The Effectiveness of Cigarette Taxes on Older Adult Smokers: Evidence from Recent State Tax Increases

Transcription

1 The Effectiveness of Cigarette Taxes on Older Adult Smokers: Evidence from Recent State Tax Increases Kevin Welding University of California at Santa Barbara October 2014 Abstract Using the recent excise tax increases at the state-level in the United States from 2000 to 2010, I estimate two-way fixed effects models of older adult smoking participation. I find that a one dollar increase in the excise tax reduces smoking participation by 2.5 percent for adults age 30 to 74. An investigation of the age distribution finds that results are sensitive to how older adult age groups are defined. The effect of one dollar excise tax increase is largest for a 45 to 49 year old group, reducing smoking participation by over 7 percent. The effect of taxation is more widespread across ages for women and low income smokers, while women and high income smokers reduce their smoking participation earlier in life. I find evidence that the effect of taxation on smoking participation of established smokers diminishes at higher tax and price levels. There is a clear need for complementary cessation strategies for smokers unresponsive to cigarette excise tax increases and this need is growing over time. JEL Codes: I18, I12 Keywords: tobacco taxation; smoking cessation; economics of smoking; public health policy welding@econ.ucsb.edu I am grateful to Kelly Bedard, Daniel Rees, and Meghan Moran for useful discussions and suggestions that benefited this paper. All remaining errors are my own.

2 1. Introduction Smoking is a problem. According to the U.S. Department of Health and Human Services (USDHHS, 2014), cigarette smoking accounts for more than 480,000 preventable deaths each year in the United States. This makes it the country s leading cause of preventable death. As of 2012, the Centers for Disease Control and Prevention (CDC, 2014) estimate that 42.1 million people, about 18% of adults, are current smokers. In 2000, the USDHHS set the Healthy People 2010 objective for smoking prevalence by adults to 12%. Failing to reach this target, the USDHHS set the Healthy People 2020 objective for smoking prevalence to 12% again. To meet this target it is important to stop new smokers from starting, but it is essential to reduce the number of existing smokers. Cigarette taxes provide an effective tool to reduce smoking participation. They reduce initiation, encourage cessation, and generate tax revenue. However, the effectiveness of raising cigarette prices (through excise taxation) is not uniform across all ages. Gallet and List (2003) conduct a meta-analysis of price elasticities and find the median elasticity from studies on teens is -1.43, young adults is and adults is This evidence suggests that higher taxes are a less effective tool on smoking as smokers age, but it doesn t distinguish if the trend continues for older adults. 2 In this study, I take a closer look at the effectiveness of cigarette taxation on the smoking participation for older adult smokers. It is important to understand whether more 1 These elasticities include participation and conditional demand. Wilson et al. (2012) provide a more recent systematic review and find that price participation elasticities range from to -0.1 for youths and to for adults. There is a similar finding that price sensitivity decreases with age. Considering that initiation levels are higher for youths these participation differences understate the effect of price increases on cessation. See Chaloupka and Warner (2000) for an extensive literature review. 2 In the smoking literature, adults are generally considered to be individuals who are 18 years of age and older. Part of this analysis is to define "older adults", but I begin by using individuals between the ages of 30 and 74. 1

3 established smokers are affected by cigarette excise taxes differently across the age distribution in order to reach reduced smoking prevalence targets. Higher levels of disposable income and greater degrees of addiction to nicotine make tax induced quitting harder for experienced older smokers, but there is still the motivation of substantial personal 3 health benefits from quitting at ages upward of 55 (Taylor et al.,2002; Ostbye and Taylor, 2004; Jha et al., 2013). Burns (1992) finds that eight of ten smokers in the United States express a desire to quit smoking, but most find it diffi cult without help. Cigarette taxes can provide such assistance, acting as a self-control function.(gruber and Koszegi, 2004) Recent research (Callison and Kaestner 2013; DeCicca and McLeod 2008; Farrelly et al. 2008; Franz 2008; Farrelly et al. 2001; Evans and Farrelly, 1998) provides mixed results about whether price increases affect the smoking participation of older adults in the United States. The first issue is that the definition of older adults is not consistent across the literature. Most studies examining adult smokers use samples of individuals over 18 years old and indirectly present estimates for older adults when stratifying by age groups. DeCicca and McLeod (2008) is the only study that looks explicitly at an older adult sample. Using data from 2000 to 2005, they find a price participation elasticity of about -0.2 for a 45 to 64 age group and about -0.3 for a 45 to 59 age group, which suggests the year old group is less price sensitive than their younger peers. The other most recent study, Callison and Kaestner (2013), looks at a earlier time period, 1995 to 2007, and finds a participation tax elasticity of (the implied price elasticity is about -0.14) for adults ages 55 to 74, but do not find a significant result for a age group. 4 3 There are also social health benefits to less smokers producing secondhand smoke. The CDC (2014) estimates, from , that secondhand smoke exposure causes over 7,300 lung cancer deaths annually among adult non-smokers. 4 Some of the details of these studies (data source, smoking measure) are available in Table A1 in the 2

