BMI & Smoking

7/30/2019 BMI & Smoking

http://slidepdf.com/reader/full/bmi-smoking 1/20

Abstract This study examines the relationship between smoking and body massindex (BMI) with a simultaneous equations system allowing for censoring and end-ogeneity of the number of cigarettes smoked, which alleviates simultaneity biascaused by unobserved heterogeneity and expansion bias by censoring in the regres-sor. The results suggest smoking may not have a strong long-term causal effect onbody weight after controlling for the endogeneity. The negative relationship between

smoking and BMI reported in the literature is potentially attributable to the afore-mentioned biases and should be interpreted with caution. The statistical proceduredeveloped can be useful in other applications with a censored endogenous regressor.

Keywords Body mass index Æ Censored regressor Æ Overweight Æ

Simultaneous equations system Æ Smoking

Introduction

Obesity and smoking are two major public-health concerns in the US. The preva-

lence of obesity has reached an alarming level in the country. Hedley et al. (2004)combined data from the 1999–2000 and 2001–2002 National Health and NutritionExamination Surveys (NHANES) and found that 65.1% of the adults aged 21 andover in 1999–2000 were either overweight or obese, 30.4% were obese, and 4.9%were extremely obese. Brown, Mishra, Kenardy, and Dobson (2000), Kuchler and

Z. Chen (&)The Chicago Center of Excellence in Health Promotion Economics, The University of Chicago,Chicago, IL 60637, USAe-mail: [email protected]

S. T. Yen Æ D. B. EastwoodDepartment of Agricultural Economics, The University of Tennessee, Knoxville,

J Fam Econ Iss (2007) 28:49–67DOI 10.1007/s10834-006-9045-4

O R I G I N A L P A P E R

Does smoking have a causal effect on weight reduction?

Zhuo Chen Æ Steven T. Yen Æ David B. Eastwood

Published online: 15 December 2006Ó Springer Science+Business Media, LLC 2006



Variyam (2003), and Mokdad et al. (2003) assessed the deleterious effects of anoverweight population. Paeratakul, Lovejoy, Ryan, and Bray (2002) examined theobesity-related chronic diseases in US adult population according to gender, raceand socioeconomic status (SES) and concluded that the disease burden associated

with obesity in the population is substantial. Meanwhile, tobacco smoking,accountable for more than 400,000 deaths annually, remains the leading preventablecause of mortality in the US (Perkins, Hickcox, and Grobe, 2000). Dardis and Keane(1995) examined the policy implications using a risk-benefit analysis of cigarettesmoking decisions.

Researchers have recently paid increased attention to the relationship betweenobesity and smoking (e.g., Brown et al., 2000; Lin, Huang, & French, 2004; Wilson,Habib, & Philpot, 2002). They have found an inverse relationship between smokingand body weight. Kuchler and Variyam (2003) concluded that nonsmokers weremore likely to be obese than smokers. Lin et al. (2004) found a strong association

between smoking and body mass index (BMI).1 Nayga (2000) reported similarfindings after controlling for the effect of health knowledge. Chou, Grossman, andSaffer (2004) suggested that the anti-smoking campaign, especially the state andFederal excise tax hikes and the settlement of state Medicaid lawsuits, hadcontributed to the recent upward trend in obesity.

Although smoking may be associated with lower body weight, it has someundesirable health related effects. Jee, Lee, Nam, Kim, and Kim (2002) found thatsmoking increases the likelihood of an individual having a low BMI but a high waist-to-hip ratio, which is a high-risk factor for diabetes mellitus. Furthermore, the

weight-reducing effect of smoking in some studies may have been overestimated.Based on a medical experiment, Audrain, Klesges, and Klesges (1995) found thatsmoking increased resting energy expenditure in both normal-weight and obesesmokers, but the metabolic effect is larger and lasts longer in normal-weightsmokers. These findings have potential implications for discouraging obese personsfrom taking up smoking and intervention among those who already smoke.

The objective of this paper is to examine the relationship between smoking andBMI with a simultaneous equations system. It is, to our knowledge, the first effort tocorrect for the simultaneity bias and expansion bias caused by endogeneity andcensoring of the smoking variable in examining the relationship between cigarette

smoking and body weight. The rest of the paper is organized as follows. The nextsection reviews relevant literature. A simultaneous equations model is outlined inthe third section. The fourth section describes the dataset and variables used. Resultsare presented in the fifth section, which is followed by the conclusion.

Literature

Lifestyle and SES variables are important determinants of body weight (e.g., Chen,

Yen, and Eastwood, 2005; Chou et al., 2004; Lin et al., 2004; Sun, 2003). Smoking of cigarettes is also related to lifestyle and SES; see, e.g., Yen (1999, 2005) for deter-minants of cigarette smoking, and Abdel-Ghany and Wang (2003) and Sharpe,

50 J Fam Econ Iss (2007) 28:49–67



variables include food/nutrition intakes and energy expenditures. Socioeconomicfactors include household and individual characteristics.2

Lifestyle, SES and Bodyweight

Lifestyle is considered an important factor contributing to overweight and obesity.Using data from the 1994–96 Continuing Survey of Food Intakes by Individuals(CSFII), Lin et al. (2004) examined the relationships among eating behaviors, die-tary intake, physical activity, attitudes toward diet and health, sociodemographicvariables and BMI among women and children in different income groups. Signifi-cant correlations between BMI and age, race, dietary patterns, TV watching, andsmoking were found for women.

Kennedy, Bowman, Spence, Freedman, and King (2001) concluded that BMIswere significantly lower for men and women on high carbohydrate diets, and the

highest BMIs were noted for those on low carbohydrate diets. These findings areconsistent with the fact that diets high in carbohydrates and low/moderate in fats areusually lower in energy. Macdiarmid, Vail, Cade, and Blundell (1998) suggested thatamong women consumption of high-fat and sweet products contributes to obesity,and the altered representation of the data created by low-energy reporters appearedto distort the relationship. Field, Gillman, Rosner, Rockett, and Colditz (2003)found that fruit and vegetable consumption was not significantly related to weight.

Socioeconomic variables such as education, employment, income and personalcharacteristics have been used to explain body weight. Gutie rrez-Fisac, Rodrı guez

Artalejo, Guallar-Castillon, Banegas, and de Rey Calero (1999) identified illiteracy,sedentary lifestyle and energy intake as the determinants of geographic variations inBMI and obesity in Spain. In a study of 1966 northern Finland birth cohort, Laitinen,Power, Ek, Sovio, and Ja ¨ rvelin (2002) found that unemployment was associated withan increased risk of obesity among women after controlling for other SES variables.Subjects with low school performance and women with long unemployment historieswere also found to be at increased risk of being obese. Examining CSFII 1994–96survey data, Townsend, Peerson, Love, Achterberg, and Murphy (2001) found anunexpected and puzzling link between food insecurity and overweight status amongwomen who were overweight and having a greater potential for increased incidence

of obesity-related chronic disease.Kan and Tsai (2004) found a relationship between individuals’ knowledge con-

cerning the health risks of obesity and their tendency to be obese. Nayga ( 2000)found a statistically significant effect of health knowledge on obesity after control-ling for education among US adults using the Diet and Health Knowledge Survey(DHKS) component of the 1994 CSFII. Based on the NHANES III survey for 1988–94, Kuchler and Variyam (2003) argued that information programs linking over-weight and obesity with health risks might fail to induce diet and lifestyle changes if individuals do not recognize that they are overweight or obese. They suggested that

correcting such misperceptions may increase the size of the population susceptible toa weight-health risk information program.

