply eﬃciency in health investment and thus a highly educated person
would be expected to be more healthy ceteris paribus.
The instrument variables for alcohol consumption are knowledge about theharm of alcohol, cause of death or morbidity in their parents, and the durationof drinking.
According to the Rational Addiction Model (Becker and Murphy(1988)),
when the duration of drinking is longer, implying a high accumulation of drinking
over time, the marginal utility of drinking rises and the individual needs more
alcohol to make price equal the marginal utility of drinking. Hence, the durationof drinking increases alcohol consumption. However, the duration of drinking
or drinking behavior may have aﬀected labor productivity in the past, and may
aﬀect current labor productivity. In that case, the duration of drinking aﬀects
not only current alcohol consumption, but also current labor productivity. In
this sense, it is not an ideal instrument. In the following analysis, we perform
both cases where the duration of drinking is used and not used as an instrument
for assessing alcohol consumption.
Next, the instrument variables for labor productivity are the quadratic function of tenure, the interaction of age and tenure, and employee size. These arevery popular variables in the Mincer type wage function applied to the Japaneselabor market.
Summary statistics are shown in Table 1. As shown below, the unit of alcohol
consumption is one drink of sake, which is equivalent to 20g pure alcohol. It
indicates that only 5-6% of respondents have knowledge of the rigorous definition,5

but 18% have knowledge of the broad definition.

4

Estimation Result

The estimation results for drinking and labor productivity in the rigorous definition are summarized in Tables 2 and 3. Those of the broad definition are shownin Tables 4 and 5.
Before evaluating the estimators, we must perform the Hausman test under
the null hypothesis that Ordinary Least Square (OLS) is equivalent to Instrumental Variable Methods (IV). If this test result is not rejected, alcohol consumption/labor productivity is exogenous and thus we do not need to use IV and
OLS is appropriate because it is more eﬃcient than IV. Conversely, if the result
is rejected, alcohol consumption/labor productivity is endogenous and thus wehave to use IV because OLS is inconsistent. Almost all the test statistics, except
for labor productivity in females under the broad definition, are very low and are
not rejected. Moreover, F statistics in the first step, where the instrument variable regresses on endogenous variables, are lower than 10 except for male laborproductivity. While we relax the criterion to be more than 8, the estimation foralcohol consumption overcome these condition. In this case, labor productivityof female does not suﬃcient strong instruments (Bound, Jaeger and Baker(1995),
Staiger and Stock(1997)). Therefore, these test statistics mean we should refer
the estimation result in OLS for alcohol consumption in both gender and laborproductivity in male, and we do not consider about labor productivity in female
due to lack of appropriate instruments.
Let us evaluate the relationship of most concern, which is between alcohol
consumption and labor productivity. The estimated estimator shows that laborproductivity does not significantly aﬀect alcohol consumption, but the reverse

6

relationship is significantly positive except for males under the rigorous definition.
Moreover, the estimated elasticity is less than 1 for males, but it is more than 1
for females. However, we have to remember that the female case is characterizedby high endogeneity in labor productivity and weak instruments.
In alcohol consumption estimation other than labor productivity, it is anincreasing concave function of age, with peaks at 50 years of age for males and42 years of age for females. Only female medical school graduates practice risk
aversion and decrease alcohol consumption. Healthy persons, whose self-assessed
health condition is excellent, tend to drink more compared with those who reporta fair or bad health condition.
The duration of drinking gradually increases the amount of drinking as thetheory predicts. An additional 10 years of drinking experience raises the amountdrunk by 22% in males and 12% in females. The cause of death or morbidity
in parents is not significant. Conversely, the knowledge in the broad definition
significantly reduces drinking. This result supports the strategy of Health Japan
21. Note that this does not imply causality in the rigorous sense. In other words,
it is not clear in this estimation whether knowledge exactly reduced drinking or
heavy drinkers justify their consumption levels as appropriate.
For estimation of labor productivity for eﬀects other than alcohol consumption, tenure in males and age in females are significant. The reason for insignificance of age in males seems to be long-term employment in Japan and there isthus a high covariance of age and tenure. For males, other labor income is low
when other income is high. This pattern appears to reflect part-time work of dependents of more than 20 years age. Low earnings for self-employed and part-timework is a matter of course. Health conditions do not aﬀect labor productivity.
In order to check the robustness of the results obtained, we estimate anotherspecification omitting duration of drinking as instrument variables2) . The esti7

mation results are summarized in Tables 6 and 7. Though omitting duration ofdrinking does not aﬀect alcohol consumption, it aﬀects the labor productivity
result. Specifically, the estimated elasticity is more than 1 for males, but theHausman test indicates that OLS is appropriate. For females, alcohol consumption does not significantly aﬀect this specification.