4 Studies that use data from before 2000 also find mixed results for older smokers. Farrelly et al. (2001) use data from the and report a statistically significant effect of tax on smoking participation for a group, but none for adults 40 and older. 5 Farrelly et al. (2008) uses data from 1985 to 2003 and finds the opposite result: a statistically insignificant effect for a group and a significant result for adults 40 and older. One possible explanation for the difference is that the later study controls for state tobacco control program spending. 6 Sloan and Trogdon (2004) use data from and find a statistically significant elasticity of for a age group and a elasticity for a 65 and older group, but an insignificant result for a group. 7 The different time periods, smoking measures and age stratification make comparing these studies to each other diffi cult, but the within study differences by age provide a reason to better understand older adult smokers. The sensitivity of these results is part of the motivation for examining the effect of cigarette taxes across the age distribution. I collect data on older adult smokers in the Behavioral Risk Factor Surveillance System (BRFSS) from 2000 to This time period is important for the analysis of cigarette taxes on older smokers. The historically large cigarette tax increases during the appendix. 5 This study uses data from the National Health Interview Survey (NHIS). Evans and Farrelly (1998) use 1979 and 1987 NHIS data and also find no effect from taxes for adults 40 and older. Lewit and Coate (1982), use a cross-section from the 1976 NHIS and find a smoking participation price elasticity estimate of for adults ages 35 and older, but the issue with this result is that state variation in cigarette excise taxes and smoking participation are likely correlated with unobservable state differences. 6 If this is an omitted variable bias story then the bias runs in different directions by age group. Tobacco control program spending and taxation appear to be complementary for the 40 and older group. The age group is more affected by the program spending than taxation when both are in place. 7 Sloan and Trogdon (2004) use prices instead of taxes, define a smoker as an individual smoking daily or on some days, and use a wide varitey of controls for tobacco control regulations. Franz (2008) looks at a similar time period, , but relies on region fixed effects instead of state-level ones. He finds a significant tax effect for three older age groups, including More details of these studies are in Table A1. 3

5 2000 s are harder to ignore even for most addicted smokers. The taxes during this period increased by 157% in real terms compared a 55% increase in the 1990 s and a 5% increase in the 1980 s. The analysis of this time period also benefits from excise tax increases during two recessions that created a need to raise revenue in the face of budget deficits. These shortfalls induced states to propose large increases in the cigarette excise tax to generate revenue, which makes them arguably more exogenous from state-level anti-smoking sentiment compared to earlier time periods. In sum, the previous literature on cigarette taxes has closely examined age differences for youth and young adults, but have failed to look for systematic differences between older smokers. Thus, to address this gap, this paper provides evidence that the effect of excise taxation on smoking prevalence is different across the age distribution of older adults. To my knowledge this is the first study to explicitly examine the sensitivity of age categories for smokers. I find that a one dollar increase in the excise tax reduces smoking participation by 2.5% for adults age 30 to 74. The effect of one dollar excise tax increase is largest for the 45 to 49 group, reducing smoking participation by over 7%. These findings are important to understand that taxation does not have a uniform effect for older smokers. In addition to a lack of focus on the effects of taxation across older age groups, there is also a lack of work looking at how these effects vary sociodemographically. In particular, previous research examining the impact of taxation on smoking across demographic groups has not investigated whether these effects are different across ages. Farrelly et al. (2001) and Stehr (2007) provide evidence that women are more responsive to cigarette taxes than men, in both smoking participation and conditional demand for cigarettes. There is also evidence (Farrelly et al. 2001; DeCicca and McLeod, 2008) that cigarette taxes are effective 4

6 in reducing cigarette use for low-income individuals, but not high-income smokers. Thus, to address this gap, this paper offers a second contribution by examining the effect of taxes by gender and income over the age distribution. I find evidence of tax effectiveness over a broader age range for women and low income individuals. This is consistent with Gruber and Koszegi (2004) that find using taxes as a self-control function is relatively more beneficial to price sensitive groups. I also find that women and high income smokers reduce their smoking participation in response to taxation earlier in their life. The final contribution in this paper is to examine the possibility of non-linear effects of taxes on older adult smoking participation. There is evidence that the effect of taxation diminishes as tax and price levels continue to increase. Although this result does not rule out the continual benefit of taxation on conditional smoking, it raises doubts about the effectiveness of taxation induced quitting for established smokers. These results are important for understanding that continued success with established smokers requires more than aggressive taxation. There is a strong argument for tax increases to be accompanied by cessation programs for the most inelastic smokers. The paper proceeds as follows. Section 2 describes the data sources and summary statistics. Section 3 provides details on the econometric framework. Section 4 presents the results for the overall age group and the effects of taxation across the age distribution. This section also separately investigates the age distribution by gender and income level. Section 5 examines the presence of non-linear tax effects, while Section 6 concludes. 5

