SELF-ASSESSED HEALTH AND CIGARETTE SMOKING IN CHINA

Transcription

1 SELF-ASSESSED HEALTH AND CIGARETTE SMOKING IN CHINA W. DOUGLASS SHAW a, * STEVEN T. YEN b, and YAN YUAN b a Department of Agricultural Economics and Department of Recreation, Park, and Tourism Sciences Texas A&M University TAMU 2124 / Blocker Building College Station, TX , USA wdshaw@tamu.edu b Department of Agricultural Economics 308D Morgan Hall University of Tennessee Knoxville, TN , USA The authors thank Thomas McGuire for his comments on an earlier version of this manuscript, and Paul Jakus, Mary Riddel, and V. Kerry Smith for valuable comments on a related paper on smoking behavior. Authorship is alphabetical. Shaw is also Research Fellow, Hazard Reduction and Recovery Center at A&M, and acknowledges support from the W-2133 U.S.D.A./Hatch Project. * Corresponding author. address: wdshaw@ag.tamu.edu (W.D. Shaw).

2 1 SELF-ASSESSED HEALTH AND CIGARETTE SMOKING Abstract Self-assessed health (SAH) is often elicited in surveys in lieu of expensive health assessments made by medical professionals. Measures like this are quite often used in explaining decisions to undertake various activities, but their validity as an exogenous variable has been questioned. If the SAH is improperly treated as an exogenous variable this could bias other coefficients (e.g. price) that are important for policies that attempt to curb smoking. This paper takes a first step in sorting out the relationship between SAH and smoking behavior: we examine whether the variation in SAH can be explained as an endogenous dependent variable. An ordinal endogenous treatment is modeled with an ordinal smoking variable, using the copula approach to accommodate a non-gaussian distribution in the error terms. The treatment approach avoids a wide variety of selection issues that could bias coefficients. The empirical model is estimated for a random sample of the adult males from nine Chinese provinces in the 2006 China Health and Nutrition Survey. For our sample the SAH has negative effects on the probabilities of smoking heavily, and such negative effects are most pronounced in the effect of the higher health category. Keywords: China; Self-assessed health; Smoking JEL Classification: I10, C31 ABBREVIATIONS ATE: Average treatment effect CHNS: China Health and Nutrition Survey SAH: Self-assessed health

3 2 I. INTRODUCTION In this paper we examine the factors contributing to, and the relationship between selfassessed health (SAH) and cigarette smoking using a sample of men living in nine Chinese provinces, obtained from the 2006 China Health and Nutrition Survey (CHNS). Cigarette smoking is the single most preventable cause of death in the world today. Worldwide it kills one person every six seconds and causes 1 in 10 deaths among adults, more than five million people annually (Mathers and Loncar 2006; WHO 2008). Smoking not only causes premature deaths but also leads to several diseases which may not necessarily kill a person but affect health, such as chronic bronchitis, mucus hypersecretion, bladder cancer, and peptic ulcer disease (Samet 2001). Yet, cigarette or tobacco use remains common throughout the world, with many countries having in excess of a quarter of its adult population as smokers. Cigarette smoking behavior in lower-income countries including China has received little attention from economists (Lance et al. 2004), as compared to studies in other countries, with some exceptions such as Taiwan (Liu and Hsieh 1995; Hsieh 1998). With more than 320 million smokers consuming 30% of the world s cigarette production, China is the largest producer and consumer of tobacco (Mackay 1997; WHO 2008). Per capita cigarette consumption in China rose dramatically from the early 1970s to the early 1990s, and deaths due to second-hand smoke are estimated to be over 100,000 per year (China Ministry of Health 2007). Most often one thinks about smoking causing a decline in health, eventually leading to morbidity or death. From a policy perspective, governments and non-profit groups try to get people to quit smoking using new information campaigns, bans in public places, curtailing sales to minors, or imposing cigarette taxes that affect price. The effectiveness of any of these approaches is debatable, and the tax policies relate to the price elasticity of demand for smoking

