Are obese smokers an unintended consequence of higher cigarette taxes?

Transcription

1 Are obese smokers an unintended consequence of higher cigarette taxes? Philip DeCicca McMaster University Department of Economics August 2007 Abstract Using data from the Behavioral Risk Factor Surveillance System from 2000 to 2005, I investigate whether recent, historically-large cigarette tax increases led to weight gains among smokers. Since weight gains may not represent reduced health, I investigate possible distributional impacts of taxes on weight. For women, I find evidence of weight gains across the BMI distribution and across all age groups. In particular, I find that higher cigarette taxes simultaneously reduce the fraction of female smokers who are clinically underweight and increase the fraction who are clinically obese. By contrast, estimated gains for male smokers are concentrated in the right-tail of this distribution and are limited to older men. My findings contribute to a growing literature which suggests that offsetting behaviors may mute any aggregate health benefits associated with taxinduced smoking reductions. I thank Charlie Brown, Kerwin Charles, Mike Chernew, Donald Cox, John DiNardo, Pat Kline, Sara LaLumia, Will Manning, Tomas Philipson, Anindya Sen, Jody Sindelar, Gary Solon and seminar participants at Boston College, University of Chicago, Clemson University, McMaster University and Yale University for helpful comments on an earlier version. Huyen Nguyen and Soheil Jamshidi provided excellent research assistance. All errors are mine.

2 1. Introduction Over the past three decades, the fraction of adult smokers in the United States has fallen considerably from roughly two-fifths to under one-quarter of the adult population. Over the same period, the fraction of adults labeled clinically obese has nearly doubled. In this paper, I examine one aspect of the possible association suggested by these opposing trends. In particular, I investigate whether recent, historically-large cigarette tax increases led to weight gains among smokers. Using data from the Behavioral Risk Factor Surveillance System from 2000 to 2005, I find consistent evidence that higher cigarette taxes increased the body mass index (BMI) of smokers, but not their non-smoking counterparts. Since weight gains among smokers, who are more likely to be clinically underweight than their non-smoking counterparts, may not represent reduced health, I investigate possible distributional impacts. For women, I find evidence of weight gains across the BMI distribution and across all age groups. For example, I find that higher cigarette taxes simultaneously reduce the fraction of female smokers who are clinically underweight and increase the fraction who are clinically obese. By contrast, estimated gains for male smokers are concentrated in the right-tail of this distribution and are limited to older men. Moreover, the implied magnitudes of my estimates are plausible. For example, for female and male smokers who are years old, I find that a thirty-six cent tax increase, the average real cigarette tax increase from 2000 to 2005, increased the fraction clinically obese by about four and seven percent, respectively. To the extent that taxinduced weight gains among older individuals are permanent, such gains may represent poorer long-run health. In this regard, my findings contribute to a growing literature 1

3 which suggests that any aggregate health benefits of tax-induced smoking reductions may be muted by offsetting behaviors (c.f., Evans and Farrelly, 1998; Adda and Cornaglia, 2006). The remainder of the paper proceeds as follows. In the next section, I provide background on why higher cigarette taxes may lead to heavier smokers and also some reasons society might care if they do. I also review existing evidence which is mixed. Section 3 describes my data in detail, focusing on the construction of key variables and the historically-large magnitudes of recent cigarette tax increases, which represent improved variation for estimating causal effects in this context. Section 4 presents my empirical strategy. In particular, I implement a two-way fixed effects specification with state-specific time trends that assumes cigarette taxes do not impact the weight of nonsmokers. Consistent with this notion, I allow the estimated impact of cigarette taxes on weight-related outcomes to vary by smoking status. In effect, I use non-smokers as a within-state control group. However, since higher taxes may directly influence extensive-margin smoking behavior, they may change the composition of treatment and comparison groups over time. To address this, I include recent quitters, along with smokers, in the treated group. In separate specifications, I label individuals recent quitters if they report having quit smoking within one, three or six months of being interviewed. Next, I present and discuss my findings in Section 5, which include estimates from models of BMI and various clinically-defined thresholds based upon it. Estimates from the former represent average tax effects, while estimates from the latter provide information on the distributional nature of these average effects. In addition, I present estimates from several robustness checks, including different definitions of the 2