J Fam Econ Iss (2007) 28:49–67 51



Cigarette consumption and bodyweight

In general, as previously mentioned, researchers have found a negative associationbetween cigarette smoking and body weight. Appendix Table 1 presents a review of

studies that examined this relationship.However, smoking is likely an endogenous decision, and thus simultaneity

between smoking and body weight equations may exist. Conventional statisticalprocedures undertaken in previous studies may be susceptible to at least two types of statistical bias. First, unobserved heterogeneity may affect the number of cigarettessmoked and body weight simultaneously. Perkins et al. (2000) found substitutioneffects between food intake and nicotine reinforcement. Yen (1999, 2005) and Yenand Jones (1996) found that SES variables are significantly related to cigaretteconsumption. The set of determinants of cigarette consumption, therefore, is likelyto overlap with that of body weight. Although we could control for certain types of

food intake, the regressors are unlikely to include every aspect of smoking and bodyweight. Stress and many psychological attributes, for instance, are usually unavail-able. Hence, treating the smoking decision as exogenous will produce biased esti-mates of the causal effects if, in fact, the decision is endogenous (e.g., Wooldridge,2002b, p. 86 and p. 253). The second type of bias is the expansion bias discussed byRigobon and Stoker (2003). The bias arises because the existence of a censoredregressor could lead to a higher (in absolute value) coefficient estimate for theregressor. The combined effects of simultaneity bias and expansion bias may explainwhy researchers have obtained strong negative effects of smoking on BMI. Unfor-

tunately, their conclusions may lead to unexpected and undesirable consequences.Klesges and Klesges (1988) noted that about one-third of smoking participants in auniversity sample reported using smoking to control weight, which would not havebeen recommended by any health professional.

The potential endogeneity and simultaneity raise interesting questions. Whatexactly is the causal relationship between smoking and overweight/obesity or weightloss—does smoking contribute to weight reduction after controlling for individualcharacteristics? Is smoking a potential means of weight control? Does modelmisspecification in previous studies lead to overestimation of the weight-reducingeffect of smoking? What are the policy implications of an improved estimate of the

relationship between smoking and being overweight? The following section developsa simultaneous equations model to address these issues.

Linear regression model with a censored endogenous regressor

Our interest centers on the effects of smoking on BMI. Let C be the number of cigarettes smoked. Many previous studies treat C as exogenous, which likely leads tothe regression of BMI (denoted y) on C and other explanatory variables ( x). This

model also ignores the expansion bias caused by a censored regressor and isformulated as

52 J Fam Econ Iss (2007) 28:49–67



(e.g., Perkins et al., 2000). A binary variable indicating whether the individual hadquit smoking is constructed from the survey responses and is included in theregression equation as well.

Another approach is to treat the consumption of cigarettes as binary, i.e., replace

C with smoker , which is (1) for smokers and (0) for non-smokers; see, e.g., Brownet al. (2000) and Lin et al. (2004). The binary variable approach does not permitestimating the effects of the extent of addiction, i.e., it could not distinguish a heavysmoker from an occasional smoker. More importantly, the endogeneity of cigarettesmoking is still ignored in these approaches. Treating the binary variable smoker asendogenous results in a treatment effects model as illustrated in Barnow, Cain, andGoldberger (1981). We treat the endogenous smoking variable as censored, versusbinary, from an information preserving point-of-view as the number of cigarettessmoked is available.

In order to account for the simultaneity between smoking (C ) and BMI ( y) and to

accommodate zero consumption of smoking (and thus to minimize the simultaneitybias and the expansion bias), a reduced form simultaneous equations model isproposed:

C ¼ maxð0; w0a þ u1Þ

y ¼ x0b þ cC þ u2; ð2Þ

where w is a vector of exogenous variables, a is a conformable parameter vector, andthe error terms u1 and u2 are distributed as bivariate normal with zero means,

standard deviations r1 and r2, and correlation q. The first Eqn. (2) is a censoredregression model which accounts for non-smokers in the sample. Derivation of thesample likelihood function is presented in Appendix A. The parameter estimates canbe obtained using the method of maximum likelihood (ML). It is important torecognize that, due to the endogeneity and censoring of C , the parameter estimatesby themselves may not reflect the change in the dependent variable ( y) resultingfrom a change in an explanatory variable. The effects of explanatory variables aremore appropriately explored from the relevant conditional and unconditional meansof the dependent variable. The expected values of y conditional on smoking,non-smoking and unconditional are, respectively,

E ð yjC [0Þ ¼ x0b þ cw0a þ ðcr1 þ qr2Þ/ðw0a=r1Þ

Uðw0a=r1Þ

!ð3Þ

E ð yjC 0Þ ¼ x0b À qr2/ðÀw0a=r1Þ

UðÀw0a=r1Þð4Þ

E ð yÞ ¼ x

0bþc

½r

1/

ðw

0a=r

1Þ þc

w

0aUðw

0a=r

1Þ ; ð5Þ

where /(Æ) and F(Æ) are the probability density function (pdf) and cumulative dis-

J Fam Econ Iss (2007) 28:49–67 53



Table 1 Variable definitions and sample statistics

Variable Definition Mean StandardDeviation

Continuous variablesBMI Body mass index: (weight in kg.) ‚(height in meters)2

26.40 5.26

C Average number of cigarettes per day 4.94 10.47Income Per-capita annual income (thousand) 16.85 12.90Age Age in years 49.66 17.10Education Highest grade completed (years) 12.68 3.14TV hours Average hours of TV/video game per day 2.65 2.16Beer Beer consumed per day (100 grams) 0.10 0.39Wine Wine consumed per day (100 grams) 0.13 0.57Bev. ratio % of carbonated beverages consumed

among allnon-alcoholic beverages0.22 0.31

HH size Household size 2.81 1.47Fruits Total fruits consumed per day (100 grams) 1.57 2.08Grains Total grains consumed per day (100 grams) 2.89 2.05Sugar Total sugar and sweets consumed per day (100 grams) 0.22 0.40Milk Total milk and milk products consumed per day (100 grams) 2.12 2.35Eggs Total eggs consumed per day (100 grams) 0.20 0.38Meats Total meat, poultry, fish consumed per day (100 grams) 2.13 1.65Legumes Total legumes consumed per day (100 grams) 0.25 0.70Beverage Total non-alcoholic beverage consumed per day (100 grams) 9.72 7.56Discrete variables (1 = yes; 0 otherwise)Smoker (if the individual) smokes cigarettes 0.26City Resides in central city 0.29

Suburban Resides in suburban area 0.45Rural Resides in rural area (reference) 0.26South Resides in the South 0.37Northeast Resides in the Northeast 0.18Midwest Resides in the Midwest 0.25West Resides in the West (reference) 0.20Food stamps Resides in a household

authorized to receive food stamps0.07

Male Gender is male 0.52Hispanic Of Hispanic origin 0.04Other race Race is not Black or White 0.07Black Race is Black 0.11

White Race is White (reference) 0.82Employed Employed full- or part-time 0.57Active work Occupation needs to be physically active 0.66Exercise Exercises at least once a week 0.48Low-fat milk Chooses to use skim or 1% milk 0.30Vegetarian A vegetarian 0.03Vitamins Takes any vitamin supplement(s) 0.47Alcohol Had used alcohol during the year 0.64Homeowner A homeowner 0.71Quit smoking Had quit smoking 0.27

Source: Compiled from the CSFII 1994–96 (USDA-ARS, 2000)

54 J Fam Econ Iss (2007) 28:49–67



in Eqs. (3–5) due to a finite change (i.e., from 0 to 1) in the variable, holding all othervariables constant.