4 3 (see discussions in Chalupka 1991; Chalupka and Warner 2000; Lance et al. 2004). Omitted variables and other mis-specifications such as simultaneity in the model can lead to statistical biases, affecting parameters that relate to elasticity estimates that are important in determining the effectiveness of any tax or pricing policy. To control for the effect that a person s health has on the decision to smoke, or how much to smoke, the models often include some measure of the individual s health. This is done to avoid a potentially important omitted variable, with the thought that a person s baseline health is correlated with smoking behavior. In this paper we explore whether health is an exogenous variable, and apply a treatment approach that can avoid selection bias issues for many of the important variables. Over the long term individuals can of course change behaviors to affect their own health, so strictly speaking, health levels are endogenous to some extent. Clearly, people can adopt better eating habits, get exercise, take medications, get medical treatments and reduce their intake of alcohol and tobacco, all leading to better health. While it is more difficult for agencies to directly influence individuals health than some other smoking factors, it is possible to do so via public information campaigns. Thus, it is important to examine the nature of the measure of health. It is expensive to obtain medical professionals assessments of a subject s health condition and quite often survey questionnaires or interviews simply ask individuals to rate or assess their own health on a scale, yielding the SAH. The question typically is phrased something like, On a scale from 1 (poor) to 4 (excellent), how would you rate your health? When respondents can see the question (in mail, internet, or in-person surveys) they might be instructed to circle one of the four discrete (ordinal) responses, i.e., numbers on a Likert scale. This type of question is asked in many instances where health issues might be important, but

5 4 where the researchers are hoping to avoid the higher costs of determinations of an individual s health by medical experts, via physical examinations or more lengthy questionnaires. SAH responses are used as independent variables in models of smoking and other activities or behaviors that might be affected by health, with the underlying hypothesis being that the SAH reveals a causal relationship for changes in the activities. For example, Khwaja, Sloan and Chung (2006) explore the role of the SAH for a subject and his or her spouse in their study of smoking decisions. Again, the thought here is that people who say their health is poor might be less likely to engage in activities requiring physical strength, stamina, or ability and one might think that those saying their health is poor might not smoke, or if they once did, they may have quit smoking. Still, current smokers might not quit even if they believe themselves to be in poor health, perhaps because they do not see the connection between health and smoking, and perhaps because of smoking addiction (see Chaloupka 1991, who finds empirical support for the rational addiction hypothesis). It could also be the case that people in poor health believe they have less to gain, in comparison to being healthier, by quitting smoking. Thus, the relationship between health status and smoking behavior is an empirical issue. It is here that we focus our attention. We link an ordinal model for an SAH variable that is assumed endogenous, to an ordinal smoking participation equation. We use a very general econometric model that accommodates dependent and potentially skewed error distributions using the copula approach. According to a China national survey conducted in 2002, 29.8% of the 3,993 respondents are smokers. Only 3.5% of the 2,036 female respondents smoke whereas 57.3% of the male respondents are smokers and 45.7% smoke daily: in China, smoking is largely an activity for males. Because female smoking rates are negligible compared to those of men, this paper

6 5 investigates the demand for cigarettes in China by men using micro-level analysis. It permits more extensive examination of variation in price sensitivity across demographic subgroups and allows for inter-community variation. In addition, we carry out the empirical analysis using a generalized ordinal treatment effects model with the copula approach. The remainder of the paper is organized as follows. First, we briefly review some related literature on the SAH measure and selected studies of smoking behavior. Then we describe the CHNS data and report simple statistics in section 3. The empirical model is featured in section 4, followed by estimation results in section 5. The final section concludes this paper. II. REVIEW OF EMPIRICAL LITERATURE A. Self-assessed Health To begin, we first consider several studies that explore SAH in detail. There are a host of reasons why a simple measure of SAH may be problematic and several studies have examined many of these. For example, Case and Paxson (2005) note that women may actually be healthier than men, but report worse health during surveys or interviews. Others have found gender differences in SAH measures (e.g., Idler 2003). This might be because they more accurately assess their own health than men, or some believe they are less stoic, and consider minor health issues to be more serious. While SAH measures have been found to be valid indicators of health (see, e.g., review on actual mortality rates and the SAH by Idler and Benyamini 1997), there is also evidence of measurement error in them (see Butler et al. 1987), causing some to question, in general, what information the SAH really provides (see Baker, Stabile, and Deri 2004). Several health researchers have questioned whether SAH measures are in fact exogenous variables in various studies of behavior, including explaining the decision to retire (Dwyer and Mitchell 1999). Similarly, Khwaja, Sloan, and Salm (2006) specifically exclude SAH measures