4 treated group, different model specifications and replacing actual smoking measures with predicted ones, following Gruber and Mullainathan (2003). Section 6 concludes the paper with a brief discussion of possible health implications of my findings. 2. Background While a possible connection between the opposing trends in smoking and obesity is of considerable interest, I focus on a narrower question. 1 Namely, I investigate whether higher cigarette taxes increase the body weight of smokers. The idea is not new. As noted by Philipson and Posner (1999): Anti-smoking measures may increase obesity and by doing so reduce the health benefits of these measures because smoking is a method of weight control and so the heavy taxes and other regulations aimed at smokers may induce people to be overweight in a Pareto sense. In the following paragraphs, I provide possible explanations for why higher cigarette taxes might result in heavier smokers and then discuss reasons why society might care if they do. The section concludes with a review of recent work, which is mixed in terms of its findings. 2.1 Why might higher cigarette taxes lead to fatter smokers? Beyond anecdotal evidence, several clinical studies find that smokers gain weight when they stop smoking or reduce their consumption of cigarettes (c.f., Gritz et al., 1989; Klesges et al., 1989; Perkins, 1993; French and Jeffery, 1995). On average, smoking cessation is associated with a weight gain of five to ten pounds (USDHHS, 1990; Williamson et al., 1991), but more recent evidence suggests that the appropriate figure might be closer to ten to fifteen pounds (Klesges et al., 1998). Moreover, there is evidence that, on average, women gain more weight than men and that they are also more 1 By contrast, Lakdawalla and Philipson (1999), Cutler, Glaeser and Shapiro (2003) and Chou, Grossman and Saffer (2004) attempt to explain the upward trend in adult obesity in the U.S. Given its relevance, I will discuss the last study in detail later in this section. 3

5 likely to be supergainers, which implies a weight gain of at least thirteen kilograms (Williamson et al., 1991). However, estimates from these studies should be considered descriptive in nature since the authors treat changes in smoking behavior as completely exogenous events. Nevertheless, several physiological reasons support the likelihood of an inverse relationship between smoking and body weight. For example, there is evidence that smoking increases the body s metabolic rate, the rate at which calories are burned while an individual is at rest (Kershbaum et al., 1966; Glauser et al., 1970; Wack and Rodin, 1982; Hofstetter et al., 1986). While the exact mechanism is not completely understood, researchers believe that smoking raises metabolism by stimulating the central nervous system to produce catecholamines. These hormones cause the heart to beat faster and hence lead to greater resting caloric expenditure. In addition, nicotine induces thermogenesis, the process by which the body generates heat, which also causes additional calories to be expended. As a result, former smokers tend to burn fewer calories each day once they quit. Absent any offsetting activity, this implies a weight gain of one pound within two to four weeks. 2 In addition to its role as a metabolic stimulant, nicotine is known to be a moderately effective appetite suppressant (Grunberg, 1982). Indeed, several studies find that reductions in smoking lead to additional caloric intake, though it is difficult to place the blame squarely on nicotine, or other potential appetite suppressants in cigarette smoke, since former smokers often report an improved sense of smell and taste, which may independently increase food intake (Stamford et al., 1986; Rodin, 1987; Perkins et al., 1990; Pomerleau et al., 1991; Clearman et al., 1991). 2 This implication relies on a linear relationship between time and reduction in caloric expenditure. Note also that thirty-five hundred calories equals one pound. 4

6 Finally, higher cigarette taxes may lead to heavier smokers even if smoking behavior is orthogonal to taxes. In particular, given their magnitude, these historicallylarge tax increases may have had non-trivial reductions in smokers disposable income. 3 Such income reductions may lead smokers, for example, to substitute into cheaper, less healthy foods or more hours worked which may lead to a wide range of other substitutions that may make them more prone to weight gain. 4 Since tobacco is an addictive good and since smokers tend to have low incomes, such effects seem plausible, especially since the majority of smokers do not quit altogether when faced with higher taxes Why might we care? Since first warning Americans about the dangers of cigarette smoking in 1964, nearly all U.S. surgeons general have advocated for higher taxes on cigarettes. More recently, the Department of Health and Human Services, via its Healthy People 2010 program, has recommended a combined federal and state cigarette tax of $2.00 per pack (USDHHS, 2004). This recommendation is based at least partially on evidence which suggests that higher cigarette taxes are associated with reductions in premature mortality and morbidity among smokers (GAO, 1986; Warner, 1986; Harris, 1987; Chaloupka, 1989; Moore, 1995). 5 None of these studies, however, allow for potential offsetting behavior including the possibility that higher taxes induce weight gains among current and former smokers. In the present context, these omissions are especially relevant since, according to recent 3 For example, assuming no change in smoking behavior, a smoker who consumes one pack of cigarettes per day will end up paying $30 extra per month following a per-pack tax increase of $1.00. For a full-time worker earning minimum wage, this represents nearly four percent of pre-tax monthly earnings. 4 Cutler et al. (2003) suggest that one particular substitution might be away from home prepared meals to mass produced ones, though their main argument relies on changes in the relative price of these meal types. 5 See Chapter 6 of Reducing Tobacco Use: A Report of the Surgeon General (USDHHS, 2000), especially pp , for a description of these and other related studies. 5

