1. Smoking, Drinking and Obesity Hung-Hao Chang* David R. Just Biing-Hwan Lin National Taiwan University Cornell University ERS, USDA Present at National Chung-Cheng University March, 2007

2. Background  Smoking, Drinking and Obesity have caused serious public-health concern in the U.S. -- 65% of adults aged 21 and over were either overweight 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.S is substantial. In 1995, the cost of obesity were US$ 92 billion, 10% of the total cost of illness.

3.  In 2000, tobacco smoking caused more than 400,000 deaths. Smoking has been a leading preventable cause of mortality in the United States. Recently, anti-smoking has been an important policy in U.S.  Evidence from public health has shown that drinking may be associated with smoking behavior.

4. 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

5. Literature Review

6. 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).

7. 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 serious bias. (Kenkel and Wang 1999).  Conditional mean effect: Most of the studies relied on the ordinary least squares (OLS). However, this method might not be sufficient in the context of obesity. (Kan and Tsai 2004).

8. Research Objectives  Investigate the interrelationship among smoking, drinking beer, and drinking liquor. Determine if these decisions are jointly or independently determined.  Identify factors that may affect each decision.  Account explicitly for the effects of these decisions on body weight.  Test if the effects of these decisions on body weight are heterogeneous (distinction between overweight and normal weight people).

9. 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.

10. Distribution of BMI in our selected sample

11.  According to the definition of the Center for Disease Control (CDC), overweight people are those whose BMI is greater than 25. If the BMI exceeds 30, the individual can be regarded as obese.  In our sample, 45% are normal weight; about 22% are identified as obese.  The distribution of BMI departs from the normal distribution.

12. Sample Statistics

14. Econometric Strategy Our econometric model contributes to previous studies in: -- Smoking and drinking decisions are considered jointly. -- Account for endogeneity between drinking and smoking on body weight. -- Distinguish effects of these three decisions on different weight status (distribution of BMI).

15. 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 correlations among these choices.  Stage 2: A body weight equation is estimated to account explicitly for the endogenous choices. We estimate this quation with quantile regression method.

16. Stage 1: Modeling the joint decisions (trivariate probit model)

17. Stage 1: (cont.) Smoking Decision Decision to drink beer Decision to drink wine

18. Estimate the discrete choice model (MLE) The probability of regime (1,1,1): Log likelihood function of the entire eight regimes: where k 1 =2I 1 -1, k 2 =2I 2 -1, k 3 =2I 3 -1

19. Statistical Evidence of the Joint Decisions Coefficientt-value RHO (Smoking, Liquor) 0.165.20 RHO (Smoking, Beer) 0.196.57 RHO (Liquor, Beer) 0.5625.03

20. Correlations between smoking and drinking  Drinking beer and liquor is strongly associated (56%).  The decisions to smoke and to drink beer are significantly 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”.

21. Other Determinants of Smoking and Drinking

23. Empirical findings  Perception and knowledge of healthy food consumption 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 drink beer and liquor.

24. How much we believe in our model specification? -- Empirical results of statistical tests

25. 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 selected instruments are not over-identified.

26. Stage 2: Body Weight Equation The body weight equation is specified as: To avoid endogeneity, predicted probabilities are used as instruments for I j. Quantile regression is used to estimate this equation (Koenker and Bassett 1978).

27. Marginal change in the Q th quantile of BMI as a result of change in X

28. Evidence of heterogeneous effects on BMI

29. Effect of smoking on BMI distribution

30. Effect of Drinking Liquor on BMI distribution

31. Effect of Drinking Beer on BMI distribution

32. Effects of other variables

33. 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).

34.  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. Black have heavy weight than others, on average; Asian are those with less weight.

35. 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 the critical factor driving the increasing trend of body weight over 30 years.

36.  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 have direct and indirect effects on body weight. A well- educated consumer has less likelihood of being overweight.