The model used in this paper is the first approach of handing censored regressorsas in Rigobon and Stoker (2003). It is analogous to the treatment effects model, in

which the endogenous variable is a binary indicator. There are two methods of estimation. The two-stage method uses the generalized residual from the first stageas an instrument. The ML approach specifies a bivariate normal distribution andmaximizes the sample likelihood. The two-stage method produces consistentestimates whereas the ML procedure produces efficient estimates when the errordistribution is correctly specified. We use the latter.

Data

Data from the CSFII 1994–96, conducted by the US Department of Agriculture(USDA-ARS, 2000), are used in this study. CSFII 1994–96 is the most recent surveyof similar purpose and scale carried out by the USDA. The CSFII 1998 module is notused as it covers only children under 10 years old. The CSFII dataset is a multistagestratified area probability sample of non-institutionalized individuals in the US.Demographic characteristics of each individual, as well as self-reported height andweight index and food and nutrient intake information, are included. Table 1presents the names and labels of these variables.

The dependent variable in the weight equation is BMI. The explanatory variables

include regions (South, Northeast, Midwest; reference = West), urbanization (city,suburban; reference = rural), food stamp recipient status, household income, age,education, gender (male), race (Black, other race; reference = White), ethnicity(Hispanic), employment status (employed), time spent watching TV or playing videogames (TV hours), whether the individual exercised frequently (exercise) andwhether work activities involved active physical efforts (active work). Also includedin the BMI equation are food/nutrition intake and dietary pattern variables. Theyare intakes of fruits, grains, sugar, milk, eggs, meats, and legumes, consumption of beer, wine, and total non-alcohol beverages, and dummy variables indicating use of skim or 1% fat milk instead of 2% fat or whole milk (low-fat milk), whether the

respondent was a vegetarian, and was taking vitamin supplement(s) (vitamins).The number of cigarettes smoked is included in the BMI regression equation to

reflect the effect of smoking on overweight. Also included is the dummy variableindicating whether the individual had quit smoking.

To account for endogeneity and non-smokers in the sample, cigarette consump-tion is modeled as a Tobit model, and the observed consumption (C ) is also includedas an explanatory variable in the BMI Eqn. (2). All exogenous variables used in theBMI regression are included in the cigarette equation except the quitting indicator(quit smoking).

Because only individuals over 21 can legally drink alcohol and smoke cigarettes,respondents under 21 years old were excluded. The final sample included 7,876observations (see Table 1), of which 52% were male. The average age was 49.7 years

J Fam Econ Iss (2007) 28:49–67 55



be fair or better. Average per capita income was $15,850, and 7% of the individualsresided in households were authorized to receive food stamps. Average intakes of fruits, grains sugar, milk, eggs, meats, and legumes were 157, 289, 22, 212, 20, 213,25 grams, respectively.

Estimation results

Before estimating the system of equations by the ML procedure, BMI is regressedwith four specifications using OLS, and the results are presented in Table 2. In thefirst and third models, the continuous variable C is used while the dummy variable smoker is used in the other models. The last two linear models include an additionalvariable quit smoking. Both measures of smoking have strong negative and statis-tically significant effects on BMI without controlling for endogeneity. Effects of

smoking cessation on BMI are also found, which had been ignored in many previouscross-sectional studies (e.g., Lin et al., 2004). The adjusted R2 ranges from 0.098 to0.106. Inclusion of the smoking cessation variable gives the last two models slightlymore explanatory power. These OLS estimates do not provide sufficient evidence toinfer the effects of smoking on bodyweight because of the potential simultaneity biasand the expansion bias of including a censored regressor as discussed in Rigobon andStoker (2003).

ML estimation of the simultaneous equations system is carried out using themaxlik optimization routines in Gauss. Asymptotic standard errors of the parameter

estimates are derived from White’s (1982) robust covariance matrix. The parameterestimates are presented in Table 3. The endogeneity test is equivalent to testing thenull hypothesis that there is no correlation between the error terms of the twoequations; see, e.g., Hamilton, Merrigan, and Dufresne (1997) for a similar endo-geneity test. The likelihood ratio test (LR = 23.47, P < .001) leads to rejection of thenull hypothesis that there is no correlation between the two error terms, suggestingendogeneity of cigarette smoking and justifying the joint estimation of the twoequations. The negative value of q indicates that the unobserved heterogeneityaffects body weight and the number of cigarettes smoked in different directions. Thepseudo R2 (Wooldridge, 2002a, p. 465) is small (0.032), which is typical for cross-

sectional analysis.The estimated coefficient of C is negative and significant according to the OLS

estimates but insignificant according to the ML estimates. These conflicting resultssuggest that the negative effects of cigarette smoking in the OLS estimates likelyresulted from failure to accommodate endogeneity of cigarette smoking. Previousfindings in the literature that smoking helps weight control therefore may haveresulted from simultaneity and expansion biases. Individuals in medical experimentssmoke a number of cigarettes preset by the researchers, e.g., in Klesges and Klesges(1988). However, cigarette consumption is not exogenous in the long term because

individuals choose their preferred level of cigarette consumption themselves.Quitting smoking has a statistically significant and positive effect on BMIaccording to both the OLS and simultaneous-equations estimates. This agrees with

56 J Fam Econ Iss (2007) 28:49–67



S e s t i m

a t e s ( d e p e n d e n t v a r i a b l e = B M

I )

M o d e l 1

M o d e l 2

M o d e l 3

M o d e l 4

E s t i m a t e s

S E

E s t i m a t e s

S E

E s t i m a t e s

S E

E s t i m a t e s

S E

1 9 . 6 1 7 a

0 . 6 9 2

1 9 . 8 9 4 a

0 . 6 8 9

1 9 . 7 4 3 a

0 . 6 9 3

1 9 . 9 3 6 a

0 . 6 9 0

h a r a c t e r i s t i c s – 0 .