7 2. Data The primary data source is collected from the Centers for Disease Control and Prevention s (CDC) Behavioral Risk Factor Surveillance System (BRFSS). The BRFSS is an annual health-related telephone survey in the United States that measures health-risk behaviors and the use of health services. The availability of smoking and demographic information on a monthly basis makes this data very useful for this analysis. Data are collected for individuals, 18 years of age and older, in all 50 states, the District of Columbia and three U.S. territories. The individuals sampled are civilians who are not institutionalized. The survey is conducted using random digit dialing and is representative of state populations by design. In this study, I analyze a sample of 30 to 74 year old adults in the 2000 to 2010 surveys for all 50 states and the District of Columbia. The cigarette excise tax and cigarette price information is from the Tax Burden on Tobacco (Orzechowski and Walker, 2011). This section describes the smoking measure and cigarette excise taxes. It also provides details for the sample and covariates. 2.1 Smoking Measure The smoking measure comes from the combination of two BRFSS questions. The key question asks "do you now smoke cigarettes everyday, some days, or not at all?" A participant answers this question if they answer yes to a previous question; "Have you smoked at least 100 cigarettes 8 in your entire life?" Both questions provide self-reported smoking information. I use the key question to create a smoking measure that considers the more regular smokers. The measure defines a smoker as an individual who is smoking everyday and includes an individual who reports smoking some days as non-smokers. According to 8 One hundred cigarettes is equivalent to 5 packs. 6

8 this measure, 15.4% of the respondents are smokers in the BRFSS over the period. This is a conservative definition of a smoker, which allows me to focus on the more addicted smokers. I use this measure for most of the paper since I am interested in examining the response of established smokers to cigarette taxes. I do provide some preliminary results for a more liberal definition of a smoker where individuals who report smoking on some days are combined with the daily smokers. Using this measure, 20.3% of the respondents are smokers in the BRFSS over the period. Figure 1 shows that the trend of smoking participation is similar for both definitions of a smoker. The goals of comprehensive tobacco control programs are to prevent smoking initiation, reduce cigarette consumption, and increase smoking cessation. This study does not address the reduction in cigarette consumption for a few reasons. The BRFSS is a valuable dataset because of the monthly variation in individual smoking participation, but since 2000 it does not ask about the number of cigarettes an individual smokes on a daily basis. I focus on smoking participation because of the potential for large public health benefits from older adult smoking cessation. Taylor et al. (2002) found gains of between 3.5 and 5.5 years for those who quit smoking at age 55 and 5.5 to 7 years for 45 year olds who quit. Ostbye and Taylor (2004) shows that not only does years of life increase, but, arguably more important, so does healthy years of living. They find that if former smokers quit before the age of 35 then they can expect to live as long and healthfully as nonsmokers. These potential internal health benefits translate into reduced public healthcare costs without accounting for a decrease in secondhand smoke. Less secondhand smoke exposure means further reductions in future healthcare costs. At the intensive margin, higher excise taxes theoretically influence smokers to smoke 7

9 fewer cigarettes, but that does not account for changes in smoking behavior. Previous studies (Evans and Farrelly, 1998; Adda and Cornaglia, 2006) show that reductions in the number of cigarettes is accompanied by switching to longer lasting and higher nicotine content cigarettes or smoking the same cigarette longer. Therefore, a decrease in the number of cigarettes consumed does not ensure an improvement in health or a movement towards quitting. This is another reason to emphasize the extensive margin, the participation in smoking. 9 The first question in this analysis is whether there is a change in smoking participation for established smokers when excise taxes increase the price of cigarettes. 2.2 Cigarette Excise Taxes, The use of cigarette excise taxes is an important policy tool that can reduce cigarette smoking, generate tax revenue, and curb future medical costs. The cigarette excise tax is considered a specific indirect tax. It is a fixed dollar amount charged per pack that the government does not directly apply. The excise tax is typically applied after the manufacturer stage and before the wholesaler or retailer stage. In early 2009, the Children s Health Insurance Program Reauthorization Act of 2009 was signed into law. As part of the law the federal tax rate on cigarettes increased from $0.39 per pack to $1.01 per pack, a 159% increase. From 2000 to 2010 there were 109 cigarette excise tax changes at the state-level. California, Missouri and North Dakota are the only states that did not change their excise tax during this period. The data on cigarette excise taxes and prices are collected from an industry-funded 9 The participation in smoking includes initiation and cessation, but with an older adult sample the extensive margin should be dominated by cessation. Schmidt (2013) finds that 80 percent of all adult smokers begin smoking before the age of 18, and 90 percent do so before leaving their teens. 8

10 annual report called the Tax Burden on Tobacco (Orzechowski and Walker, 2011). 10 The price information is the annual average price per pack weighted by the market share of the brand and includes generic brands. The report provides the enactment date and size of cigarette excise tax changes at the state-level. This information is used to construct a monthly panel of state cigarette taxes. When the enactment date is not the first of the month a weighted average of the daily tax is used to create an effective tax rate for that month. 11 The tax data are merged with the individual-level BRFSS data using state of residence and month and year of the interview. Unless otherwise stated, the real cigarette excise taxes and prices per pack of 20 cigarettes are denominated in 2001 dollars using the Consumer Price Index provided by the Bureau of Labor Statistics. The time period studied, , plays an important role in this analysis. Cigarette taxes can be used as a public health tool to reduce smoking, but this motivation will bias estimates on the effect of a tax if the decision is correlated with unobserved state-level anti-smoking sentiment. From 2000 to 2010 there were two recessions, which provided an alternative reason to increase the cigarette excise tax. The 2001 recession had a strong negative impact on state tax revenues relative to earlier recessions. Following the 2001 recession, state tax policy focused tax increases on a narrower base, tobacco users (Maag and Merriman, 2003; Maag and Merriman, 2007). The Great Recession started in late 2007 and lasted 18 months provided an additional need for states to generate revenue. These tax increases in response to budget deficits represent arguably exogenous variation for estimating the effect of cigarette excise taxes on smoking participation. 10 The information on enactment date and size of the tax were cross-referenced with information from the Campaign for Tobacco-Free Kids. 11 For example, if the tax was 1 dollar for the first ten days and 2 dollars for the last twenty days then the effective tax rate for the month is $