5

Concluding Remarks

For males, OLS results with the Hausman test show the amount of drinking
rises labor productivity significantly and its elasticity is about 0.13. Conversely,
female case is suﬀered from weak instruments and thus we cannot reach the form
conclusion. The potential reason for this may be the small sample size, which ishalf that of males, and/or more heterogeneous than male. Thus, we cannot makeconclusions about the female case and suggest further research.
Moreover, the knowledge of the harmful eﬀects of alcohol reduces drinking by
20% in males and supports the Health Japan 21 strategy. On the other hand,labor productivity or income does not aﬀect alcohol consumption.
Since this is the first investigation of drinking and labor productivity in Japan,
the results obtained allow us to make only tentative conclusions. We need to checkthe robustness of our research by using other data, methods and instruments.

8

Footnotes*) This research is a part of the research project financed by the Japanese
Ministry of Education and Science, the 2000 Scientific Research Grant,
entitled “Evaluation for Economic Institutions Based on Empirical Researchfor Household Behavior” (grant number 12124207), headed by Prof. Fumio
Hayashi of the University of Tokyo. We are grateful for the comments
received from the participants in the project and Professor Tadashi Yamada
of Tsukuba University. We would like to acknowledge the contributions ofdiscussion and information provided to us by Wataru Senou. I would also
like to acknowledge the assistance provided by Kazuko Matsumoto andKunio Tsuyuhara.
1) Other than the relationship with labor supply, there is considerable empirical research on alcohol from the viewpoint of health economics. Baughaman,
Colin, Dickert-Conlin, Pepper(2001) analyzed the eﬀects of drinking regulations on traﬃc accidents and found that such a regulation increases therate of accidents. On the other hand, Pinka and Markowitz(2001) could
not establish a robust relationship between mothers’ alcohol consumption
and their children’s behavior.
2) The estimation under another specification is performed in the broadly
defined knowledge because it provides more interesting results in the above
estimation. There is no substantial diﬀerence between the two definitionsof knowledge.

9References[1] Baughaman,R., M.Colin, S.Dickert-Conlin, and J.Pepper (2001), ”Slippery
When Wet: the Eﬀects of Local Alcohol Access Laws on Highway Safety,”
Journal of Health Economics 20, pp.1089-1096.
[2] Becker,G.S., and K.M.Murphy (1988), ”A Theory of Rational Addition,”
Journal of Political Economics 96,4, pp.675-700.
[3] Benham,L., and A.Benham (1982), ”Employment, Earnings, and PsychiatricDiagnosis,” Economic Aspects of Health, Fuchs.V.(ed). University of Chicago
Press:Chicago, pp.203-220.
[4] Berger,M.C., and J.P.Leigh (1988), ”The Eﬀect of Alcohol Use on Wages,”
Applied Econnomics 20, 1343-1351.
[5] Bound,J., D.A.Jaeger, and R.M.Baker (1995), ”Problems With Instrumental
Variables Estimation When the Correlation Between the Instruments andthe Endogenous Explanatory Variable Is Weak,” Journal of the American
Statistical Association pp.443-450.
[6] Bryant,R.R., V.A.Samaranayake, and A.Whilhite (1993), ”The Influence ofCurrent and Past Alcohol Use on Earnings: Three Approaches to Estimation,” Journal of Applied Behavior Science 29, 9-31.
[7] Freeman,D.G. (1999), ”A Note on Economic Conditions and Alcohol Problems,” Journal of Health Economics 18, pp.661-670.
[8] French,M.T., and G.A.Zarkin (1995), ”Is Moderate Alcohol Use Related toWages? Evidence from Four Work-Sites,” Journal of Health Econnomic 14,
pp.319-344.