4 7 4 a

0 . 1 6 3

– 0 . 4 5 0 a

0 . 1 6 2

– 0 . 4 8 1 a

0 . 1 6 3

– 0 . 4 5 4 a

0 . 1 6 2

– 0 . 2 7 9 c

0 . 1 4 5

– 0 . 2 7 9 c

0 . 1 4 5

– 0 . 2 8 5 b

0 . 1 4 5

– 0 . 2 8 2 c

0 . 1 4 5

– 0 . 0 2 8

0 . 1 6 7

– 0 . 0 5 5

0 . 1 6 6

– 0 . 0 2 5

0 . 1 6 7

– 0 . 0 5 3

0 . 1 6 6

0 . 0 4 7

0 . 1 8 9

0 . 0 5 0

0 . 1 8 9

0 . 0 3 1

0 . 1 8 9

0 . 0 4 2

0 . 1 8 9

0 . 4 0 6 b

0 . 1 7 7

0 . 3 9 3 b

0 . 1 7 6

0 . 4 0 9 b

0 . 1 7 7

0 . 3 9 5 b

0 . 1 7 6

1 . 4 0 7 a

0 . 2 5 1

1 . 4 1 8 a

0 . 2 5 0

1 . 4 0 1 a

0 . 2 5 1

1 . 4 1 5 a

0 . 2 5 0

– 0 . 0 0 7

0 . 0 0 6

– 0 . 0 0 8

0 . 0 0 6

– 0 . 0 0 7

0 . 0 0 6

– 0 . 0 0 8

0 . 0 0 6

0 . 0 0 1

0 . 0 4 7

– 0 . 0 0 8

0 . 0 4 6

0 . 0 0 2

0 . 0 4 7

– 0 . 0 0 7

0 . 0 4 6

r a c t e r i s t i c s

0 . 3 4 2 a

0 . 0 2 1

0 . 3 4 6 a

0 . 0 2 1

0 . 3 3 6 a

0 . 0 2 1

0 . 3 4 3 a

0 . 0 2 1

– 0 . 3 2 9 a

0 . 0 2 0

– 0 . 3 3 4 a

0 . 0 2 0

– 0 . 3 2 5 a

0 . 0 2 0

– 0 . 3 3 2 a

0 . 0 2 0

– 0 . 0 8 8 a

0 . 0 2 3

– 0 . 0 9 7 a

0 . 0 2 3

– 0 . 0 8 8 a

0 . 0 2 3

– 0 . 0 9 6 a

0 . 0 2 3

0 . 4 6 3 a

0 . 1 2 9

0 . 4 3 5 a

0 . 1 2 9

0 . 4 0 5 a

0 . 1 3 1

0 . 4 1 1 a

0 . 1 3 0

0 . 6 7 0 c

0 . 3 4 3

0 . 6 5 2 c

0 . 3 4 2

0 . 6 9 1 b

0 . 3 4 3

0 . 6 6 1 c

0 . 3 4 2

– 0 . 7 4 3 a

0 . 2 5 8

– 0 . 6 6 5 a

0 . 2 5 7

– 0 . 7 0 2 a

0 . 2 5 9

– 0 . 6 5 1 b

0 . 2 5 7

1 . 3 4 8 a

0 . 2 0 1

1 . 4 7 5 a

0 . 2 0 0

1 . 3 7 7 a

0 . 2 0 1

1 . 4 8 2 a

0 . 2 0 0

0 . 0 6 4

0 . 1 6 0

0 . 0 7 9

0 . 1 6 0

0 . 0 7 7

0 . 1 6 0

0 . 0 8 4

0 . 1 6 0

0 . 0 8 5

0 . 1 5 9

0 . 1 2 3

0 . 1 5 8

0 . 0 7 5

0 . 1 5 9

0 . 1 1 8

0 . 1 5 8

– 0 . 4 5 6 a

0 . 1 1 9

– 0 . 4 5 8 a

0 . 1 1 8

– 0 . 4 4 7 a

0 . 1 1 9

– 0 . 4 5 4 a

0 . 1 1 8

0 . 1 8 5 a

0 . 0 2 8

0 . 1 8 8 a

0 . 0 2 8

0 . 1 8 2 a

0 . 0 2 8

0 . 1 8 7 a

0 . 0 2 8

– 1 . 5 5 8 a

0 . 3 6 6

– 1 . 5 4 3 a

0 . 3 6 4

– 1 . 5 6 6 a

0 . 3 6 6

– 1 . 5 4 7 a

0 . 3 6 4

r

– 0 . 2 9 4 b

0 . 1 4 2

– 0 . 3 2 3 b

0 . 1 4 2

– 0 . 3 0 6 b

0 . 1 4 2

– 0 . 3 2 8 b

0 . 1 4 2

n t i n t a k

e s

– 0 . 5 7 6 a

0 . 1 1 9

– 0 . 5 7 9 a

0 . 1 1 8

– 0 . 5 8 7 a

0 . 1 1 9

– 0 . 5 8 4 a

0 . 1 1 8

0 . 3 0 0

0 . 2 0 2

0 . 2 9 2

0 . 2 0 1

0 . 3 2 4

0 . 2 0 2

0 . 3 0 2

0 . 2 0 2

0 . 0 3 2 a

0 . 0 0 8

0 . 0 3 4 a

0 . 0 0 8

0 . 0 3 1 a

0 . 0 0 8

0 . 0 3 3 a

0 . 0 0 8

k

0 . 7 1 5 a

0 . 1 2 8

0 . 6 9 5 a

0 . 1 2 7

0 . 7 2 3 a

0 . 1 2 8

0 . 6 9 9 a

0 . 1 2 7

– 0 . 6 5 6 a

0 . 1 3 2

– 0 . 5 6 7 a

0 . 1 3 2

– 0 . 6 8 7 a

0 . 1 3 3

– 0 . 5 8 4 a

0 . 1 3 3

– 0 . 3 8 2 a

0 . 1 0 3

– 0 . 3 7 7 a

0 . 1 0 2

– 0 . 3 9 8 a

0 . 1 0 3

– 0 . 3 8 4 a

0 . 1 0 3

J Fam Econ Iss (2007) 28:49–67 57



t i n u e d

M o d e l 1

M o d e l 2

M o d e l 3

M o d e l 4

E s t i m a t e s

S E

E s t i m a t e s

S E

E s t i m a t e s

S E

E s t i m a t e s

S E

– 0 . 0 4 6

0 . 0 2 9

– 0 . 0 6 1 b

0 . 0 2 9

– 0 . 0 4 4

0 . 0 2 9

– 0 . 0 5 9 b

0 . 0 2 9

– 0 . 0 1 7

0 . 0 2 9

– 0 . 0 2 0

0 . 0 2 9

– 0 . 0 1 6

0 . 0 2 9

– 0 . 0 2 0

0 . 0 2 9

– 0 . 0 9 8

0 . 1 4 2

– 0 . 1 1 1

0 . 1 4 2

– 0 . 1 0 3

0 . 1 4 2

– 0 . 1 1 2

0 . 1 4 2

– 0 . 0 0 4

0 . 0 2 5

– 0 . 0 0 6

0 . 0 2 5

– 0 . 0 0 4

0 . 0 2 5

– 0 . 0 0 6

0 . 0 2 5

0 . 3 5 7 b

0 . 1 5 2

0 . 3 6 9 b

0 . 1 5 1

0 . 3 4 4 b

0 . 1 5 2

0 . 3 6 4 b

0 . 1 5 1

0 . 1 0 5 a

0 . 0 3 7

0 . 1 0 3 a

0 . 0 3 7

0 . 1 0 4 a

0 . 0 3 7

0 . 1 0 3 a

0 . 0 3 7

– 0 . 2 2 2 a

0 . 0 8 3

– 0 . 2 1 8 a

0 . 0 8 2

– 0 . 2 2 4 a

0 . 0 8 3

– 0 . 2 2 0 a

0 . 0 8 2

g

0 . 4 4 5 a

0 . 1 4 0

0 . 1 9 8

0 . 1 4 3

– 0 . 0 5 3 a

0 . 0 0 6

– 0 . 0 4 7 a

0 . 0 0 6

– 1 . 7 2 3 a

0 . 1 4 0

– 1 . 6 4 6 a

0 . 1 5 1

o r

4 . 9 9 7

4 . 9 7 5

4 . 9 9 4

4 . 9 7 4

F ( 3 5 , 7 8 4 0 ) = 2 5 . 5 2

F ( 3 5 , 7 8 4 0 ) = 2 7 . 7 7

F ( 3 5 , 7 8 4 0 ) = 2 5 . 1 2

F ( 3 5 , 7 8 4 0 ) = 2 7 . 0 5

0 . 0 9 8

0 . 1 0 6

0 . 0 9 9

0 . 1 0 6

l e s i n d i c a t e l e v e l s o f s t a t i s t i c a l l y s i g n i fi

c a n c e : a

= 1 % ,

b

= 5 % , a n d c =

1 0 %

58 J Fam Econ Iss (2007) 28:49–67



Table 3 Maximum-likelihood estimates of simultaneous equations of BMI and cigarettes

Variable Cigarettes BMI

Estimates SE Estimates SE