11 During the sample period, the 109 state-level cigarette excise tax changes had an average size of approximately 43 cents in real terms. The list of these taxes is in the appendix Table A2. The average real tax increased from 50.5 cents in 2000 to cents in 2010; this is about a 129% increase. To get a sense of how aggressive the taxation is in this period compare that change to the 55% increase and 5% increase in the previous two decades, respectively. Figure 1 shows the trend of the average state tax rates from 2000 to The tax rates increase steadily after the 2001 and 2007 recessions. The trends of the cigarette excise tax and smoking participation are suggestive of an inverse relationship, but this is the correlation without controls for other variables potentially impacting smoking behavior. 2.3 Covariates Table 1 presents state-level descriptive statistics by year. The first three rows are the numerical version of Figure 1. Daily smoking participation decreased by 27.4%, from to 0.127, from 2000 to If occasional smokers are included in the smoking measure then the decrease is 21.7% over the same period. The cigarette excise tax increases by about 129%. 13 Data on the seasonally adjusted monthly unemployment rates by state are collected from the Bureau of Labor Statistics (BLS). This accounts for the effect of macroeconomic conditions on smoking participation (Ruhm, 2005). He finds that smoking participation decreases when unemployment rates increase. Information on state-level smoking bans for private workplaces, restaurants and bars is collected from the Center for Disease Control and Prevention s (CDC) State Tobacco Ac- 12 The unweighted average cigarette tax displays a 161 percent increase in the tax, but the pattern is similar to the sample weighted change discussed previously and shown in Table I display the number of tax changes by year, but do not use this as a covariate. It gives a sense of how active states were with the cigarette excise tax in each year. 10

12 tivities Tracking and Evaluation (STATE) System. These variables provide another channel to induce a smoker to consider quitting. The number of states banning smoking in these areas increased substantially from 2000 to 2010; restaurant bans went from one to 28 states, private worksite bans from zero to 30 and bans in bars from zero to 22 in that time period. 14 Annual spending of state funds for tobacco prevention programs was collected from the Campaign for Tobacco-Free Kids. I adjusted these expenditures to reflect real prices and per capita using annual state populations from the US Census. There is a 40% reduction in the use of state-level funding for tobacco control during the sample period and there is some indication that the spending is cut while coming out of a recession. One of the benefits of the BRFSS is the relatively large sample sizes. The BRFSS data has information on 3,571,325 individuals. The sample of individuals between 30 and 74 years old is 2,744,511 after some restrictions. The first restriction is to keep observations with valid smoking information. The other restrictions come from analysis of "refused" and "I don t know" coded responses. I use a two group mean analysis to examine if individuals with one of those responses are systematically different in their smoking behavior. If there is a statistical difference then an indicator flag for the "refused" and "I don t know" response is created and the observations are kept. 15 This results in a sample of 424,430 smokers and 2,320,081 non-smokers when occasional smokers are considered non-smokers. The BRFSS provides demographic information including age, gender, race, education, household income, marital status and health status. Table 2 presents the individuallevel mean and standard error of the sampling distribution by smoking status. Smokers are, 14 State decisions include a ban of smoking, a designated smoking area, a separate ventilated smoking area and no provision. The indicator variable is one when a state has an explicit ban. 15 I also complete the analysis without making any statistically similar drops and the results are similar. 11

13 on average, younger, male, less educated, less likely to be married, less likely to be high income, and less likely to report favorable health. These demographic variables are controlled in the econometric analysis, which is presented in the following section. 3. Empirical Methodology The aim of this paper is to estimate the causal effect of state cigarette tax increases on smoking participation for older smokers. Then, in turn, investigate if this effect is uniform over the age distribution of older adults. Ideally, there would be a way to randomly assign smokers to face cigarette taxation, but this is not the case. The quasi-experimental set up, which is popular in the smoking literature, relies on the differences in timing and size of state-level cigarette excise taxes. State fixed effects are used to eliminate time-invariant differences. This approach is susceptible to biased results if tax changes are correlated with other time-varying state-level factors of smoking participation. The time period of 2000 to 2010 contains two recessions that created state-level deficits, which provided an exogenous motivation for increasing cigarette excise taxes. The basic econometric specification is: S ijmt = α + γt jmt + β X ijmt + δ Z jmt + σ j + µ m + τ t + ε ijmt, where the dependent variable, S ijmt, representing the smoking participation status for individual i residing in state j who is interviewed in month m and year t. The dependent variable is a binary variable since it takes the value of one for an individual categorized as a smoker and takes the value of zero for a non-smoker. I use the ordinary least squares (OLS) method to produce a linear probability model. I make this choice since preliminary results are robust to using probit or logit 16 and since later specifications include interaction terms, 16 Table A3 in the appendix provides an example of the robustness between the OLS and probit spec- 12