7 government figures, obesity-related disorders may soon overtake smoking as the leading cause of premature death in the United States (Mokdad et al., 2004). So, while reducing smoking-related premature mortality and avoidable morbidity is a laudable goal, it may not be fully achieved if smokers substitute weight gain for cigarette consumption or otherwise gain weight when cigarette taxes increase. Weight gain alone, however, is not cause for concern. For example, if most of any prospective gains occur in the left-tail of the weight distribution, higher taxes on cigarettes may actually represent improved weight-related health. 6 In other words, while there is much focus on obesity, being underweight may also have health consequences. So, if higher taxes reduce the fraction of smokers who are clinically underweight, this may represent an additional health benefit of higher cigarette taxes. Alternatively, if gains are concentrated in the right-tail of the distribution, higher cigarette taxes may be more likely to offset some of the health gains associated with tax-induced smoking cessation or reduction since excess body weight, like smoking, is linked to premature death and avoidable morbidity. Hence, it is important to understand the possible distributional impacts of cigarette taxes on BMI and not just their average effect. In this spirit, we estimate the impact of taxes on several thresholds of weight-related health, including underweight (BMI 18.5), overweight (BMI 25), obesity (BMI 30) and morbid obesity (BMI 35) Related Evidence Recent evidence on the impact of the money price of cigarettes on weight-related outcomes is mixed. For example, using BRFSS data from , Chou et al. (2004) examine several factors that plausibly may have influenced the well-documented rise in 6 This is in addition to the direct health benefits of smoking reduction or cessation. 6

8 adult obesity over this period. The authors estimate models with an extensive set of covariates that includes state-specific cigarette prices and find evidence that within-state price increases led to weight gains among adults in their sample. More specifically, they find evidence that higher cigarette prices also led to an increase in the fraction obese, suggesting potentially health-reducing weight gains. 7 Gruber and Frakes (2006) re-examine the findings of Chou et al. (2004) using data from BRFSS files. While their empirical specification is similar to Chou et al. (2004), they characterize the money price of cigarettes with taxes, rather than prices, and control for trends common to all states (i.e., the source of tax or price variation in both of these studies) with year indicators, rather than a quadratic trend variable. 8 These authors find evidence that higher cigarette taxes reduce average BMI and the fraction of clinically obese individuals. Moreover, their estimates are robust to the inclusion of state-specific trends, which capture unobserved time-varying characteristics specific to individual states. However, they note that the implied magnitudes of their findings, and those of Chou et al. (2004), seem implausibly large. I extend this literature in two ways. First, I am able to exploit several historicallylarge tax increases that occurred post As I document in the next section, the magnitude of tax increases from 2000 to 2005, is much larger, and hence more useful for identifying the relationship of interest, than any other period in recent history. As documented in Section 3.2, unlike earlier cigarette tax hikes, these recent increases were 7 The authors do not examine the underweight threshold since they attempt to explain the upward trend in adult obesity. 8 These authors also control for a particular time-varying covariate, state unemployment rate, to account for macroeconomic conditions that previous work suggests impact smoking behavior (Ruhm, 2005). I also control for state-specific unemployment rate, as well as other time-varying state-level factors, including anti-smoking policies such as bans on smoking in public workplaces and restaurants. 7

9 driven by state budgetary concerns, rather than anti-smoking sentiment or, more generally, a collective preference for health. Second, I allow the impact of cigarette taxes to vary across individuals whose weight, in principle, should and should not be affected by higher taxes. More specifically, I allow the impact of cigarette taxes to have differential impacts on the weight of smokers and non-smokers. In effect, this strategy treats non-smokers as a within-state control group since, conceptually, their weight should not be impacted by changes in cigarette taxes. To deal with tax-induced changes in the composition of the treated group (i.e., smokers), I include recent quitters, who would otherwise be labeled as non-smokers, in this group. As a result, the group I label treated contains smokers and recent quitters. I elaborate on this strategy, and related issues, in Section Data I use data from the Behavioral Risk Factor Surveillance System (BRFSS). The BRFSS is an annual telephone survey of adults eighteen years of age and older from across the United States. Two key features of the BRFSS are that it has relatively large sample sizes and, more importantly, is representative of state populations by design. In addition to smoking behavior, the BRFSS contains more standard demographic information such as age, race, education, health status and marital status. In what follows, I describe key variables, my analysis sample and discuss how I address a relevant data limitation Measures of Body Weight BRFSS data contain information on body mass index (BMI), which is, conceptually, body weight normalized by height. More precisely, it is defined as the ratio of one s weight in kilograms divided by height in meters squared. While BMI is preferred to body 8