10

[9] Grossman,M. (2001), ”The Human Capital Model”, in A.J.Culyer and
J.P.Newhouse, eds, Handbook of Health Economics, Elsevier.
[10] Holman,C.D.J., D.R.English, E.Milne, et al. (1996), ”Meta-Analysis of Alcohol and All-Cause Mortality: a Validation of NHMRC Recommendations,”
MJA 164, pp.141-145.
[11] Kenkel,D.S., and D.C.Ribar (1994), ”Alcohol Consumption and Young
Adults’ Socioeconomic Status,” Brookings Papers Econnomics Activity:Microeconomics 1, pp.119-161.
[12] Mullahy,J. and J.L.Sindelar (1993), ”Alcoholism, Work, and Income,” Journal of Labor Economics 11(3), pp.494-520.
[13] Pinka,C., and S.Markowitz (2001), ”The Impact of Maternal Alcohol andIllicit Drug Use on Children’s Behaviour Problems: Evidence from the Children of the National Longitudinal Survey of Youth,” Journal of Health Economics 20, pp.703-731.
[14] Ruhm (1995),”Economic Conditions and Alcohol Problems,”Journal of
Health Economics 14, pp.583-603.
[15] Staiger,D., and J.H.Stock (1997), ”Instrumental Variables regression With
Weak Instruments,” Econometrica 65(3), pp.557-586.
[16] Thomas,S. (2001), ”Alcohol Abuse and Economic Condition: Evidence from
Repeated Cross-Section of Individual-Level Date,” Health Economics 10,
pp.257-270.
[17] Tsugane,S., M.T.Fahey, S.Sasaki, et al. (1999), ”Alcohol Consumption and
All-Cause Cancer Mortality Among Middle-Aged Japanese Men: Seven Year11

Follow-Up of the JPHC Study Cohort I,” American Journal of Epidemiology
150, pp.1201-1207.
[18] Zarkin,G.A., M.T.French, T.Mroz, et al. (1998), ”Alcohol Use and Wages:
New Results from the National Household Survey on Drug Abuse,” Journalof Health Economics 17(1), pp.53-68.
[19] The Committee for Health Japan 21 (2000), Report for the National Movement of Health Promotion in the 21st Century (Health Japan 21).
[20] Sato,M., and Y.Ohkusa (2002), ”An Empirical Research for Smoking Initiation in Japan,” manuscript.

12

Table 1: Summary Statistics

Labor Income(log)
Alcohol Cons.(log)Age
Age2
TenureTenure2
Age·Tenure
Other Income(log)
Education Attainment
Medical School
Other University
Two Years College
Vocational School
Time Discount Factor
Risk Aversion
Self Assessed Health
Good
Fair
Poor
Very Poor
Goverment Employee
Self Employed
Part Time Job
Firm Size(log)
Knowledge Level
Knowledge Level(2)Morbidity in Parents
Drinking Duration

Average
6.192261.999656
43.14043
2002.832
14.64953
341.5498
707.0142.464473

Male
Std. Dev.
1.088171.6083761
11.91444
1065.795
11.2756436.1824
668.5376
3.00303

Average
4.820081.5379219
41.12286
1846.694
7.338192
121.3382
356.4082
6.044326

FemaleStd. Dev.
1.068712.4356412.49203
1060.113
8.227178
264.8124484.7816
1.614122.0092879.495356.0263158.0928793.8859264.1334806.0959996.5003659.1601967.2904881.0754176.1545627.0028653.2091691.2406877.1232092.8915542.116444.0535288.4072993.4281148.329149.0760757.1411296.3051643.5117371.0985915.0156495.0740741.1450617.0262346
4.72599.0524691.1805556.1033951
21.57935.4608377.5002538.2983464.1242123.2620937.3524351.1599556
2.28262.2231434.3849467.3047094
11.10908.3409742.5071633.1002865.008596.04.1228571.5657143
3.847275.0628571.1857143.0857143
16.46285.4747173.5006665.3008129.0924477.1962397.3287431.4963724
2.122926.2430533.3894327.2803425
10.50488

Note:Sample size is 643 in male and 350 in female.

13

Table 2: Estimation Result in Alcohol Consumption

Labor Income(log)AgeAge2
Other Income(log)
Education Attainment
Medical School
Other University
Two Years College
Vocational School
Time Discount Factor
Risk Aversion
Self Assessed Health
Good
Fair
Poor
Very Poor
Knowledge Level
Morbidity in Parents
Drinking Duration
constant
Hausman Test
p-value
F stat(1st step)
p-value
F stat(2nd step)
p-value
2
R

3SLS
Male
Female.0097332.0456081.0465336∗∗∗.056246∗∗∗-.0005727∗∗∗ -.0005583∗∗∗.0046057-.0207122