7 6 in their analysis of whether smokers are more impatient, or more risk tolerant, than non-smokers, doing so because of the potential endogeneity in this measure of health. Some researchers have examined possible factors that lead to heterogeneity in the SAH (see, e.g., reviewed by Etilé and Milcent (2006) of studies indicating that individuals with lower incomes are more likely to report poor health levels than higher income groups; they also find evidence of income-related reporting heterogeneity in their own study). Van Doorslaer and Jones (2003) estimate several models of a typical SAH response, using procedures that impose cardinality or specifically allow for an ordinal nature of the usual data. As many do, they consider the ordered-probit specification in explaining responses such as those corresponding to their 1 (poor) to 5 (excellent) scale. Au, Crossley, and Shellhorn (2005) explore differences in the impact of health on employment status by using simple reported SAH measures versus a measure adjusted for endogeneity, finding that the simple approach underestimates this health impact. Similarly, Campolieti (2002) and Cai and Kalb (2006) reject the hypothesis that self-rated health is exogenous. Moore and Zhu (2000) also question whether the usual kind of SAH measures are exogenous variables that might be used in assessing health impacts related to passive smoking. They state that there is little in the health economics literature on the use of these measures as dependent variables, but note that this possibility is more commonly found in the more general literature related to health (e.g., McDowell and Newell 1996) than in economic analysis. Moore and Zhu (2000) hypothesize that in theory, there is a latent subjective health index that in turn depends on subjective expected utility, which involves the individual s perception of health risk. Thus, theoretically, the self-reported measure could fundamentally depend on risk perceptions. In

8 7 using the self-reported measures as explanatory variables of perceived risks in regressions, the researcher would be erroneous in assuming that the measure is an exogenous variable. As they note (ibid), errors in the perception of risk will lead to measurement error in the dependent variable, the self-reported health measure. More recently, Ding et al. (2007) consider the role of endogenous health outcomes and interactions with risky behaviors, along with academic achievement. They find that genetic markers make good instruments in modeling the effect of health on academic performance during adolescent years. B. Cigarette Smoking The literature on cigarette smoking is much too extensive to address here (see Viscusi, 1992 for an extensive discussion). Researchers have dealt with a host of issues for over twentyfive years, but much of the detailed micro-level research has been done in the U.S. or in a European country. Many smoking studies conducted in developing countries rely on aggregate data (Chapman and Richardson 1990), and aggregate data have been used to examine the demand for cigarettes in China (e.g., Mao and Xiang 1997; Xu, Hu, and Keeler 1998). These studies are subject to the limitations and empirical concerns normally associated with aggregate data: explanatory variables are often highly collinear and there can be possibly substantial simultaneity. In addition, aggregate data do not generally provide information for demographic subgroups. For instance, Lance et al. (2004) argue that modest overall smoking rates in poorer societies often disguise high incidence for certain subgroups among whom consumption is highly concentrated. Furthermore, studies fail to incorporate price variation below the national level, which can be considerable in developing nations (Lance et al. 2004). A few papers have used micro-level data to study cigarette demand in China (Lance et al. 2004; Mao and Xiang 1997), and smoking status in Taiwan (Hsieh 1998; Liu and Hsieh 1995).

9 8 Several important issues in micro-data modeling of smoking like these are summarized in Sloan, Smith, and Taylor (2003), and earlier, by Jones (1994). These include whether smokers are addicts, and if so, are they rational in their addiction (e.g., Chaloupka 1991), and as in the Taiwan studies, the role that risks perceptions play (which build on earlier work by Viscusi 1990) in decisions to smoke. As we do not have data on perceived risks, the large number of studies that deal with those are not reviewed here. The connections between smoking and health status are complicated. First, perhaps unlike some scholars or other disciplines, economists consider several possible gains from smoking as being factors that rational individuals will consider. Smoking provides pleasure, and many smokers indicate that they feel more relaxed while smoking than when not: a common sentiment is that smoking reduces their feelings of stress that might be related to a job, personal, or economic situation. In an early exploration of smoking and health status, Blaylock and Blisard (1992) consider the possibility of simultaneity between health status, the decision to be a current smoker, to quit, and the quantity of cigarettes consumed. The SAH variable these authors use was binary (health is simply good, or not); their smoking participation and quit decision variables are also subsequently binary. Using a sample of women from the Continuing Survey of Food Intakes by Individuals in the U.S., they found SAH did not influence the probability of smoking or quitting. Jones (1994) noted that the smoking-health relationship is potentially obscured by unobservable variables. This includes such variables as a measure of the above-mentioned level of stress. For example, if one has a genetic predisposition toward anxiety, she might feel stressed and smoke because of that personal trait, which may be quite difficult to measure, but which may