14 which Ai and Norton (2003) show are very diffi cult to interpret. The value of the cigarette excise tax, T jmt, varies at the month, year and state level. The parameter of interest, γ, represents the marginal effect of a tax change on the probability of being a smoker. To control for other determinants of smoking participation the model includes X ijmt, which is the set of individual-level demographic characteristics from the BRFSS data and Z jmt, which is a set of state-level covariates for smoking bans, tobacco prevention spending, and monthly unemployment rate. 17 State, month and year fixed effects are represented by σ j, µ m and τ t, respectively. The state fixed effect controls for time-invariant state-level variables that might affect smoking participation rates, like the difference in smoking sentiment between California and North Carolina. The month fixed effect controls for seasonal variations, such as less reported smoking participation during a New Year s resolution. The year fixed effect accounts for overall time trends across states. The impact of cigarette taxes is identified by with-in state variations, relative to the changes occurring in other states. The fixed effects method is susceptible to multicollinearity. I calculate the variance inflation factor (VIF 18 ) and find there is suffi cient independent variation in cigarette excise taxes across states over time to use fixed effects. All robust standard errors are clustered at the state-level to account for the correlation between individuals within a state. All models are weighted using sample ifications. The logit results are similar to the OLS results as well. These models take the form: Pr(S ijmt = 1) = G(α + γt jmt + β X ijmt + δ Z jmt + σ j + µ m + τ t + ε ijmt ), where G () is the standard normal cumulative distribution function for probit and the logistic function for logit. 17 The smoking bans and tobacco prevention spending are annual so there is no variation across the m index for these variables. ( ) 18 1 I regress the tax variable on state, year and month fixed effects. The VIF is 1 R with the R 2 from 2 this regression. I find an R 2 of The VIF is Since the VIF< 10 there is suffi cient variation to use fixed effects (see Kennedy, 1994). 13

15 weights provided by the BRFSS Results 4.1 Older Adults from 30 to 74 Figure 1 implies a negative relationship between cigarette excise taxes and smoking participation. This section estimates the effect of cigarette excise taxes on smoking participation using a fixed effects econometric model. Table 3 presents the main estimates for older adults from age 30 to 74. The upper panel is the estimates for the frequent smokers, those who smoke daily. The estimates in the lower panel include a more general group of smokers, those who smoke on all or some days. The parameter estimate reported for each specification is the marginal effect of a 1 dollar increase in the cigarette excise tax on smoking participation while holding the included covariates constant. The first specification reported in column (1) is from a baseline model including only controls for the month, year and state fixed effects. A 1 dollar increase of the cigarette excise tax decreases daily smoking by after controlling for month, year and state variation. On a base daily smoking participation of this is about a 3% reduction. The implied price participation elasticity 20 is , which means a 10% increase in the cigarette price reduces daily smoking participation by 1.18%. The estimated effect when occasional smokers are included with daily smokers is slightly smaller. 21 This pattern in the results is apparent across all specifications and is consistent with findings in the previous literature. The main focus of this study is the most 19 These weights adjust for non-response and non-coverage for geographical strata and take into account state stratification by age, gender, and race/ethnicity. Full details of the BRFSS weighting are available from the CDC (CDC, BRFSS). 20 This is estimated using an average price of $ The inclusion of this group decreases the price participation elasticity. This means the non-daily smokers are less responsive to price changes. This is probably a result of their lower expenditure on cigarettes and not from a stronger addiction. 14

16 habitual smokers so I will proceed by using the daily smoking measure. The second specification reported in column (2) adds a set of individual level controls, which include age, gender, race, education, income, marital status and general health status. The age variable is measured in years and is included as a quadratic. The gender variable is an indicator for being male. The other controls are sets of indicator variables. Including the reference groups there are six indicators for race, four for education, eight for income, six for marital status, and five for health status. 22 More details for those controls are found in the descriptive statistics in Table 2. After controlling for demographic variation the effect of the cigarette excise tax on daily smoking participation decreases slightly, but is still statistically significant. The final three columns include controls for state-level differences in the monthly unemployment rate; smoking bans in private workplaces, restaurants, and bars; 23 and per capita tobacco control spending, respectively. For some context on how these covariates are changing over time see Table 1. The marginal effect of the cigarette excise tax on smoking participation is robust to the inclusion of these controls. I consider the specification in column (5) as the main specification. The estimated effect of the cigarette tax on daily smoking participation has an implied price participation elasticity of This means a 10% increase in the price of cigarettes reduces smoking participation by almost 1%, holding the covariates constant. This estimate is on the lower bound of the range of price participation elasticities found in the older adult smoking literature. 22 The descriptive statistics in Table 2 do not show all of the categories. There are also indicator flags for "refused" or "I don t know" responses since those individuals could be systematically different in their smoking behavior than individuals willing to respond. 23 The smoking bans include two partial designations: a separate ventilated area or a designated smoking area. I did not include states with these two designations as having a full smoking ban, which results in a stricter measure. 15