Household characteristicsCity –0.845 1.076 –0.435b 0.170Suburban –1.324 0.991 –0.245c 0.150South 0.791 1.077 –0.058 0.168Northeast –1.634 1.246 0.066 0.188Midwest –0.056 0.998 0.403b 0.179Food stamps 2.124 1.483 1.324a 0.340Income –0.045 0.043 –0.007 0.005HH Size –1.158a 0.313 0.016 0.057Personal characteristicsAge 1.825a 0.159 0.310a 0.023Age2/100 –2.048a 0.162 –0.297a 0.023Education –1.400a 0.152 –0.066a 0.025Male 1.930b 0.877 0.365a 0.133Hispanic –10.080a 2.382 0.865b 0.350Other race –3.646b 1.704 –0.582b 0.279Black –1.967 1.275 1.481a 0.234Employed 0.840 1.029 0.054 0.165Active work 6.117a 1.056 –0.030 0.144Exercise –3.993a 0.794 –0.370a 0.120TV hours 0.878a 0.177 0.163a 0.035Vegetarian –2.475 2.608 –1.548a 0.304Home owner –6.330a 0.935 –0.174 0.156

Food/nutrient intakesVitamins –1.489c 0.805 –0.550a 0.120Bev. Ratio –2.093 1.334 0.363 0.224Beverages 0.739a 0.061 0.011 0.011Low-fat milk –4.146a 0.895 0.791a 0.134Alcohol 9.020a 0.935 –0.805a 0.140Wine –1.201c 0.737 –0.371a 0.085Fruits –2.580a 0.342 –0.015 0.028Grains –0.878a 0.212 0.000 0.030Sugar 0.536 1.087 –0.115 0.147Milk 0.212 0.181 –0.010 0.024Eggs 2.102b 0.945 0.309b 0.155

Meats 0.206 0.233 0.099a

0.037Legumes –0.207 0.598 –0.215a 0.079Quit smoking 0.335b 0.143C 0.015 0.019Constant –36.146a 4.760 19.769a 0.703Error std. dev. (r) 25.220a 0.421 5.018a 0.078R –0.177a 0.049Log-likelihood –35,339.218

Note: Variables indicate levels of statistically significance: a = 1%, b = 5%, and c = 10%

J Fam Econ Iss (2007) 28:49–67 59



Most of the signs for the other coefficients are as anticipated and consistent acrossmodels. Among the household characteristics in the BMI equation, people whoresided in the Midwest and those with household members in the food stamp pro-gram are more likely to have a high BMI than the reference group, which includes

the residents of the Western states. Compared to individuals from rural areas,individuals residing in the cities are less likely to be overweight. Individual charac-teristics also have strong effects on BMI. Age has a nonlinear effect. Ethnicity andrace also play a role, with Hispanic and Black respondents being more likely to havehigh BMI. Consistent with common belief about lifestyle, exercise has a negativeeffect whereas time watching TV or playing video games has a positive effect onBMI. Vegetarians have lower BMI, which may reflect higher levels of self-control.Men are more likely to have higher BMI than women, while respondents with higherlevel of education tend to have lower BMI. This is likely related to the well-knownbias of self-reported weight and height. Men tend to over-report their weight while

women may under-report.3

Among the food/nutrient intake variables, the preference for low-fat milk has apositive effect on the BMI, which echoes the puzzle reported by Lin et al. (2004).Our conjecture is that either the use of low-fat milk increases the total amount of milk consumed or overweight respondents are more concerned with the fat content.Those who take vitamin supplements regularly and those who had used alcohol inthe past year are more likely to have lower levels of BMI. More intakes of wine andlegumes are associated with lower BMI while intakes of beverages, eggs, and meatsare associated with higher levels of BMI. This sheds light on what diet pattern might

be preferred for the purpose of weight control.Also reported in Table 3 are estimates of the cigarette equation. Respondentswho are members of larger households smoke less than those from smaller house-holds. Age has a nonlinear effect on the number of cigarettes smoked, as evidencedby significance of age and its squared term. Education reduces the number of cigarettes smoked. Men smoke more than women. Respondents of different race orgender groups do display significant variations in cigarette consumption. Hispanicssmoke less than non-Hispanics, as do Black and individuals of other races thanWhite. Work that involves heavy physical labor increases the number of cigarettessmoked. Time watching TV or playing video games is associated with increased

smoking, as are use of beverages and alcohol and consumption of fruits, grains, andwine. Home ownership, active exercises, consumption of low-fat milk and frequentuse of vitamin supplements are associated with reduced smoking.