10 9 be correlated with an observed health measure. He finds that individuals with poor or fair SAH are less likely to have quit smoking than those in better health. 1 III. DATA AND SAMPLE The data used for this analysis come from the 2006 CHNS. The survey was designed to examine the effects of the health, nutrition, and family planning policies and programs implemented by national and local governments and to see how the social and economic transformation of the Chinese society is affecting the health and nutritional status of its population. CHNS is a longitudinal survey that covers the Guangxi Zhuang and eight other provinces that vary substantially in geography, economic development, public resources, and health indicators in 1989, 1991, 1993, 1997, 2000, 2004 and The nine provinces accounted for approximately 42% of China s population in 2006, and include both urban and rural areas. In the longitudinal surveys data were collected on consumer goods as well as detailed information on measures of health outcomes, such as height, weight, blood pressure, activities of daily living, SAH status, morbidity, physical function limitations, and disease history. A multistage, random cluster process was used to draw the sample surveyed in each of the provinces. Counties in the nine provinces were stratified by income (low, middle, and high) and a weighted sampling scheme was used to randomly select four counties (one in low, two in middle, and one in high-income levels) from each province. In addition, the provincial capital and a lower income city were selected. Villages and townships within the counties and urban and suburban neighborhoods within the cities were selected randomly. Currently, there are about 4,400 households in the overall survey, covering some 19,000 individuals. Further details and 1 Jones (1994) and Sloan, Smith, and Taylor (2003) considered more complicated decisions to stop smoking, but such modeling requires panel data, which we do not have.

11 10 updates of the survey are described elsewhere (CHNS 2007). The 2006 CHNS adult survey (for the males subsample) is used for this study, merged with community-level cigarette prices. Community-level prices in poorer, developing countries can provide legitimate cross-sectional variation that identifies demand (Deaton 1997). After eliminating observations with missing values, 3,162 men remained in the final sample for analysis. Definitions of variables and their descriptive statistics are presented in Table 1, with the likely endogenous variables at the top of the table. The first endogenous variable used in our analysis is an indicator of cigarette smoking behavior. The survey contains information on the number of cigarettes smoked per day. Cigarette consumption can be modeled as an integer-count variable in the literature, especially when individual cigarettes consumed can be observed. However, due to the observed cluster of the number of cigarette packs smoked in this data (i.e., pile-up s at 0.5, 1, 1.5 and 2 packs), we recode the cigarette pack quantity into an ordinal variable representing the decision to smoke or not and how much (with values from 1 to 4). The other key variable in the analysis, the SAH, is collected as an ordinal variable, ranging from poor (coded as 1) to excellent (coded as 4). The average SAH is about 2.7, and that the largest single frequency falls in category 3 (corresponding to good health). In percentage terms, the patterns of proportions of individuals falling into each SAH category are roughly the same across the smoking status categories. Continuous explanatory variables in the data set include age, income, household size and the price of cigarettes, which are collected for local markets. Several of these factors have been shown to be important in health-related studies (e.g. Deshazo and Cameron 2009). A common problem in cross-sectional studies is lack of variation in cigarette prices, but as our data include

12 11 people from many provinces and regions, we do have some variation here. The bottom of Table 1 lists several key qualitative or binary explanatory variables, including those relating to education, which has often been used to explain smoking decisions (e.g., Viscusi 1995), the region of residence, marital status, and some indicators of alcohol drinking behavior. IV. AN ORDINAL SMOKING PROBABILITY MODEL WITH AN ORDINAL ENDOGENOUS TREATMENT A treatment effects model accommodates the non-random selection of individuals into the treated state and avoids statistical bias in the empirical estimates caused by such nonrandom selection. Most empirical models with non-random sample selection or endogenous treatment have heretofore been estimated with Gaussian (jointly normal) error disturbances. Joint normality of the error disturbances generally cannot be justified by economic theory and, more importantly, misspecification of the distribution can lead to inconsistent empirical estimates and misleading inference. To avoid biases caused by such distributional misspecification, we develop an ordered probability treatment effects model with a more flexible error distribution. Below we specify the model, first building its likelihood function without a distributional assumption for the error terms. Following this the error distribution is specified by the copula approach (Nelsen 2006), which requires specification of a marginal cumulative distribution function (cdf) (henceforth, margin) for each error term and a copula function which links the margins. In all that follows, observation subscripts are suppressed for brevity. The model consists of an ordinal treatment equation for SAH (y 1 ) y = j if μ zα+ u <μ, j = 1,..., J, (1) 1 j 1 1 j and an ordinal outcome equation for cigarette smoking (y 2 ) which includes observed SAH as a regressor:

13 12 (2) 2 k y = k if ξ xβ+δ y + u <ξ, k = 1,..., K, k where z and x are vectors of explanatory variables, α and β are conformable vectors of parameters, and the μ s and ξ s are threshold parameters such that μ 0 = μ, 1 = 0, μ J =,, 0,, ξ 0 = ξ 1 = ξ K = and 2 1 μ,..., μ J and ξ2,..., ξ K 1 are estimable. The random errors u 1 and u 2 are bivariate (not necessarily normally) distributed with zero means, unitary variances, and a correlation structure as specified below. Besides a different error distribution, the specification above is an extension of existing treatments effects models in that the treatment variable y 1 is ordinal (vs. binary) and the outcome variable is ordinal (Equation (2)) rather than continuous as in Barnow, Cain, and Goldberger (1980). The likelihood function for an independent sample is 1( y = j, y = k) (3) = { = = } 1 2 J K L Pr( y j, y k), all j= 1 k= where all indexes summation over sample observations, and the bivariate probabilities (likelihood contributions) are defined as (4) Pr( y = j, y = k) 1 2 = F( μ z α, ξ x β δy ) F( μ z α, ξ x β δy ) j k 1 j k 1 1 F( μ z α, ξ x β δy ) + F( μ z α, ξ x β δy ) j 1 k 1 j 1 k 1 1 j= 1,..., J; k = 1,..., K, such that Fv ( 1, v2) = Pr( V1 vv 1, 2 v2) is a bivariate cdf for standardized random variables V 1 and V 2 with margins F1( v1) = Pr( V1 v1) and F2( v2) = Pr( V2 v2). To accommodate skewness in the distribution of the error terms u 1 and u 2, each of the bivariate cdf s in (4) is specified as a copula (Nelsen 2006). Due to space limit we present only the Gaussian copula, 2 which is the 2 A copula, denoted Cv ( 1, v2) = CF [ 1( v1), F2( v2)], is a dependence function that can be used to generate joint distributions of random variables V 1 and V 2 with specific margins F1( v 1) and F ( v ). Besides the Gaussian copula, in preliminary analysis we also used the Frank, Clayton, 2 2

14 13 preferred copula for the current application as a result of model specification tests (discussed below): (5) CF (, F; ρ) = Φ [ Φ ( F), Φ ( F); ρ], where Φ and Φ 2 are the univariate and bivariate standard normal cdf s, respectively, and ρ is the correlation between the two random variables. This is the distribution function used by Lee (1983), not called copula at the time, on sample selection models with nonnormal error distributions. Note that even with the bivariate Gaussian link function Φ 2, the copula in (5) can accommodate skewness in the error distribution by linking (any legitimate) skewed distributions characterized by the margins F 1 and F 2. We consider two forms of margins for F 1 and F 2. The first is the benchmark Gaussian cdf and the other is the generalized log-burr cdf for standardized random variable u i (Burr 1942; Lawless 2003) (6) F u = + e < u < u 1/ ( ; ) 1 (1 i κ ) i i i κi κi, i, which corresponds to the probability density function (pdf) (7) f u e e u 1/ 1 ( ; ) u i ui κi i i κi = (1 + κi ), < i <, for i = 1,2. The generalized log-burr distribution includes the logistic (κ i = 1) and extreme value (κ i 0) distributions as special cases. Figure 1 presents the pdf s of the generalized log-burr distribution with varying skewness and Figure 2 presents the corresponding cdf s. As demonstrated in the figures, the generalized log-burr distribution can deliver very different probabilities even within a moderate range of skewness. and Gumbel copulas (Nelsen, 2006) which are all capable of accommodating error skewness beyond that accommodated by the margins. The non-negative concordance, a measure of error correlation, of the Clayton and Gumbel copulas was rejected by our data in all estimation attempts, and the Frank copula (which also delivered a negative error correlation) was also rejected by a nonnested model specification test (Vuong 1989).

