070312 seminar
TRANSCRIPT
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Smoking, Drinking and Obesity
Hung-Hao Chang* David R. Just Biing-Hwan LinNational Taiwan University Cornell University ERS, USDA
Present at National Chung-Cheng University
March, 2007
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Background
Smoking, Drinking and Obesity have caused serious
public-health concern in the U.S.
-- 65% of adults aged 21 and over were eitheroverweight or obese. 30% of them were obese.
Compared to 30 years ago, it increases almost 50%.
(Hedley et al,2004)
-- Disease burden associated with obesity in the U.Sis substantial. In 1995, the cost of obesity were US$
92 billion, 10% of the total cost of illness.
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In 2000, tobacco smoking caused more than
400,000 deaths. Smoking has been a leading
preventable cause of mortality in the UnitedStates. Recently, anti-smoking has been an
important policy in U.S.
Evidence from public health has shown thatdrinking may be associated with smoking
behavior.
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Is smoking negatively associated with body weight?
From: Gruber and Frakes (2006),Journal of Health Economics.
Smoking and obesity rates are two significant trends over 30 years in U.S
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Literature Review
Study Data Method Conclusion
This Study CSFII 1994-1996 Quantile
Chen et al. (2007) CSFII 1994-1996 Censored Smoking has insignificant effect on BMI
Gruber and Frakes(2006 BRFSS 1984-2002 IV No evidence that smoking leads to weight gain
Ruidavets et al. (2002) France Survey OLS Positive of smoking and BMI
Chou et al. (2004) BRFSS 1984-2002 OLS Cigarette price (+); alcohol price (-) on BMI.
Lin et al. (2004) CSFII 1994-1996 OLS Smokers tend to be thinner than non-smokers
Wilson et al. (2004) St. Louis Survey Logit Smoking leads to low body weight
Jee et al. (2002) Korea Health Survey Logit Smoking leads to low body weight
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What do we learn from previous studies?
Association between body weight and unhealthy decisions:
The evidence whether the increased alcohol
consumption contributes to body weight is mixed.However, it may be important to distinct the effects of
drinking beer and liquor.
Smoking tends to be negatively associated with body
weight. However, the negative evidence has been re-investigated recently. (Chen et al, 2007. Gruber and
Frakes 2006).
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What may drive these inconclusive results?
Interrelationship between unhealthy decisions:
Smoking and drinking are highly correlated. Failing
to control for one in estimation may lead to seriousbias. (Kenkel and Wang 1999).
Conditional mean effect:
Most of the studies relied on the ordinary least
squares (OLS). However, this method might not besufficient in the context of obesity. (Kan and Tsai
2004).
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Research Objectives
Investigate the interrelationship among smoking,drinking beer, and drinking liquor. Determine ifthese decisions are jointly or independently
determined. Identify factors that may affect each decision.
Account explicitly for the effects of these decisionson body weight.
Test if the effects of these decisions on body weightare heterogeneous (distinction between overweightand normal weight people).
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Data
Data from Continuing Survey of Food Intakes by
Individuals (CSFII 1994-1996) is used. This data set
is conducted by USDA.
We exclude individuals under 20 years-old.
The final sample size includes 3,409 adult of this
survey.
Body weight is measured as body mass index (BMI),weight in kilograms divided by height in meters
squared.
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Distribution of BMI in our selected sample
Mean 26.5
Std. Dev. 5.7
Skewness 1.4
Kurtosis 7.1
10% 20.4
20% 21.9
25% 22.5
30% 23.2
40% 24.4
45% 25.0
50% 25.6
60% 26.6
70% 28.3
75% 29.2
78% 30.0
80% 30.3
90% 33.7
Percentile
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According to the definition of the Center forDisease Control (CDC), overweightpeopleare those whose BMI is greater than 25. If theBMI exceeds 30, the individual can beregarded as obese.
In our sample, 45% are normal weight; about
22% are identified as obese. The distribution of BMI departs from the
normal distribution.
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Sample Statistics
Variable Definition Mean Std. Dev.DSMOKE If the respondent smokes cigarette (=1) 0.3 0.4
DBEER If the respondent drinks wine (=1) 0.4 0.5
DLIQUOR If the respondent drinks liquor (=1) 0.4 0.5
BMI Body Mess Index. Weight divided by height square. 26.5 5.7
AGE Age in years 51.0 17.0
MALE If the respondent is male(=1) 0.3 0.5
EMP_STAT If the respondent is employed (=1) 0.5 0.5
PCTPOV Annual income. 211.4 95.0
NOHS If the respondent didn't finish high school(=1) 0.2 0.4
HS If the respondent finish high school(=1) 0.3 0.5
SOMECOLL If the respondent finish some colleage (=1) 0.2 0.4COLLEGE If the respondent finish colleage (=1) 0.2 0.4
WHITE Non-Hispanic White (=1) 0.8 0.4
HISPAN Respondent is Hispanic (=1) 0.1 0.3
BLACK Non-Hispanic Black (=1) 0.1 0.3
ASIAN Asian pacific islander (=1) 0.0 0.1
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Variable Definition Mean Std. Dev.NEAST Reside in the Northeast states (=1) 0.2 0.4
MIDWEST Reside in the Midwest states (=1) 0.3 0.4
SOUTH Reside in the Southern states (=1) 0.3 0.5
CENTER Live in metropolitan area, central city (=1) 0.3 0.5
OUTSIDE Live in metropolitan area, outside central city (=1) 0.4 0.5WEST Reside in the Western states (=1) 0.2 0.4
DIET Family menber is on the special diet (=1) 0.3 0.4
VITAMIN Dietary recall of vitamin user (=1) 0.4 0.5
FOOD Knowledge of food guid pyramid. 2.5 1.2
IMPVF1 If consuming plenty of fruits and vegetable is import 0.7 0.4EXERONCE Respondents exercise at least once a week(=1) 0.5 0.5
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Structure of the Empirical Analysis
An innovative two-stage econometric model is proposed:
Stage 1: Three binary choices are specified: smoking,drinking beer and drinking liquor. A tri-variate
probit model is estimated to capture the correlationsamong these choices.