The marginal effects (changes in BMI with respect to changes in the explanatoryvariables) of the variables are presented in Table 4. The unconditional marginaleffects are evaluated at the means of the variables for the whole sample. Themarginal effects conditional on smoking and non-smoking are evaluated at themeans of explanatory variables for the corresponding samples. The unconditionalmarginal effects are not significantly different from the coefficient estimates in the

BMI equation. In addition, there are notable differences between the unconditionaland conditional effects, i.e., the effect of being Hispanic is insignificant for thesmoker sample while significant for the non-smokers and the whole sample.4

60 J Fam Econ Iss (2007) 28:49–67



Implications

A brief comparison of our results with previous studies on the smoking–overweight

relationship is made in Appendix Table 1. Except for Ruidavets, Bongard, Bataille,Gourdy, and Ferrie ` res (2002), most previous studies concluded there was a negative

l ti n hi Th in l ti n hi b t n m kin nd BMI b d in th

Table 4 Marginal effects of explanatory variables

Variable Unconditional Conditionalon Smoking

Conditional on Non-smoking

Estimates SE Estimates SE Estimates SE

Continuous variablesIncome –0.007 0.005 –0.008 0.005 –0.006 0.005HH size 0.012 0.056 –0.018 0.056 0.023 0.057Age 0.149a 0.011 0.195a 0.013 0.134a 0.011Education –0.070a 0.024 –0.106a 0.027 –0.057b 0.026TV hours 0.166a 0.035 0.189a 0.035 0.158a 0.036Bev. Ratio 0.356 0.223 0.302 0.224 0.376c 0.225Beverages 0.014 0.009 0.033a 0.009 0.007 0.011Wine –0.375a 0.085 –0.406a 0.087 –0.363a 0.085Fruits –0.023 0.027 –0.090a 0.035 0.002 0.030Grains –0.003 0.029 –0.025 0.030 0.006 0.030Sugar –0.113 0.146 –0.100 0.145 –0.119 0.148Milk –0.009 0.024 –0.003 0.025 –0.011 0.024Eggs 0.316b 0.154 0.370b 0.154 0.296c 0.156Meats 0.100a 0.037 0.105a 0.037 0.098a 0.037Legumes –0.215a 0.079 –0.221a 0.080 –0.213a 0.080Discrete variablesCity –0.437b 0.170 –0.459a 0.169 –0.447a 0.169Suburban –0.249c 0.149 –0.283c 0.149 –0.264c 0.148South –0.056 0.168 –0.035 0.167 –0.047 0.167Northeast 0.061 0.187 0.018 0.185 0.043 0.185Midwest 0.402b 0.178 0.401b 0.178 0.402b 0.178

Food stamps 1.331

a

0.339 1.385

a

0.335 1.355

a

0.337Male 0.371a 0.132 0.421a 0.131 0.392a 0.131Hispanic 0.839b 0.349 0.566 0.373 0.740b 0.352Other race –0.594b 0.275 –0.689b 0.272 –0.632b 0.275Black 1.475a 0.233 1.424a 0.230 1.454a 0.231Employed 0.057 0.165 0.079 0.163 0.066 0.163Active work –0.011 0.143 0.149 0.154 0.056 0.143Exercise –0.383a 0.118 –0.486a 0.120 –0.427a 0.117Vegetarian –1.555a 0.302 –1.620a 0.298 –1.582a 0.298Homeowner –0.197 0.150 –0.357b 0.155 –0.268c 0.151Vitamins –0.555a 0.119 –0.593a 0.118 –0.571a 0.119Low-fat milk 0.778a 0.131 0.670a 0.133 0.733a 0.131

Alcohol –0.777a

0.136 –0.540a

0.158 –0.680a

0.137Note: Marginal effects are evaluated at the sample means of all explanatory variables for the fullsample, smokers, and non-smokers, respectively. Variables indicate levels of statistically significance:a = 1%, b = 5%, and c = 10%

J Fam Econ Iss (2007) 28:49–67 61



individual heterogeneity (personal characteristics, physical conditions, etc.).Regressing BMI on the number of cigarettes smoked does not allow for thesimultaneity of cigarette smoking and obesity, and thus the estimates of coefficientsare likely to be biased and inefficient. Second, most of the literature either ignored

the zero-consumption problem or did not account for the effects of increased ciga-rette consumption. Chou et al. (2004) used cigarette price in their study of the effectof an anti-smoking campaign, which might induce the zero consumption problem.Those who do not smoke because the cigarette market price is higher than theirreservation prices will continue to abstain from smoking. A small portion of thesmokers will stop smoking after the cigarette price hike. Studies that treat smokingas a binary variable neglect the quantitative information of cigarettes smoked. Thepresent study addresses these problems by treating the number of cigarettes smokedas an endogenous decision and by accommodating censoring in the variable assuggested in Rigobon and Stoker (2003).

The implication of this study is that the negative relationship between smokingand BMI obtained through OLS should be interpreted with caution. The relationshipbetween anti-smoking campaigns and the prevalence of obesity may be spurious dueto the endogeneity of smoking. Most of the medical experiments which had shownthat smoking has a temporary weight-reducing effect only considered the short-termchange of weight or energy consumption. Smoking decisions in these studies weremade to be exogenous while in the long run it is not so. The result of this studysuggests cigarette consumption may not have a direct long-run effect on body weightafter controlling for endogeneity. There is no support for using cigarette smoking as

a means of weight control.

Appendix A Derivation of the likelihood function

Denote z1 ¼ ðC À w0aÞ=r1 and z2 ¼ ½ y À ð x0b þ cC Þ=r2, the likelihood contributionfor the regime with C = 0 is

gðu2ÞZ Àw0a

À1

hðu1ju2Þdu1 ¼ rÀ12 /ðz2ÞU

z1 À qz2

ffiffiffiffiffiffiffiffiffiffiffiffiffi1Àq2p

The integral in the above follows from the bivariate normality of u1 and u2 andproperties of the conditional pdf of u1 given u2. The likelihood contribution for theregime C > 0 is simply the bivariate normal pdf. Therefore, the sample likelihoodfunction is

L ¼YC ¼0

rÀ12 /ðz2ÞU

z1 À qz2

ffiffiffiffiffiffiffiffiffiffiffiffiffi1 À q2

p

ÂY

C [0

rÀ11 rÀ12 12p

ffiffiffiffiffiffiffiffiffiffiffiffiffi1 À q2

p exp 12ð1 À q2Þ

ðz21 À 2qz1z2 þ z22Þ !

:

62 J Fam Econ Iss (2007) 28:49–67



Appendix B Conditional Means of the Dependent Variable and the Marginal

Effects of Explanatory Variables

Takingtheexpectation of y inequation(2)conditionalonsmoking( C >0),wehavethat

E ð yjC [0Þ ¼ x0b þ cE ðC jC [0Þ þ E ðu2ju1[À w0aÞ

¼ x0b þ cw0a þ cE ðu1ju1[À w0aÞ þ E ðu2ju1[À w0aÞ

¼ x0b þ cw0a þ c r1/ðw0a=r1Þ

Uðw0a=r1Þ

!þ q

r2

r1r1

/ðw0a=r1Þ

Uðw0a=r1Þ

!