15 14 To demonstrate the copula approach in the present context, for a model with Gaussian copula and generalized log-burr margins (henceforth, Gaussian-Burr model), the first probability on the right-hand side of (4) is obtained by substituting F1( u1; κ 1) and F2( u2; κ 2) from Equation (6) into the probability in (4): F( μ z α, ξ x β δy ) j 1 1/ κ1 (8) = Φ2{ Φ [1 (1 + κ1exp( μ j z α)) ], 1 1/ κ1 Φ [1 (1 + κ exp( ξ x β δy )) ]; ρ}. k 1 2 k 1 Likewise, the corresponding probability for the Gaussian-Gaussian model is (9) F( μ z α, ξ x β δy ) j k 1 = Φ { Φ [ Φ( μ z α)], Φ [ Φ( ξ x β δy )]; ρ} j k 1 = Φ { μ z α, ξ x β δy ; ρ}. 2 j k 1 Equation (9) demonstrates that a Gaussian copula with Gaussian margins delivers a bivariate Gaussian probability which corresponds to that of a bivariate Gaussian model, viz., one with bivariate Gaussian distribution for u 1 and u 2. The specific forms of the remaining probabilities in the likelihood contribution (4) are similar to (8) and (9) with slightly different threshold parameters μ s and ξ s. A. Average Treatment Effects An important reason for estimating a treatment effects model is to calculate average treatment effects (ATEs). Unlike conventional models in which a treatment is specified as an exogenous discrete variable, an endogenous treatment effects model accommodates endogeneity of the treatment and non-random selection of individuals into the treated states, thus eliminating simultaneous-equations and sample selection biases and providing consisent estimates of the treatment effects and the effects of other explanatory variables. Equations (1) and (2) give the marginal probabilities

16 15 Pr( y = h) = F( μ z ακ ; ) F( μ z ακ ; ), (10) 1 1 h 1 1 h 1 1 Pr( ) ( ) ( ). (11) y2 = k = F2 ξk xβ δy1 F2 ξk 1 xβ δy1 Using the joint probability (4) and the marginal probability (10), we have the conditional probability (12) Pr( y = k y = h) = Pr( y = h, y = k) / Pr( y = h). Using the conditional probability (12), the treatment effects can be calculated as k (13) hg TE = Pr( y = k y = h) Pr( y = k y = g), for all h> g, k = 1,..., K, which is the effect of being in SAH category h (in reference to category g) on the probability of being in the kth cigarette category. All ATEs are calculated by averaging the component effects across the sample. B. Marginal Effects of Explanatory Variables Drawing on the marginal probability (11) and conditional probability (12), we calculate the marginal effects of exogenous variables on smoking probabilities by differentiating (or differencing, in the case of a binary explanatory variable) the marginal probabilities Pr( y 2 = 1) and Pr( y 2 = 4), and conditional probabilities Pr( y2 = 1 y1= 1), Pr( y2 = 1 y1= 4), Pr( y2 = 4 y1= 1), and Pr( y2 = 4 y1= 4). For statistical inference, standard errors of all marginal effects (and ATEs) are calculated by the delta method (Serfling 1980). V. EMPIRICAL RESULTS Our first empirical results relate to testing among the econometric specifications with alternative copulas and margins. As these specifications are non-nested, they are compared using non-nested specification tests. Specifically, let r i and s i be the log likelihood contributions of sample observation i for two competing specifications and define differences d i = r i s i for i =

17 16 1,,n with sample mean d and standard deviation s d. Then, under the null hypothesis of no difference between the two models, Vuong s (1989, Eq. (5.6)) standard normal statistic is = / ~ (0,1). The test results suggest that the Gaussian-Gaussian and Gaussian-Burr 1/ 2 z n d sd N model perform equally well, with slightly higher log-likelihood in the former, and better than the Frank-Gaussian and Frank-Burr models (not presented) at the 10% level of significance (though not at the 5% level). Thus, the Gaussian-Gaussian model, used in much of the sample selection and treatment-effects literature, is adequate in the current application. Next, maximum-likelihood estimates for the Gaussian-Gaussian and Gaussian-Burr models are presented in Table 3. Here however, we limit reporting of the coefficients and marginal effects to the preferred models due to space consideration. 3 First, the parameter estimates are not directly comparable between the two models as they are based on different margins. The two sets of parameters are nevertheless qualitatively consistent, in that the coefficients have the same signs and statistical significance for all variables (except HH size and self-employed). For both models, the error correlation is negative and significant at the 1% level, and all threshold parameters are positive and significant at the 1% level. A negative threshold parameter would have suggested possible mis-specification of the models. For the Gaussian-Burr model, the skewness parameters (κ) is significantly different from unity in the cigarette equation, justifying the accommodation for a skewed error distribution. The skewness parameter is not significantly different from unity for the health equation, suggesting the logistic distribution is appropriate for the data set. Table 3 contains many coefficients, but moving straight to the variable relating to the main interest of the paper, note that the effect of the SAH, holding other factors constant, at first 3 The full set of results for all econometric specifications are available from the authors.