Stage 2: A body weight equation is estimated toaccount explicitly for the endogenous choices. We
estimate this quation with quantile regressionmethod.
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Stage 1: Modeling the joint decisions
(trivariate probit model)
RHO (S,B)
RHO (S,W) RHO (W,B)
Smoking
Drinking Wine
Drinking Beer
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Stage 1: (cont.)
1111 '* eXHI
2222 '* eXHI
3333 '* eXHI
Smoking Decision
Decision to drink beer
Decision to drink wine
)
1
1
1
;0,0,0(~),,(
3231
3221
3121
321
Neee
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Estimate the discrete choice model (MLE)
The probability of regime (1,1,1):
Log likelihood function of the entire eight regimes:
],,,',','[loglog 2332133112213332221111
kkkkkkXHkXHkXHkLn
i
)',','Pr()1,1,1Pr(33322211132111
XHeXHeXHeIIIP
),,,',','(231312332211
XHXHXH
where k1=2I1-1, k2=2I2-1, k3=2I3-1
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Statistical Evidence of the Joint Decisions
Coefficient
t-value
RHO (Smoking, Liquor)
0.16 5.20
RHO (Smoking, Beer) 0.19 6.57
RHO (Liquor, Beer) 0.56 25.03
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Correlations between smoking and drinking
Drinking beer and liquor is strongly
associated (56%).
The decisions to smoke and to drink beer aresignificantly correlated (19%). In addition, the
correlation between drinking liquor and
smoking is 16%. This is consistent with the evidence of public
health in terms of the gateway effect.
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Other Determinants of Smoking and Drinking
Variable Coef. t-value Coef. t-value Coef. t-value
FOOD -0.04 -1.96 0.01 0.54 0.03 1.52
IMPVF1 -0.27 -4.90 -0.09 -1.58 -0.05 -0.91
DIET -0.12 -2.04 -0.10 -1.91 -0.19 -3.58
VITAMIN -0.13 -2.37 -0.03 -0.67 0.00 0.02EXERONCE -0.18 -3.45 -0.01 -0.23 0.16 3.42
PCTPOV 0.00 -2.80 0.00 5.82 0.00 5.09
AGE -0.01 -7.17 -0.01 -8.38 -0.02 -10.69
MALE 0.13 2.48 0.23 4.57 0.80 15.50
EMP_STAT 0.03 0.54 0.12 2.17 0.11 2.01NOHS1 0.59 6.76 -0.53 -6.30 -0.19 -2.37
HS1 0.60 8.50 -0.28 -4.40 -0.16 -2.40
SOMECOLL 0.39 5.06 -0.09 -1.29 -0.13 -1.90
Smoking Drinking Liquor Drinking Beer
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Variable Coef. t-value Coef. t-value Coef. t-value
ASIAN -0.99 -3.24 -0.98 -4.01 -0.45 -2.17
BLACK -0.06 -0.69 -0.10 -1.17 0.14 1.91
HISPAN -0.30 -2.93 -0.43 -4.28 -0.06 -0.59
OTHER 0.33 1.69 -0.19 -0.83 -0.05 -0.21WEST 0.22 2.67 -0.01 -0.17 0.10 1.33
SOUTH 0.13 1.85 -0.43 -6.46 -0.16 -2.41
MIDWEST 0.16 2.15 -0.10 -1.51 0.05 0.69
CENTER 0.04 0.53 0.25 3.81 0.13 1.97
OUTSIDE -0.03 -0.57 0.11 1.76 0.02 0.31
Smoking Drinking Liquor Drinking Beer
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Empirical findings
Perception and knowledge of healthy foodconsumption decrease the likelihood to smoke.
Low education and income lead to high chance to
smoke, but low chance to drink wine. Male is more likely to smoke, and to drink beer.
Job status increases the propensity to drink wine.
Young generation has high probability to smoke. Other lifestyles also matter. If family members are
on diet, they are less likely to smoke, and to drinkbeer and liquor.
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How much we believe in our model specification?