17 Although Table 3 does not report the coeffi cient estimates for the state-level controls they are worthy of some discussion. The unemployment rate has a negative effect 24 on daily smoking, which means as the economy gets worse then daily smoking participation decreases. This is consistent with healthy living in hard times (Ruhm, 2005). The smoking bans did not have a statistically significant effect on smoking participation 25 and the worksite ban was the only one with a negative coeffi cient estimate. 26 It is reasonable to believe that smoking bans are more helpful for conditional cigarette demand. The per capita tobacco control spending has a statistically significant negative effect 27 on smoking participation with an estimated elasticity of , which is more elastic than the estimate found when Rhoads (2012) used the current level of tobacco control expenditure. Her estimated elasticity of , which could be smaller because of a younger sample (an older sample could be more responsive to tobacco control programs) or the absence of a control for state unemployment rate (this would bias the tobacco control expenditure elasticity toward zero since unemployment rates and tobacco control spending is inversely correlated). Despite the difference in magnitude this is still additional evidence of the impact of tobacco control spending on reducing smoking participation. I will revisit the impact of these controls during the analysis of the age distribution. Table 4 presents estimates for separate robustness checks. The first column shows the implied price participation elasticity of the main specification, which is the upper 24 This effect is borderline significant at the 10% significance level. 25 I considered the possibility of strong multicollinearity in these variables. The use of a smoking ban composite variable did not change the results. 26 I believe that worksite bans should have the largest impact on smoking participation considering the amount of time spent at work and since it is less avoidable than bars and restaurants. It is arguable that bar and restaurant smoking bans are more of a measure for anti-smoking sentiment. 27 The estimated coeffi cient is with a p-value of The average amount of per capita spending used to calculate the elasticity was 2.42 dollars. 16

18 panel of the last column in Table 3. I argue that the tax changes during the sample period are orthogonal to state-level anti-smoking sentiment, but I am not entirely dependent on this assumption. In case some of the tax changes are endogenous I model the state fixed effect in order to eliminate the time invariant differences in observable and unobservable anti-smoking sentiment that could bias the estimates. Column (2) handles the possibility of endogeneity by explicitly modeling anti-smoking sentiment. I remove the state fixed effect and add an indicator for being a major tobacco producing state, 28 a measure of anti-smoking sentiment, 29 and the annual spent on tobacco prevention program funding as a percentage of the CDC recommendation. This model still includes the controls for smoking bans in worksites, restaurants, and bars, which may reflect anti-smoking sentiment. The resulting elasticity is ; it is slightly smaller and statistically insignificant. 30 The national trend in smoking participation is accounted for in the main result by the year fixed effect. Column (3) adds state specific time trends to the main specification. This accounts for any trend in smoking participation at the state level. The implied price participation elasticity, -0.21, is larger with the inclusion of state specific time trends. Column (4) looks at the main specification for the years This time period corresponds with the most active time for cigarette tax changes since there were only 6 tax changes in 2000 and 2001 combined. The response of daily smokers to a tax change is slightly larger in this 28 This information is collected from the 2007 Census of Agriculture. I use the six major producers: North Carolina (170,083 acres), Kentucky (87,641), Virginia (20,881), Tennessee (20,109), South Carolina (20,084) and Georgia (17,989). These states make up 96 percent of total leaf tobacco production in the United States. 29 The measure for anti-smoking sentiment is taken from DeCicca et al. (2006). They use nine variables on attitudes toward smoking to create a score for each state. They have a score for each state in , and The scores are highly positively correlated so I use their results from the Tobacco Use Supplements of the Current Population Survey. 30 The standard error on the tax coeffi cient has increased. This is likely because of the correlation between taxes and the added variables. 17

19 period. The implied price participation elasticity is The final three columns look at the robustness of the sample with respect to certain states dominating the results. In column (5), I remove the observations from New York and Illinois from the sample since they include large local taxes that aren t accounted for in individuals with only state information. Then in column (6), I run the model without individuals from California, Missouri, and North Dakota since they do not change their cigarette tax in the sample period. The estimated effect of taxes is robust across these specifications Effect Across Age Groups The smoking literature defines "adults" as individuals over 18, but the definition of "older adult" is ambiguous. As discussed in Section 1 there was no consistent age group that represented older adults. The difference in the results of the literature, by significance and magnitude, reveals a possible heterogenous effect of taxes on smoking participation across the life cycle. For example, the most recent studies exhibit this sensitivity. Callison and Kaestner (2013) report different results for two older adult age groups, an insignificant effect for year olds and significant elasticity for the group. DeCicca and McLeod (2008) focus on older adult smokers and present results for both a 45 to 59 year old group and a 45 to 64 year old one. They find similar results when they extend the age range, but "slightly smaller in magnitude, suggesting less price-sensitivity among those years old." The sensitivity of the estimates to the age groupings is enough motivation to investigate. The previous section found an estimated effect of the cigarette tax on daily smoking participation 31 I also estimated the effect without Hawaii since they were an outlier in the number of tax increases (8) over the period of 11 years. And I ran the model without the 8 states (AZ, CO, MT, OK, OR, SD, WA) that increased the taxes by a state vote. The estimates from neither of these models were different than the main specification. 18