18 17 glance appears to be positive, on smoking status. This immediately requires that the reader understand that these models are not the usual simple linear or exponential models and thus, the sign of the coefficient does not reveal the real treatment effects. Therefore, without further ado, we consider the effect of SAH and other variables as presented in the average treatment effect or ATE tables. Table 4 reports the ATE s, i.e., the average change in the probability of smoking categories for various health treatments, for the two preferred models. With only a few exceptions, all ATE s are significant at the 1% level. The top left corner of the table shows the ATE s on the probability of not smoking (i.e., when smoking = 1, 0 packs per day are consumed) and the top right corner shows the ATE s on the probability of heavy smoking (smoking = 4 corresponds to 2 or more packs per day). For instance, relative to an individual in category 1 (poor) health, an individual in category 2 (fair) health has a higher probability of not smoking, whereas an individual in category 4 (excellent) health has a lower probability of smoking heavily. Thus, the SAH has negative effects on the probabilities of smoking heavily, and such negative effects are most pronounced in the effect of the higher health category (e.g., excellent vs. poor). Such negative effects are also the obvious pattern (with two exceptions) among those who smoke moderately (between one half and one pack, or between 10 and 20 cigarettes per day). In sum, for this sample of Chinese men, the treatment of better health reduces the probability of smoking. Technically, looking between the statistical models, the Gaussian- Gaussian and Gaussian-Burr models produce few discernable differences in the ATE s. The Frank-Gaussian and Frank-Burr model produce qualitatively consistent ATE s (not reported) in terms of signs, although a number of the ATE s are notably different from those produces by the

19 18 two models with Gaussian copula. At a more intuitive economic level, SAH in our data is consistent with the thought that on average, healthier people smoke less than unhealthy people. We cannot read more specific causal stories into this because details on underlying nature of the health status are not available here. For example, with the exception of a variable indicating hypertension, we do not know if the individuals in our sample who rate themselves as unhealthy do so because they have one or more of the specific smoking-related diseases (cardiovascular disease, one of the associated cancers, or non-fatal diseases mentioned in the introduction). The relationships between smoking and the explanatory variables are next explored by examining the estimated marginal effects, while controlling for the SAH measure. A. Marginal Effects The marginal effects of exogenous variables on the probabilities of non-smoking and smoking levels, conditioned on the SAH levels, are calculated for all models, again with alternative copulas and margins for comparisons. We report marginal effects from the Gaussian- Gaussian (Table 4) and Gaussian-Burr (Table 5) models, as these are the preferred model specifications and are found to perform equally well by the non-nested test. The Frank-Gaussian and Frank-Burr models produced more notably different marginal effects and, since these models are rejected by statistical tests, marginal effects for these models are not presented here. The large number of explanatory variables in the model prohibits discussion of the marginal effect of every variable in this paper, so our discussion is confined to the ones most important for the theme here, as well as for the standard smoking policy purposes (taxes and smoking bans). To begin, government agencies naturally can influence income. In both tables of the models (5 and 6), household income has a positive effect on smoking, with higher income reducing the probability of not smoking and increasing the probability of smoking. This is

20 19 certainly not always the case in studies of smoking conducted in developed countries such as the U.S. For example, Gruber (2001), finds no strong correlation between smoking and household socioeconomic status among youths. Price has the opposite effect of income, decreasing the probabilities of smoking. This potentially supports the use of taxes on packs of cigarettes as a potential policy tool to curb smoking. Again, while it may seem obvious to those who don t study smoking that increasing prices reduces quantity demanded, the evidence on the strength and role that cigarette prices play is mixed in developed countries. In three studies (see Becker, Grossman, and Murphy 1994; Jones 1994; Viscusi 1995), the effect of price on smoking appears to be relatively small. Existing smoking literature has shown that when prices of cigarettes are included in continuous models of smoking demand, there may be complicated price and income effects, especially when the role of addiction is examined (Chaloupka 1991), and this highlights the importance of avoiding important selection issues. For the Chinese sample here both the price and income effects, while statistically significant, are fairly small in magnitudes. Government can affect education, which is often found to play a role understanding the health risks of smoking. In fact, education is often used as an instrumental variable in attempts to explain the observed sample variation in endogenous, perceived-subjective risks of smoking because educational institutions are places where people receive such general health risk information and general information about the harm that smoking causes, and because cognitive skills may improve with education. Several different qualitative education categories are included in the specification of the models. We find that having High school and College education have negative effects on the probability of smoking, across all SAH levels. Men with less than high-school education