¼ x0b þ cw0a þ ðc r1 þ qr2Þ/ðw0a=r1Þ

Uðw0a=r1Þ

!:

ðB:1Þ

In (B.1), the third equality follows from properties (truncated moments) of the

univariate and bivariate normal distributions. Following a similar procedure, themean of y conditional on non-smoking (C =0) is

E ð yjC ¼ 0Þ ¼ x0b þ cð0Þ þ E ðu2ju1 Àw0aÞ ¼ x0b À qr2/ðÀw0a=r1Þ

UðÀw0a=r1Þ

!: ðB:2Þ

Using equations (B.1) and (B.2), and recognizing that PrðC [0Þ ¼ Uðw0a=r1Þ; the‘‘unconditional’’ mean of y (though conditional on exogenous variables) is

E ð yÞ ¼ x0b þ c r1 /ðw0a=r1Þ þ cw0aUðw0a=r1Þ: ðB:3Þ

Differentiating the expectations in equations (B.1), (B.2) and (B.3) with respectto a common element of x and w (say x j = w j), we have

@ E ð yjC [0Þ

@ x j

¼ b j þ ca j þ c þ qr2

r1

r1

Â À/ðw0a=r1Þ

Uðw0a=r1Þ

2a j

r1

À/ðw0a=r1Þ

Uðw0a=r1Þ

w0a

r1

a j

r1

" #

¼ b j þ ca j À a j c þ qr2

r1

/ðw

0

a=r1ÞUðw0a=r1Þ

!2

þ /ðw0

a=r1ÞUðw0a=r1Þ

!w

0

ar1

( )ðB:4Þ

@ E ð yjC 0Þ

@ x j

¼ b j À qr2

r1r1 À

/ðÀw0a=r1Þ

UðÀw0a=r1Þ

!2 Àa j

r1

(

À/ðÀw0a=r1Þ

UðÀw0a=r1Þ

!Àw0a

r1

Àa j

r1

'

¼ b j À qr2

r1

a j

/ðw0a=r1Þ

UðÀw0a=r1Þ !

2

þ/ðw0a=r1Þ

UðÀw0a=r1Þ ! w0a

r1 ( ) ðB.5Þ

J Fam Econ Iss (2007) 28:49–67 63



a b l e 1

C o m p a r i s o n t o p r e v i o u s s m o k i n

g - o v e r w e i g h t s t u d i e s

D a t a

M e t h o d

R e s u l t

I m p l i c a t i o n

C S F I I 1 9 9 4 - 9 6 , 7 , 8 7 6 a d u l t s

O L S

S t a t i s t i c a l l y s i g n i fi c a n t n e g -

a t i v e c o e f fi c i e n t o f c i g a r e t t e

c o n s u m p t i o n .

E n d o g e n e i t y c

o n t r i b u t e d t o

t h e

n e g a t i v e

r e l a t i o n s h i p

b e t w e e n c i g a r e t t e c o n s u m p -

t i o n a n d B M I

e s t i m a t e d i n

p r e v i o u s s t u d

i e s . S m o k i n g

s h o u l d n o t b e s u g g e s t e d a s a

m e a n s o f w e i g

h t c o n t r o l .

S i m u l t a n e o u s e q u a t i o n

s s y s t e m

S t a t i s t i c a l l y i n s i g

n i fi c a n t

p o s i t i v e c o e f fi c i e

n t o f

c i g a r e t t e c o n s u m

p t i o n .

( 2 0 0 4 )

B R F S S 1 9 8 4 – 1 9 9 9 ( t h e

B e h a v i o r a l R i s k F a c t o r

S u r v e i l l a n c e S y s t e m ) ,

1 , 1 1 1 , 0 7 4 a d u l t s

O L S

S t a t i s t i c a l l y s i g n

i fi c a n t p o -

s i t i v e e l a s t i c i t y o f B M I w i t h

r e s p e c t t o c i g a r e

t t e p r i c e .

A n t i - s m o k i n g

c a m p a i g n s

c o n t r i b u t e d t o

t h e u p w a r d

t r e n d i n o b e s i t y .

0 0 4 )

C S F I I 1 9 9 4 – 1 9 9 6 , 2 , 4 1 9

a d u l t w o m e n & 1 , 6 5 1

s c h o o l - a g e c h i l d r e n

W e i g h t e d M u l t i v a r i a t e

R e g r e s s i o n o f B M I

N e g a t i v e a n d s t a

t i s t i c a l l y

s i g n i fi c a n t c o e f fi c i e n t

o f s m o k e r d u m m

y v a r i a b l e .

S m o k e r s t e n d

t o b e t h i n n e r

t h a n n o n - s m o k e r s .

. ( 2 0 0 2 )

8 8 n u r s i n g h o m e r e s i d e

n t s

i n S t . L o u i s

S u r v e y w i t h s i x m o n t h f o l l o w -

u p ,

l o g i s t i c r e g r e s s i o n

S m o k e r s h a d a l o w e r B M I

o n a d m i s s i o n . W

e i g h t g a i n

o c c u r r e d a t a s l o

w e r r a t e i n

s m o k e r s

c o m p a r e d

w i t h

n o n - s m o k e r s .

S m o k i n g c e s s a

t i o n b e

e n c o u r a g e d a s

a n a d j u n c t t o

n u t r i t i o n a l i n t e r v e n t i o n

i n

n u r s i n g h o m e

r e s i d e n t s

w i t h n i c o t i n e d e p e n d e n c e

a n d w e i g h t p r o b l e m s .

0 0 2 )

K o r e a n N a t i o n w i d e H e a

l t h

E x a m i n a t i o n S u r v e y , 3 , 4

5 0

m e n a n d 4 , 2 5 0 w o m e n

L o g i s t i c r e g r e s s i o n

F o r b o t h m e n a n d w o m e n ,

t h e r i s k f o r p a r a d o x A ( l o w

B M I a n d

h i g h

w a i s t - h i p

r a t i o ) w a s h i g h

e r a m o n g

s m o k e r s t h a n n o

n - s m o k e r s .

S m o k i n g m a y

i n c r e a s e t h e

r i s k f o r p a r a d o x A ; s m o k e r s

a r e t h u s m o r e

l i k e l y t o h a v e

d i a b e t e s m e l l i t u s .

64 J Fam Econ Iss (2007) 28:49–67



a b l e 1

c o n t i n u e d D

a t a

M e t h o d

R e s u l t

I m p l i c a t i o n

a l .

3 3 0 f r e e - l i v i n g F r e n c h

m e n

( 4 5 – 6 4 y )

M u l t i p l e l i n e a r r e g r e s s i o n

P o s i t i v e c o e f fi c i e

n t s f o r

p a s t / c u r r e n t s m o

k i n g d u m -

m y v a r i a b l e s .

M e n w h o h a d

n e v e r

s m o k e d h a d a

l o w e r w a i s t –

h i p r a t i o o r B

M I t h a n c u r -

r e n t s m o k e r s

o r f o r m e r

s m o k e r s .

( 2 0 0 0 )

1 4 , 7 7 9 w o m e n ( 1 8 –

2 3 y e a r s ) i n t h e A u s t r

a l i a n

L o n g i t u d i n a l S t u d y s u r v e y

i n 1 9 9 6

S u m m a r y s t a t i s t i c s

G r e a t e r r e p r e s e n

t a t i o n o f

s m o k e r s i n t h e t w o l o w e s t

B M I c a t e g o r i e s .

S m o k i n g i s a s s o c i a t e d w i t h

l o w e r B M I .

a l . ( 1 9 9

5 )

4 0 f e m a l e s m o k e r s ( 2 0

n o r m a l w e i g h t a n d 2 0

o b e s e )

M e d i c a l e x p e r i m e n t

S m o k i n g i n c r e a s e d r e s t i n g

e n e r g y e x p e n d i t u r e i n b o t h

w e i g h t c a t e g o r i e s b u t t h e

e x t e n t d i f f e r e d .