-- Empirical results of statistical tests
Quantiles Test value#
Smoking 25% 10.14
Drinking Liquo 50% 20.15
Drinking Beer 75% 15.62
Quantiles Test-value##
Smoking 25% 2.73
Drinking Liquo 50% 2.15
Drinking Beer 75% 1.39
* Ho: exogeneity
**Ho: additional varialbes are valid
# Critical value isF[3,3386]
## Critical valuex
2
(0.95,3)=7.81
Overidentification Test**
Endogeneity Test*
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Findings
If binary indicators are used, they are
endogenous to the body weight. Therefore,
there is a call for instruments (IV). When instruments are used, statistical tests
show that the added restrictions are not
rejected. In other words, our selectedinstruments are not over-identified.
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Stage 2: Body Weight Equation
The body weight equation is specified as:
To avoid endogeneity,predicted probabilitiesareused as instrumentsforIj. Quantile regression is
used to estimate this equation (Koenker and Bassett
1978).
eIdIdIdXBMI 332211 ***'
332211321 ***'),,,|( IdIdIdXIIIXBMIQ
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Evidence of heterogeneous effects on BMI
Coef. t-value Coef. t-value Coef. t-value Coef. t-value
PSMOKE -4.8 -2.7 -1.4 -0.8 -2.7 -1.5 -4.7 -1.9
PLIQUOR -8.7 -3.4 -3.5 -1.2 -7.0 -2.6 -10.8 -3.0
PBEER 10.1 3.4 6.5 2.2 8.4 2.6 10.6 2.8
OLS 25% 50% 75%
F Test ** test p-value
25% vs 50% 2.72 0.01
25% vs 75% 21.43 0.00
50% vs 75% 8.98 0.00
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Effect of smoking on BMI distribution
-25
-20
-15
-10
-5
0
5
10
0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95
Percentiles
OLS
PSMOKE
PSMOKE_U
PSMOKE_L
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Effect of Drinking Liquor on BMI distribution
-35
-30
-25
-20
-15
-10
-5
0
5
10
0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95
Percentiles
OLS
PLIQUOR
PLIQUOR_U
PLIQUOR_L
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Effect of Drinking Beer on BMI distribution
-15
-10
-5
0
5
10
15
20
25
30
35
40
0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95
Percentiles
OLS
PBEER
PBEER_U
PBEER_L
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Effects of other variables
Coef. t-value Coef. t-value Coef. t-value Coef. t-value
PCTPOV 0.0 4.1 0.0 3.9 0.0 3.9 0.0 3.1
AGE -0.1 -7.4 -0.1 -2.7 -0.1 -4.2 -0.2 -6.5
MALE 4.1 5.6 3.7 5.2 4.1 4.8 4.2 4.5
IMPVF1 -1.0 -3.3 -0.5 -1.8 -0.8 -2.4 -1.1 -2.6
VITAMIN -0.6 -2.6 -0.3 -1.5 -0.4 -1.7 -0.4 -1.2
BLACK 2.8 7.2 2.1 4.2 2.9 6.2 3.7 6.7
HISPAN -1.2 -2.0 0.4 0.5 -0.2 -0.3 -1.6 -1.7
ASIAN -8.0 -6.7 -3.3 -2.8 -6.0 -5.8 -9.1 -6.1
EXERONCE -0.3 -1.1 -0.2 -0.5 -0.1 -0.2 -0.6 -1.5
WEST 0.7 1.9 0.1 0.4 0.4 1.0 0.4 0.8
SOUTH -1.4 -3.9 -0.4 -1.2 -0.9 -2.3 -1.9 -3.7
MIDWEST 0.6 2.1 0.7 2.7 1.0 3.0 0.0 0.0
CENTER 0.9 2.8 0.5 1.6 0.7 2.3 0.8 1.8
OUTSIDE 0.2 0.7 0.2 0.9 0.2 1.0 0.0 0.1
OLS 25% 50% 75%
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Empirical findings
A significant evidence supports the misspecification
of using OLS. The effects are heterogeneous across
the entire distribution of BMI.
Smoking tends to be negatively correlated with BMI.
However, it is insignificant over the entire
distribution of BMI.
Drinking beer tends to increase the body weight.However, this effect is not significant for obese
people (above 85 percentile).
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Drinking liquor is found negatively associated with
body weight. In addition, the decreasing effect is
significant for obese people (75 percentile).
Knowledge of healthy food consumption decreases
the risk of being overweight.
Higher income leads to lower body weight.
Race is also associated with body weight. Blackhave heavy weight than others, on average; Asian
are those with less weight.
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Concluding and Policy Implications
The discussion of smoking, drinking and obesity
should be interpreted with caution. We have shown:
-- strong correlations between smoking, drinking beer
and drinking liquor.
-- heterogeneous effects of these decisions on BMI.
The effect of smoking on body weight is found
insignificant. As such, anti-smoking may not be thecritical factor driving the increasing trend of body
weight over 30 years.
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Drinking liquor is found negatively associated with
body weight. Particularly, the effect is even stronger
for normal weight people.
Drinking beer tends to increase body weight
regardless of the weight status. Beer drinkers are
those in a higher risk of being overweight.
Knowledge of healthy food consumption also havedirectand indirecteffects on body weight. A well-
educated consumer has less likelihood of being
overweight.