10 weight, and is a generally-accepted metric to assess weight-related health, it is not without its own weaknesses. First, BMI might not be a valid measure for some individuals, perhaps due to differences in body type or composition. If not, widely-used cutoffs at the upper and lower tails of the distribution, for example, may not represent similar weight-related health for such individuals. Second, BMI information in the BRFSS is constructed from self-reports of height and weight, so it is subject to measurement error (Cawley, 1999). In particular, it is likely that individuals tend to under-report their weight and hence their BMI. This is seen most clearly by comparing average BMI from my sample to comparable information from the fourth National Health and Nutrition Examination Survey (NHANES IV), which took physical measurements of respondents weight and height between 1999 and In my analysis sample, mean BMI is in 2000, while in the NHANES IV the corresponding average for individuals eighteen years old and older is This difference translates into nearly nine pounds for an individual of average height. Note, however, that since my identification strategy posits changes in body mass index as a function of tax changes within states, this should not be problematic if misreporting patterns are constant over the six years in question. 10 In addition to BMI, itself, I examine four widely-used thresholds of weightrelated health clinical measures of underweight, overweight, obesity and morbid obesity to better understand the distributional impacts of any average effects on BMI. Respectively, these are defined as BMI of no greater than 18.5, BMI of 25 or higher, BMI of 30 or higher and BMI of 35 or higher. While the primary concern is that affected 9 The latter figure is taken from Table 1 in Chou, Grossman and Saffer (2004). 10 My use of self-reports of weight and height to construct BMI is consistent with Chou et al. (2004) and Gruber and Frakes (2006). 9

11 smokers become clinically obese, I also consider clinical underweight status since, in principle, weight gains among smokers may improve health if they occur in the left-tail of the BMI distribution Cigarette Excise Taxes, 2000 to 2005 I use data on taxes and dates of enactment from Orzechowski and Walker (2005) to construct monthly state cigarette taxes and merge this information to the BRFSS, which contains data on state of residence and date of interview. Using monthly, rather than annual, variation allows for better identification of within-year changes in behavior, which may be especially important in the context of the large tax increases I exploit. In all models, I use real cigarette excise tax rates per pack of twenty cigarettes denominated in 2001 dollars. As alluded to earlier, there were several large tax increases on cigarettes between 2000 and 2005 at the state-level. Table 1 lists all increases of at least fifty cents and their dates of enactment. As can be seen, all but one of these large increases occurred after Indeed, from January 2002 to December 2005 there were twenty-one such large increases in state cigarette excise tax rates, with an average increase of just over sixtyeight cents per pack. While this list includes many of the usual suspects (e.g., Massachusetts, New Jersey and Rhode Island), it contains others not known for high cigarette excise taxes (e.g., Colorado, Montana, Ohio and Oklahoma). Research from state and local public finance suggests that the size and breadth of these tax increases were due state budget shortfalls following the 2001 recession in the United States (Maag and Merriman, 2003). 11 To the extent that these increases were indeed driven by 11 In a separate paper, these authors also show that relative to the recession in the U.S., the 2001 recession had much deeper negative impacts on state tax revenues (Maag and Merriman, 2007). 10