20 for the sample of 30 to 74 year olds has an implied price participation elasticity of This section takes a closer look at the effect of cigarette excise taxes across the age distribution. Table 5 presents the results of the main specification for five year age groupings of adults starting at 30 years old and ending at 74. The estimated impact of the cigarette excise tax on daily smoking participation is statistically significant for only one of these groups, the age group. 32 The corresponding implied price participation elasticity is -0.27, which means a 10% increase in the price of cigarettes reduces smoking participation by 2.7%. This age group appears to be driving the price participation elasticity for the broader age group over the same time period. Now the question is why only one age group has a statistically significant responsive to the cigarette tax. One observation is that the age group has the highest propensity to smoke daily, which means they might also have the highest number of smokers at the extensive margin. The cigarette tax can be viewed as a self-control device instead of a monetary tool. Another possibility is the perceived health benefit. The timing of the decrease in smoking participation matches the U.S. Surgeon General message that a 50 year old who quits smoking cuts the risk of dying by age 65 in half (USDHSS, 1990). But it does not fit with more recent research (Taylor et al., 2002; Ostbye and Taylor, 2004) that show the biggest health benefits exist for cessation before the age of 35. It is conceivable that the general public was more likely to receive the message from the Surgeon General than the later research papers. 4.3 Income and Gender 32 The coeffi cient estimate for the age group is statistically different from all the other age groups.at 10 percent significance level (50-54 and 55-59) or better. Neither the covariates for smoking bans or tobacco prevention program spending came back as significant for this age group. Program spending significantly reduced smoking participation for those in their early 30s, early 40s and both 50 year old groups. The smoking bans had less of an effect on smoking participation. 19

21 The previous literature looks at the heterogeneity of tax effects across demographic groups. I analyze the sample by gender and income, but I do so while examining the effect across the age categories. Farrelly et al. (2001) and DeCicca and McLeod (2008) find that low-income individuals are more responsive to cigarette price increases. This is attributable to less disposable income and a larger percentage of expenditure. In a study conducted for the New York State Department of Health, Farrelly et al. (2012) found that low-income smokers (households making under $30,000) spent a larger proportion of their annual household income on cigarettes relative to high-income smokers, but had a similar average daily consumption. If low-income smokers are addicted to smoking and need help with self-control then increased prices could help reduce their cigarette tax burden. Gruber and Koszegi (2004) present a time-inconsistent model where taxes are a self-control function that is relatively more beneficial to low income smokers. In this study, low income is defined as individuals in households with an income under $25,000 and high income individuals are in households with an income over $75,000. This allows for the use of approximate top (27.5%) and bottom (20.6%) quartiles. Table 6 displays the results by income status and age group. The effectiveness of cigarette taxation on the smoking participation of high income individuals is isolated to smokers in their 40s. The effect of cigarette taxes on smoking prevalence is more widespread for individuals in low income households. Taxation appears to aid smoking cessation for low income smokers from their late 40s to early 60s. These results provide a good example of the sensitivity in categorizing adult samples in this literature. Choosing a sample of individuals 45 to 59 results in an estimated significant decrease in smoking participation from taxation for the low income group, while a sample over the age of 40 finds a statistically insignificant effect. 20

22 Stehr (2007) highlights the importance of state fixed effects for gender analysis. He reviews the studies without state fixed effects and finds that they report men are relatively more price elastic than women. When state fixed effects are included (Farrelly et al., 2001; Stehr, 2007) women are more responsive to cigarette prices than men. In this paper, I found that for the general age category, 30 to 74, the men, who are more likely to smoke, have an implied price participation elasticity of and the women s elasticity is Although women look more responsive to prices from these estimates they are not statistically different from each other. Table 7 presents the impact of taxes by gender across the age categories. The women are reducing their smoking participation because of increased taxes earlier than men. This finding could be the result of different preferences for health or indicative of more successful quits by women. There is evidence that women benefit less from nicotine replacement therapies and use a combination of treatment programs to successfully quit smoking (Perkins, 2001; US DHHS, 2001). This may explain why female smokers are more likely to respond to increased cigarette taxation. It is also possible that female smokers appear more price sensitive because of differences at the underlying income levels. Table 8 takes a look at the sample of low income households separately by gender. The results in the upper panel show that the price sensitivity found in female smokers is driven by women in low income households. Results for women in medium and high income households (not shown) did not show any significant reductions in smoking participation from cigarette taxes in those age groups. 33 The bottom panel does show that low income households account for the price sensitivity found for men 33 There is a significant reduction in smoking participation for year olds in medium income households ($25,000-$75,000). 21