OLS
Male
Female.0286383.0260648.0549352∗∗∗ .0392732∗∗-.0005536∗∗∗ -.000472∗∗∗.0061013-.015491-.3922111-.0761214.059898.0071876-.0897377.0974137-.8569118∗-.0433436-.0273993.0276715-.2899026-.5957341∗∗∗-.3857193-.0954014∗.0441416.0073094-.0895623.1105082-.790234∗-.0234935-.0152943.0402326-.2491723-.5561271∗∗∗-.1278031-.1716826∗-.2050657∗-.1296732-.0312078-.0157774.0221136∗∗∗-.5462771
7.71
0.73
8.24
≤0.0000
13.02≤0.0000
0.2674-.0823639-.0779425-.2593828∗-.1225815.0309553.0545959.012405∗∗∗-.3440808
6.63
0.25
8.78
≤0.0000
3.58≤0.0000
0.1223-.1219897-.1627078∗-.1923044∗-.1273477-.0237749-.0143534.0219188∗∗∗-.6367792-.0820405-.0719642-.2423169∗-.1113445.03618.0469279.0127898∗∗∗-.1995851

13.83≤0.0000
0.27573.39≤0.0000
0.114914

Table 3: Estimation Result in Labor Income

Alcohol Cons.(log)Age
Age2
TenureTenure2
Age·Tenure
Other Income(log)
Education Attainment
Medical School
Other University
Two Years College
Vocational School
Time Discount Factor
Risk Aversion
Self Assessed Health
Good
Fair
Poor
Very Poor
Goverment Employee
Self Employed
Part Time Job
Firm Size(log)constant
Hausman Test
p-value
F stat(1st step)
p-value
F stat(2nd step)
p-value2
R

Male.4599617.0334321-.0004334.0512598∗-.000096-.0005259-.0236258∗

3SLS
Female1.33893∗∗-.130719∗∗.0014811∗∗.0669202.000481-.0010649.0589067

Male.1339077∗.062083∗-.0006702∗.0538695∗∗-.0000653-.0005863-.0215961

OLS
Female.1273949-.0397929.0004646.0439066.0003651-.0005719.029733-.5278725.3512312∗∗∗.2665785.1716071.2917005-.0318548

1.604901.0362669-.1400049.0717846
1.134021.8479043∗-.6628151.3287081∗∗∗.2660608.1703461.3035178.0156771.6584168.0089018-.1750401.156441.8768537.1944511-.1904141-.0311075-.0150414.0616582-.0884288-.3363858∗∗-.7700529∗∗∗.0401534
4.388965∗∗∗
4.95
0.998914.56≤0.0000
7.04≤0.0000
0.1626.1793413.1418404.5538471.7434778.36709-1.149695∗∗∗-1.054122∗∗∗.0124207
5.78408∗∗∗
6.63
0.24963.53≤0.0000
6.61≤0.0000
0.0862-.2425456-.1002714-.0891181-.0040772-.1064286-.2849229∗∗-.7598206∗∗∗.0421536∗
3.993381∗∗∗.070973.0210541.2070738.6167716.3264826-1.113089∗∗∗-1.174005∗∗∗.0150725
5.115168∗∗∗7.45≤0.0000
0.19299.00≤0.0000
0.352315

Table 4: Estimation Result in Alcohol Consumption under
Another Information Definition

Labor Income(log)AgeAge2
Other Income(log)
Education Attainment
Medical School
Other University
Two Years College
Vocational School
Time Discount Factor
Risk Aversion
Self Assessed Health
Good
Fair
Poor
Very Poor
Knowledge Level
Morbidity in Parents
Drinking Duration
constant
Hausman Test
p-value
F stat(1st step)
p-value
F stat(2nd step)
p-value
2
R

3SLS
Male
Female-.0024074.0478219∗∗∗.0454807∗∗∗.055175-.0005533∗∗∗ -.0005416∗∗∗.0058485-.0200375

OLS
Male
Female.024915.0270465∗∗∗.0529184.0380158∗∗-.0005244∗∗∗ -.0004524∗∗.0075709-.0153611-.4313849∗-.083297.0337495-.002611-.1510412.1052713-.8079828∗-.0462229-.0292963.0307527-.269825-.5938243∗∗∗-.4185234∗-.1047405∗∗.0137051-.0032938-.1550303.1176636-.7278953∗-.0278087-.0186063.043636-.2203742-.5539879∗∗∗-.132076-.1814609∗∗-.2048198∗-.153765-.1524088∗∗-.00885.0217493∗∗∗-.3673267
7.24
0.77908.24≤0.0000
13.51≤0.0000
0.2739-.0912049-.0865325-.2548128∗-.144767-.0626086.0509993.0122161∗∗∗-.3395605
6.18
0.28898.78≤0.0000
3.64≤0.0000
0.2748-.124683-.1724246∗-.1912336∗-.1496814-.1522299∗∗-.0065359.0215486∗∗∗-.4855488-.0910628-.0820336-.2370446∗-.1369673-.0777138.0452682.0125643∗∗∗-.1851095