12 budgetary concerns, rather than state-specific preferences for health, they represent more appropriate variation for estimating the causal effect of taxes on weight-related outcomes. 12 Over the entire period, and with respect to all increases, the average state excise tax increased from about 42 to roughly 87 cents, more than doubling over this short period in nominal terms. By contrast, the average cigarette tax increased only from 30 to 40 cents over the previous six year period, 1994 to The difference in real, rather than nominal, increases is even more telling. In real 2001 dollars, average state cigarette tax increased from 35 to 42 cents for the earlier period versus 42 to 78 cents from 2000 to This dramatic increase over a relatively short period of time provides improved variation to identify the impact of taxes on smoking behavior, relative to earlier periods Analysis Sample The BRFSS files contain data on 1,089,460 individuals between 18 and 59 years old. Limiting the sample to respondents with valid information on body mass index, smoking and state of residence results in a slightly smaller set of 1,008,537 individuals, including 431,048 men and 577,489 women. I exclude women who were pregnant at the time of interview since the responsiveness of their smoking behavior, coupled with natural pregnancy-related weight gain, may lead to a spuriously positive association between taxes and weight (c.f., Evans and Ringel, 1999; Colman et al., 2003). Given that I include indicators for missing covariates, these figures represent my analysis samples. I also estimate models that contain no covariates, other than relevant fixed 12 All specifications include controls for state-specific monthly unemployment rates as previous work finds that macroeconomic conditions may affect smoking behavior (Ruhm, 2005). That said, their inclusion has no impact on estimated tax coefficients. Further, while there is evidence that the Master Settlement Agreement led to increased state taxes, the estimated effect is relatively small (roughly ten cents) and likely occurred prior to the majority of the recession-driven increases I exploit (Trogdon and Sloan, 2006). 11

13 effects, to check whether treating missing covariates, as described above, affects my estimates. Finally, due to the likelihood of different data generating processes for different demographic groups, I estimate models by age group (18-24, and 45-59) for women and men separately Data Limitation While the BRFSS data are very useful for estimating the impact of cigarette taxes on body weight-related outcomes, they have one important drawback. After 2000, BRFSS contains no information on the number of cigarettes smoked per day by self-reported smokers. As a result, when I allow the impact of taxes to vary by smoking status, as described in the following section, I define smokers using the most liberal characterization available in BRFSS. In particular, I label someone a smoker if they smoke on at least some days since this measure better captures individuals who respond behaviorally to increased taxes than the alternative smoking participation measure daily smoking. For example, it should better capture individuals in the process of quitting smoking or individuals who have otherwise decided to reduce consumption in response to higher taxes. In addition, I also include recent quitters in the smoking group for reasons discussed in detail when I present my empirical strategy in the following section. Beyond compositional issues, this limitation affects my estimation strategy. In particular, I do not estimate instrumental variables models of weight regressed on smoking status since the estimate would represent the causal effect of tax-induced smoking cessation on weight-related health (Angrist and Imbens, 1994; Angrist et al., 1996). As a result, the IV estimate would miss weight gains that are due to tax-induced 12

14 changes in smoking behavior on the intensive margin. For example, it is plausible that reducing consumption from, say, one to one-half pack of cigarettes per day could lead to weight gain. My reduced-form approach will capture such gains, though admittedly it cannot distinguish between gains due to smoking cessation and gains due to intensivemargin smoking reductions Empirical Strategy As noted by others, unobserved heterogeneity is perhaps the most important concern in relating cigarette taxes to any outcome of interest. In the present context, the concern is that unobserved state-level characteristics that are correlated with cigarette tax levels and exert an independent influence on weight will result in biased estimates. For example, it is commonly believed that states with higher cigarette taxes are somehow more progressive or otherwise express a stronger collective preference for health. As a result, regressing weight on cigarette taxes in a single cross-section of data may bias the true effect of taxes towards zero if residents of high tax areas are, as implied, relatively more health conscious and this healthy disposition extends to weight-related health. The repeated cross-sectional nature of BRFSS data allow for inclusion of state fixed effects, which will eliminate the troublesome heterogeneity if it is time-invariant. With this in mind, a model that bases statistical identification on within-state variation in cigarette taxes is given by: BMI ijmt = α T + ρs + βx + ϕz + σ + μ + τ + ε (1) jmt ijmt ijmt jmt j m t ijmt Here, i indexes the individual, j state of residence, m month surveyed, and t year surveyed. BMI represents log of body mass index and other weight-related outcomes, T 13 IV would also miss any impacts that are not smoking-related (e.g., income effects associated with these historically-large cigarette taxes). 13