21 20 however are more likely to smoke than those without a primary school education. Our findings are consistent with much of the smoking literature, including Blaylock and Blisard (1992), who find that more educated women are less likely to be current smokers than less educated women. For the Chinese men, education levels appear to play the role in determining smoking behavior that government agencies hope for, or even expect. VI. CONCLUSIONS In this paper we have developed and estimated a model of smoking behavior that includes formal development of self-assessed health (SAH) as an ordinal endogenous variable, applied across a sample of Chinese men from nine provinces. The importance of this is first and foremost, simply to get the estimated model to take consideration of the possibility that the SAH is an endogenous variable, and the treatment approach we apply, in general can alleviate problems stemming from selection issues. If SAH is in fact endogenous, but treated as an exogenous variable, then this might bias all of the coefficients that may relate to policy variables, such as cigarette price and income. If the price coefficients are wrong, for instance, then of course estimates of price elasticity could also be in error, making it difficult to know what tax levels and policies should be, and sort out issues of effectiveness of those policies. Though there have been some important studies of smoking in China and Taiwan, we add to the empirical literature on smoking in a developing country. Results based on one-year sample from the CHNS support the notion that better health, as indicated by the SAH, decreases the probability of smoking, after controlling for age and other socio-demographic effects. The CHNS data do not contain information on stopping and starting smoking over time, nor do we have details on specific diseases of the sample units, so our interpretation is somewhat limited to what

22 21 we can say based on this cross sectional analysis. Given these limitations our interpretation is that, after controlling for endogeneity in the SAH and examining average treatment effects, it appears that men in better health in this region of China are less likely to smoke than those in poor health, at least to the extent our sample is representative of the Chinese population living in these nine provinces. This key result may seem somewhat counter-intuitive to some health economists. Smith, Taylor, and Sloan (2001), for instance, suggest that individuals respond to adverse health shocks in their smoking decision, i.e., smoking stops when one s own negative health shocks are faced. Taken at one instance of time, a cross-sectional analysis might therefore show that people in worse health are less, not more, likely to be smoking at the time of the survey than people in good health. Our analysis of smoking status supports the notion that people in better health, ceteris paribus, are less likely to be smoking than people in self-assessed poor health, but we do not know whether they have had previous health shocks. Jones (1994), who specifically models the decision to quit smoking, finds that people in poor or fair health are less likely to quit smoking. We cannot make an inference about whether people in good health are more likely to have stopped smoking, having once started, but our result does not contradict Jones s finding. At the technical/econometric level, the copula approach we implement here allows for specification of the treatment effects model with more flexible distributions for the error terms than would be the case in conventional treatment effects models. Although we find the Gaussian- Gaussian model performs equally well as the Gaussian-Burr model despite the error skewness uncovered in the latter, we find only moderate differences in the ATEs and marginal effects of explanatory variables between the models. The use of the flexible model allows a convenient way to test against the more conventional model (i.e., the Gaussian-Gaussian model used in

23 22 much of the sample selection literature). A few final additional caveats pertain to the results here. First, we recognize that there may also be a fully simultaneous relationship between the SAH and smoking (e.g., Blaylock and Blisard 1992), but as seen below, the careful modeling here of the choice to smoke and the endogeneity in the SAH, is a good additional step in sorting out this relationship. We also note that there may be difficult identification problems to say the least, if one pursued a fully simultaneous model with these two types of variables (see Schmidt 1981). We also note that unlike some other modeling of smoking behavior, we have not explored the role of several possible influences on smoking decisions. For example, we have not included information about the subjective smoking-related mortality or morbidity risks of the smokers and non-smokers (e.g., Viscusi 1990), because the data set for this part of China does not contain it. We also do not have indicators of perceived addiction, which may be quite important. However, to the extent that subjective risks of harm or addiction are themselves endogenous variables that are functions of other explanatory variables, such as gender, age, etc., these other factors that become instruments are indeed used in our models. Thus, whether our model being incomplete matters in the usual econometric sense is not assured, but is a question that remains unanswered.

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