O b e s e p e r s o n s s h o u l d b e

d i s c o u r a g e d f r o m t a k i n g u p

s m o k i n g t o c o

n t r o l w e i g h t .

J Fam Econ Iss (2007) 28:49–67 65



References

Abdel-Ghany, M., & Wang, M. Q. (2003). Contemporaneous and intertemporal relationshipbetween the consumption of licit and illicit substances by youth. Journal of Family andEconomics Issues, 24, 281–289.

Audrain, J. E., Klesges, R. C., & Klesges, L. M. (1995). Relationship between obesity and themetabolic effects of smoking in women. Health Psychology, 14, 116–123.

Barnow, B., Cain, G., & Goldberger, A. (1981). Issues in the analysis of selectivity bias. EvaluationStudies Review Annual , 5, 43–59.

Brown, W. J., Mishra, C., Kenardy, J., & Dobson, A. (2000). Relationships between body mass indexand well-being in young Australian women. International Journal of Obesity, 24, 1360–1368.

Chen, Z., Yen S. T., & Eastwood D. B. (2005). Effects of food stamp participation on body weightand obesity. American Journal of Agricultural Economics, 87 , 1167–1173 .

Chou, S., Grossman, M., & Saffer, H. (2004). An economic analysis of adult obesity: Results from thebehavior risk factor surveillance system. Journal of Health Economics, 23, 565–587.

Dardis, R., & Keane, T. (1995). Risk-benefit analysis of cigarette smoking: Public policy implica-tions. Journal of Consumer Affairs, 29, 351–367.

Field, A. E., Gillman, M. W., Rosner, B., Rockett, H. R., & Colditz, G. A. (2003). Associationbetween fruit and vegetable intake and change in body mass index among a large sample of children and adolescents in the United States. International Journal of Obesity, 2, 821–826.

Gutie rrez-Fisac, J. L., Rodrı guez Artalejo, F., Guallar-Castillon, P., Banegas, J. R., & de Rey Calero,J. (1999). Determinants of geographical variations in body mass index (BMI) and obesity inSpain. International Journal of Obesity, 23, 342–347 .

Hamilton, V. H., Merrigan, P., & Dufresne, E. (1997). Down and out: Estimating the relationshipbetween mental health and unemployment. Health Economics, 6, 397–406.

Hedley, A. A., Ogden, C. L., Johnson, C. L., Carroll, M. D., Curtin, L. R., & Flegal, K. M. (2004).Prevalence of overweight and obesity among U.S. children, adolescents, and adults, 1999–2002. Journal of American Medical Association, 29, 2847–2850.

Jee, S. H., Lee, S. Y., Nam, C. M., Kim, S. Y., & Kim, M. T. (2002). Effect of smoking on the paradoxof high waist-to-hip ratio and low body mass index. Obesity Research, 10, 891–895.

Kan, K., & Tsai, W. (2004). Obesity and risk knowledge. Journal of Health Economics, 23, 907–934.Kennedy, E. T., Bowman, S. A., Spence, J. T., Freedman, M., & King, J. (2001). Popular diets:

Correlation to health, nutrition, and obesity. Journal of the American Dietetic Association, 101,411–420.

Klesges, R. C., & Klesges, L. M. (1988). The use of cigarette smoking as a weight loss strategy in auniversity population. International Journal of Eating Disorders, 7 , 413–419.

Kuchler, F., & Variyam, J. N. (2003). Mistakes were made: Misperception as a barrier to reducingoverweight. International Journal of Obesity, 27 , 856–861.

Laitinen, J., Power, C., Ek, E., Sovio, U., & Ja ¨ rvelin, M. R. (2002). Unemployment and obesityamong young adults in a northern Finland 1966 birth cohort. International Journal of Obesity, 26,1329–1338.

Lin, B., Huang, C. L., & French, S. A. (2004). Factors associated with women’s and children’s bodymass indices by income status. International Journal of Obesity, 28, 536–542.

Macdiarmid, J. I., Vail, A., Cade, J.E., & Blundell, J. E. (1998). The sugar-fat relationship revisited:Differences in consumption between men and women of varying BMI. International Journal of Obesity, 22, 1053–1061.

Mokdad, A. H., Ford, E. S., Bowman, B. A., Dietz, W. H., Vinicor, F., Bales, V. S., & Marks, J. S.(2003). Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. Journal of American Medical Association, 289, 76–79.

Nayga Jr., R. M. (2000). Schooling, health knowledge and obesity. Applied Economics, 32, 815–822.Paeratakul, S., Lovejoy, J. C., Ryan, D. H., & Bray, G. A. (2002). The relation of gender, race and

socioeconomic status to obesity and obesity comorbidities in a sample of U.S. adults. Interna-

tional Journal of Obesity, 26, 1205–1210.Perkins, K. A., Hickcox, M. E., & Grobe, J. E. (2000). Behavioral economics of tobacco smoking. InW. K. Bickel, & R. E. Vuchinich (Eds.), Reframing health behavior change with behavioral

( )

66 J Fam Econ Iss (2007) 28:49–67



Sharpe, D. L., Abdel-Ghany, M., Kim, H. Y., Hong, G. S. (2001). Alcohol consumption decision inKorea. Journal of Family and Economics Issues, 22, 7–24.

Sun, H. (2003). Physical and mental health of contemporary Chinese children. Journal of Family andEconomics Issues, 24, 355–364.

Townsend, M. S., Peerson, J., Love, B., Achterberg, A., & Murphy, S. P. (2001). Food insecurity is

positively related to overweight in women. Journal of Nutrition, 131, 1738–1745.US Department of Agriculture, Agricultural Research Service (USDA-ARS). (2000). ContinuingSurvey of Food Intakes by Individuals 1994–1996, CD-ROM, Washington, DC.

White, H. (1982). Maximum likelihood estimation of misspecified models. Econometrica, 50, 1–26.Wilson, M. M. G., Habib, S., & Philpot, C. (2002). Cigarette smoking and weight loss in nursing

home residents. The International Journal of Medicine–Medicine On-Line. URL http://www.priory.com/med/cigsmoking.htm (Accessed 1 November 2004).

Wooldridge, J. M. (2002a). Econometric analysis of cross section and panel data. Cambridge, MA:MIT Press.

Wooldridge, J. M. (2002b). Introductory Econometrics. Mason, OH: Thomson South–Western.Yen, S. T. (1999). Gaussian versus count-data hurdle models: Cigarette consumption by women in

the US. Applied Economics Letters, 6, 73–76.

Yen, S. T. (2005). A multivariate sample-selection model: Estimating cigarette and alcohol demandswith zero observations. American Journal of Agricultural Economics, 87 , 453–466.

Yen, S. T., & Jones, A. M. (1996). Individual cigarette consumption and addiction: A flexible limiteddependent variable approach. Health Economics, 5, 105–117.

J Fam Econ Iss (2007) 28:49–67 67



Reproducedwithpermissionof thecopyrightowner. Further reproductionprohibitedwithoutpermission.

Documents

BMI & Smoking