15 cigarette tax, S smoking status, X a set of individual covariates, Z is a set of time-varying state-level covariates including controls for macroeconomic conditions and state-level bans on smoking in restaurants and public workplaces, σ, μ and τ are vectors of state, month of interview and year of interview fixed effects, respectively. 14 This specification, however, has an important drawback. It imposes the same relationship between taxes and weight on smokers and non-smokers alike. If cigarette taxes affect the weight of any group, they should predominantly impact the weight of smokers, for whom they are most binding. Based on this logic, I estimate the following equation: BMI ijmt jmt ijmt ( S T ) + βx ijmt + ϕz jmt + σ + μm + τ t εijmt = α T + ρs + γ + (2) ijmt j Here, the effect of taxes on weight is allowed to vary by smoking status. In effect, this strategy treats non-smokers as a within-state control group. In this framework, γ represents the differential effect of cigarette taxes on the weight-related outcome of smokers versus non-smokers, while γ + α represents the total effect of taxes for smokers. An additional implication is that α should be zero; in principle, cigarette taxes should not affect the weight-related outcomes of non-smokers. Though not represented in equation (2), estimates from all models reported also include state-specific time trends to control for unobserved factors correlated with cigarette taxes and weight-related health that vary over time within states. 15 All models are estimated with sample weights and all corresponding standard errors are clustered by state This equation represents the general empirical strategy taken by Chou et al. (2004) and Gruber and Frakes (2006). 15 To be clear, these state-specific time trends are distinct from the vector τ which captures trends common to all states. 16 Though not reported, unweighted models yield estimates nearly identical to those presented in Section 5. 14

16 While likely an improvement on equation (1), this specification does not account for the possibility that taxes may directly influence smoking initiation and cessation decisions. That is, higher taxes may impact the composition of treatment and comparison groups, which may in turn affect estimates ofγ and α. 17 For example, if taxes induce cessation behavior among current smokers, and if such individuals do indeed gain weight, these gains will be incorrectly assigned to the non-smokers group if before-and-after comparisons are made. As specified in equation (2), the estimated relationship between higher taxes and weight would be attenuated. On the other margin, higher taxes may deter individuals from smoking initiation. In this case, the impact on the estimated relationship between taxes and weight is ambiguous. For example, if new smokers tend to weigh less than those who do not start smoking, reduced initiation would lead the relationship to be overstated. Conversely, if those who initiate smoking tend to be heavier, this would lead the relationship to be attenuated. Recent work finds evidence for the latter possibility, though only for young females (Cawley et al., 2004). In particular, these authors find that young females who are overweight are more likely to start smoking than their non-overweight counterparts. In the context of this study, it seems likely that initiation-related compositional issues would be limited to the year old group, since very few individuals start smoking after age twenty-four. I address this issue by grouping recent quitters with some-day smokers (S) in equation (2). In principle, this solves the cessation-related compositional problem since any weight gains by those induced to quit by higher taxes should be assigned to the treated group. This strategy, however, is not ideal since individuals identified as recent 17 As a baseline, note that if taxes did not influence extensive-margin smoking behavior, then such compositional change would not exist and non-smokers would be a natural comparison group. 15

17 quitters are not necessarily induced to stop smoking by higher taxes. Most importantly, such individuals may have quit smoking for reasons that have implications for subsequent weight gain (e.g., individuals may have quit smoking as part of an overall effort to implement a healthier lifestyle). That said, the BRFSS contains good information on the timing of smoking cessation and I construct three measures of recent quitting based on whether an individual quit smoking within one, three or six months of being interviewed. Since estimates do not differ much across these definitions of recent cessation, I present estimates based on models that define a recent quitter as one who quit smoking within one month of their interview date. Relative to the three- and six-month definitions, this should minimize the impact of non-tax-induced quitting, which is important for reasons discussed above. It should also help capture those smokers who are induced to quit by higher taxes and now report themselves as non-smokers. However, to the extent that the recent quitter measure does not account for all tax-induced quitters, α may not be zero. That said, this occurs only sporadically across the many specifications reported. Finally, since weight may involve different data generating processes across genders and ages, I estimate models by age group (18-24, and 45-59) for women and men separately. 5. Estimates In what follows, I first discuss the impact of cigarette taxes on the body mass index (BMI) of smokers and recent quitters and then present evidence on the distributional impact of these average gains. 18 In general, I find the largest and most precisely estimated effects among males aged and females aged years old. 19 Gains in 18 Throughout this section I use the word smokers and the phrase treated group interchangeably. Recall that the treated group includes both some-day smokers and individuals who report having quit smoking within one month of being interviewed. 19 Recall that I exclude pregnant women from my sample. 16