14.37≤0.0000
0.28403.49≤0.0000
0.119216

Table 5: Estimation Result in Labor Income under
Another Information Definition

Alcohol Cons.(log)Age
Age2
TenureTenure2
Age·Tenure
Other Income(log)
Education Attainment
Medical School
Other University
Two Years College
Vocational School
Time Discount Factor
Risk Aversion
Self Assessed Health
Good
Fair
Poor
Very Poor
Goverment Employee
Self Employed
Part Time Job
Firm Size(log)constant
Hausman Test
p-value
F stat(1st step)
p-value
F stat(2nd step)
p-value2R

Male.6096822∗.0209581-.0003307.0508204∗-.0000979-.0005232-.0241065∗

3SLS
Female1.2654∗∗-.1252006∗∗.0014195∗∗.0655234.000474-.001035.0571361-.4628512.3605664∗∗∗.2670884.1697773.2996624-.0503578

1.547458.0346061-.1421312.0769225
1.118413.8082451-.1666308-.0026433.0178674.0917028-.0795142-.3539808∗∗-.7726295∗∗∗.0402156
4.539304∗∗∗
6.93
0.990715.17≤0.0000
6.86≤0.0000
0.1616.1727643.1345097.5328008.7357878.3646255-1.147473∗∗∗-1.061398∗∗∗.0125816
5.743483∗∗∗51.05
0.0001
3.63≤0.0000
7.45≤0.0000
0.192917

Table 6: Estimation Result in Alcohol Consumption(excluding Drinking Duration)

Labor Income(log)AgeAge2
Other Income(log)Medical School
Other University
Two Years College
Vocational School
Time Discount Factor
Risk Aversion
Good
Fair
Poor
Very Poor
Knowledge Level
Morbidity in Parents
constant
Hausman Test
p-value
F stat(1st step)
p-value
F stat(2nd step)
p-value
2
R

3SLS
Male
Female-.0409866.0468702∗∗∗.0628476∗∗∗.0861011∗∗∗-.0006918-.0006596∗∗∗.002604-.0262538-.5285812∗∗ -.6925939-.0700843-.0226112-.0228453-.0125181.0216982.078558-.1687941-.2832074.1329646-.5299202∗∗∗-.1751032∗-.1072791∗∗-.2149321-.1043509-.2962287∗∗-.2197496∗-.213145-.1748202∗∗∗-.0692067-.1886683-.0194316.0783688-.653976-.5691712
7.38
5.89
0.76760.31708.24
8.78≤0.0000≤0.0000
12.29
2.77≤0.0000
0.00040.23380.0864

18

OLS
Male
Female.0240276.0380364∗∗∗.0784957.0574183∗∗∗∗∗∗-.0006083-.0005905∗∗∗.0059179-.0207335-.4877068∗∗ -.6271804-.1054648∗∗ -.0057627-.0596032-.0001838.0102068.091081-.1809854-.2459426.1384802-.4923349∗∗∗-.1590201∗-.112374∗∗-.2012263-.1045483-.2061258∗-.2825446∗∗-.1983901-.1765419∗∗∗-.1785905-.0861231-.0187921.0719122∗∗-.5025754-.8857697

13.29≤0.0000
0.27482.69
0.00050.0794

Table 7: Estimation Result in Labor Income (excluding
Drinking Duration )
3SLS
Male
FemaleAlcohol Cons.(log)1.30437∗1.226542
Age-.0334041-.1222843.0001083.0013868
Age2
Tenure.0495371.0647853
2-.0000707.0004703
TenureAge·Tenure-.0005476-.0010192
Other Income(log)-.0227749.0562004
Medical School-.1597815
1.517101
Other University.3981458∗∗∗ .0337284
Two Years College.2725888-.1432549
Vocational School.1363394.0796377
Time Discount Factor .427949
1.110165
Risk Aversion-.1119825.7872871
Good-.0488073.1692886
Fair.124932.1306357
Poor.1693323.5216789
Very Poor.2370051.731724
Goverment Employee -.046888.3633231
Self Employed-.4186798∗∗ -1.146299∗∗∗
Part Time Job-.7743614∗∗ -1.065243∗∗∗
Firm Size(log).0458329.0126667
constant
5.055997∗∗∗ 5.722029∗∗∗
Hausman Test
8.13
43.99
p-value
0.94490.0003F stat(1st step)
14.08
2.70
p-value≤0.0000
0.0007
F stat(2nd step)
5.09
6.72
p-value≤0.0000≤0.0000
2R
0.15840.133319