18 weight for these males are concentrated in the right-tail of the BMI distribution, while gains for these females are seen across the BMI distribution and include reductions in the fraction of female smokers who are clinically underweight. Throughout the section, I present the implied tax effects of a thirty-six cent increase, the average increase in real state cigarette taxes in my sample period Average Effects of Cigarette Taxes on Weight Table 2 presents estimates from equation (2), which includes state-specific time trends, for key parameters. 20 Recall that γ represents the differential impact of cigarette taxes on BMI of smokers versus non-smokers and α represents the impact of taxes on the BMI of non-smokers. Hence, γ+α represents the total effect for smokers and recent quitters (i.e., the treated group) which is the estimate of greatest interest. As can be seen, γ is positive and statistically significant in five of six models presented, implying larger weight gains among the treated group, relative to their nonsmoking counterparts. By contrast, α is never statistically different from zero at conventional levels of significance, though it is marginally significant in two of the six models. However, in both cases, α is negative and acts to reduce the total effect of taxes on the weight of the treated group (i.e., γ+α). Note also that average BMI is consistently lower for smokers than non-smokers, and that this difference widens with increasing age. Focusing on the total effect, γ+α is positive in all six models. Of the implied gains, those for males aged and females aged are most precisely estimated. Respectively, the sums of these coefficients imply gains of roughly one pound and three- 20 Though not reported, I also estimate models without state-specific time trends. That is, I estimate models with just state and time fixed effects. Corresponding estimates of the total effect (γ+α) are similar to those presented throughout Section 5, though are somewhat larger in magnitude and more precisely estimated. 17

19 quarters of a pound in response to a thirty-six cent increase in cigarette tax. 21 While these effects may seem small, they represent an average increase in BMI over all treated individuals (i.e., individuals who smoke on at least some days and recent quitters). As such, they may mask important distributional effects. For example, if weight gains are occurring in the left-tail of the BMI distribution, then they likely have different health implications than gains originating in the right-tail of this distribution Distributional Effects of Cigarette Taxes on Weight Tables 3A and 3B provide evidence on the distributional impacts of cigarette taxes on weight-related health for males and females, respectively. The rows of each table present estimates of the total effect of cigarette taxes for the treated group (i.e., γ+α) for four common clinical thresholds. In particular, I model clinical underweight (BMI 18.5), overweight (BMI 25), obesity (BMI 30) and morbid obesity (BMI 35). Columns in each table represent three age groups: individuals who are 18-24, 25-44, and years old. For convenience, I repeat gender-specific BMI estimates from Table 2 in the first row of each table. Once again, the models presented are estimated via equation (2) and include state-specific time trends. Before getting to the estimates of γ+α in Tables 3A and 3B, note that estimates of all key parameters (i.e., γ, α, and ρ) and associated standard errors are presented in Appendix Tables 1 through 4, which correspond, respectively, to the four clinical thresholds analyzed. As can be seen, estimates of γ, which represents the differential impact of cigarette taxes, are uniformly negative in underweight models and uniformly positive in models of overweight, obesity and morbid obesity. Moreover, α, which is the 21 These implied effects are based on average heights of roughly 1.75 meters (69 inches) for males and 1.63 meters (64 inches) for females. 18

20 impact of taxes on the weight-related health of non-smokers, the comparison group, is statistically different from zero at conventional levels in only two of twenty-four specifications represented in these four appendix tables. Estimates from Table 3A imply that weight gains among the treated group are concentrated in the right-tail of the BMI distribution among men aged For example, γ+α is positive for the overweight, obesity and morbid obesity thresholds for these older men. More specifically, the relevant obesity model estimate suggests that a thirty-six cent increase in the cigarette tax is associated with a roughly 1.5 percentage point increase in the fraction of male smokers in this age group who are clinically obese. Relative to a base of roughly twenty-two percent, this represents a gain of just over seven percent. 22 To the extent that weight gains among older individuals are more permanent in nature, my findings may reflect reduced long-run health. One other result in Table 3A is worth noting. I find evidence that higher cigarette taxes reduce the fraction of year old male smokers who are clinically underweight. In particular, the relevant tax effect suggests that a thirty-six cent increase will reduce the fraction underweight in this group by about twenty-two percent. While this seems large, it is important to remember that the effect is estimated off of a small base (i.e., less than three percent of treated individuals in this age group are labeled as clinically underweight). In addition, estimates for younger individuals may be confounded if, as suggested by Cawley et al. (2004), smoking initiation differs across the weight distribution of young people. That said, these authors found no evidence of such a difference for young men. 22 Recall that the fraction obese reported includes only those in the treated group (smokers and recent quitters) who have lower body weights than non-smokers, on average. 19

21 Unlike estimates for male smokers, estimates for female smokers suggest impacts across the BMI distribution and, to some extent, across the age distribution. For example, as seen in Table 3B, γ+α is negative in underweight models for all three age groups. Respectively, these estimates imply that a thirty-six cent increase in cigarette tax is associated with roughly a ten, eight and eleven percent decline in the fraction of treated group individuals who are underweight. That said, unlike women aged and 45-59, the estimated reduction in underweight among women seems to be driven by the impact on non-smokers, as can be seen in Appendix Table 3. While this may reflect gains of tax-induced quitters not captured by my empirical strategy, it casts some doubt on the relationship for this younger set of women. That said, all estimates for individuals aged should be viewed with some caution given the concern raised earlier, related to the work of Cawley et al. (2004). Table 3B also presents evidence of right-tail weight gains, particularly among female smokers aged For example, γ+α is positive for overweight, obesity and morbid obesity thresholds for this group of women. Estimates for overweight and obesity thresholds suggest respective gains of nearly three and six percent. Unlike male right-tail gains, however, there is some evidence that such gains for women are spread across the age distribution. In particular, tax estimates are precisely estimated for both and year old female smokers and imply gains in the fraction clinically obese of roughly ten and five percent, respectively Sensitivity Analysis In addition to the models presented, I perform several robustness checks, which are shown in Table 4. To minimize the number of estimates reported, I perform these checks 20

22 only for men aged and women aged and limit the outcomes I examine to BMI and obesity status, though all estimates are available from me by request. 23 Table 4 presents my findings. For convenience, the first two columns of Table 4 replicate relevant estimates of γ+α from Tables 2, 3A and 3B. The second two columns display estimates from models that drop all covariates with the exception of the relevant fixed effects and state-specific trends. The next four columns present estimates from models that vary the treated group by how a recent quitter is defined. In particular, I estimate models that define recent quitters as individuals who have quit smoking within three and six months, instead of the one month definition that I have used to this point. The last two columns present estimates from models that replace actual smoking status with predicted smoking status, following the empirical strategy of Gruber and Mullainathan (2003), in the context of my equation (2). As seen in Table 4, estimates from these various models are quite similar to my main estimates, which are repeated in the first two columns. In particular, estimates from models that define a recent quitter as one who stopped smoking within three months and within six months are somewhat smaller than, though quite similar to, my main estimates. That said, estimates from models that include no covariates are somewhat larger in magnitude, especially for women, suggesting some degree of correlation between the omitted control variables and the impact of tax on the weight-related outcomes of the treated group. Finally, models that replace actual smoking status with a predicted version yield statistically precise estimates of γ that are positive, and thus consistent with cigarette taxes differentially increasing the weight of smokers relative to non-smokers. In this case, however, γ is the coefficient associated with the interaction of a predicted 23 The effect of taxes on weight is largest and most precisely estimated for these two groups. 21

23 smoking probability and the cigarette tax. While this strategy recognizes that smoking status may change with higher taxes, it is difficult to interpret these findings beyond noting that the impact of taxes on BMI and obesity status rises with increases in the predicted probability of being a smoker. In sum, these additional estimates support my main findings and their general pattern. 6. Conclusion To summarize, I find evidence of weight gains across the BMI distribution and across all age groups for female smokers. In particular, I find that higher cigarette taxes simultaneously reduce the fraction of female smokers who are clinically underweight and increase the fraction who are clinically obese. By contrast, estimated gains for male smokers are concentrated in the right-tail of this distribution and are limited to older men. The evidence presented contributes to a growing literature that suggests any aggregate health benefits of tax-induced reductions in smoking may be muted by offsetting behaviors (c.f., Evans and Farrelly, 1998; Adda and Cornaglia, 2006; Adda and Lechene, 2004). 24 That said, my findings are mixed with respect to potential health implications. For example, weight gains among previously underweight individuals actually may represent improved health. By contrast, right-tail gains likely represent reduced health and such weight gains among older individuals are likely to be more permanent in nature. An additional concern, though I cannot test it directly with these data, is that taxes may induce intensive margin reductions in cigarette consumption that, in turn, lead to weight gain. In principle, this would lead to individuals who still smoke 24 While the first two papers focus on offsetting behaviors due to increased cigarette taxes, the latter paper raises the possibility that the changing nature of who smokes, in spite of widely-available evidence on its deleterious health impacts, may mute any beneficial effects of public policy aimed at reducing smoking. More precisely, the authors document the changing unobserved health characteristics of smokers over time to make their point. 22

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