23 in their early 60s, but it also shows that low income male smokers in their 40s are responsive to cigarette taxes. 34 These results provide evidence that most of the differences by gender in Table 7 are not attributable to household income differences. 5. Non-Linear Tax Effects The time period from 2000 to 2010 had frequent, large increases in the cigarette excise tax. This section tests the hypothesis that the effect of taxation on smoking participation diminished as taxes and prices reached all-time highs. For example, does a 50 cent increase in the excise tax have the same effect when the tax (price) that is initially at 1 dollar as when the tax (price) is at 3 dollars. In theory, the measurements of costs and benefits are considered in absolute dollars instead of proportions, but in reality a consumer often focuses on the percent saved or spent. I look at this possibility in two ways. The first measures the effect of the excise tax with a quadratic effect: S ijmt = α + γ 1 T jmt + γ 2 T 2 jmt + β X ijmt + δ Z jmt + σ j + µ m + τ t + ε ijmt. All of the models include controls for individual characteristics, state policies and fixed effects that appear in the main specification. The use of state fixed effects controls for the presence of low and high cigarette tax states. If the effect of the cigarette tax diminishes as taxes increase then we expect to see a negative sign for γ 1 and a positive sign for γ 2. The results of the above model are presented in Table 9. I show the results for three groups: the most responsive to taxes, 45-49, an older set of adults, 45-74, and the whole sample, Two things should be said abou the age group. The effect for this group in high income households is found only in men (not shown) and the effect for this group in low income households is significant and positive for both genders. The latter point implies some smoking initiation later in life for individuals in low income households. It is also counter-intuitive that smoking participation increases with cigarette taxes. 22

24 The first column for each of the age groups is the result from the main specification with a linear tax relationship on smoking participation. There is a statistically significant decrease for all three groups. The baseline estimate for the age group implies that a onedollar increase in the cigarette excise tax reduces smoking participation by 7%. 35 The second column presents the results from the quadratic tax relationship on smoking participation. The linear component estimates the marginal effect of a one-dollar tax increase in a market that previously was not taxing. The estimate for the age group implies that an initial one-dollar tax increase reduces smoking participation by 15.6%. The estimate on the quadratic term is positive and statistically significant, which implies that as the tax increases the effect of taxation on smoking participation is diminishing. The estimates of the quadratic form imply that the negative effect of the cigarette excise tax completely diminishes at a real tax of 239 cents. 36 There is evidence of a similar diminished effect of taxation on smoking participation in the group, but not the overall group. The second model allows the effect of the excise tax to be different at various price levels. The price information is the annual average price per pack weighted by the market share of the brand, including generics, in each state. The prices are adjusted by the CPI to be in 2001 dollars. In this model, P 1, the left out group, is an indicator when an individual faces the lowest quartile price, less than $3.20. The included indicators, P 2, P 3, and P 4, represent the other three price quartiles: between $3.20 and $3.73, from $3.73 to $4.48 and over $4.48, respectively. 35 For comparison, there is a 3.5% reduction for the group and 2.5% reduction for the group. 36 S This is from solving for the tax in the equation: T = T ax = 0. Individuals that experience taxes over 239 cents account for 1.4% of the sample size. The estimated positive effect of cigarette taxes beyond that tax is likely a result of the data being cut off at

25 S ijmt = α + γ 1 T jmt + γ 2 P 2 T jmt + γ 3 P 3 T jmt + γ 4 P 4 T jmt + η f(p ) + β X ijmt +δ Z jmt + σ j + µ m + τ t + ε ijmt. This model allows an examination of whether a one-dollar increase in the cigarette tax has the same effect at different price levels. 37 The controls from the main specification are used for this model as well. The term f(p ) is the three included price category indicators. If the tax is more effective on smoking participation at low prices then we expect γ 1 to be negative and the coeffi cient on the price interactions with tax to be positive. Table 10 shows the results for the second model. If smokers are affected by the absolute change of the tax then the estimates on the four γ i coeffi cients will not be statistically different from each other. The marginal effect of a one-dollar increase in the excise tax on smoking participation is the largest when the price is in the lowest quartile for all age groups. The evidence of a smaller effect at higher price levels is mixed. The age group is less affected by tax increases at the higher price levels, but the effect in third quartile is not statistically different from the effect in the first quartile. For the older age group the tax effect diminishes as the price level increases, but remains negative in the highest price quartile. There is a similar pattern in the overall group; when there is a one-dollar tax increase in the lowest price quartile the smoking participation decreases by 16.9%, while a one-dollar tax increase in the highest price quartile decreases smoking participation by 1.6%. These two models provide evidence that the effect of taxation on smoking participation diminishes at higher tax levels and higher price levels. This implies that for excise taxation on cigarettes to have the same effect on smoking participation at higher prices there 37 The average price in the lowest quartile is $3.01 so a one-dollar tax would increase the price by 33%. That is compared to the same tax increase being a 20% increase for the average price in the highest quartile. 24

26 will need to be even higher taxes or accompanying tobacco prevention programs. This result is for the extensive margin and it does not rule out a linear reduction in the number of cigarettes smoked as the result of a tax increase at different price levels. These results are consistent with the hypothesis that established smokers are more affected by relative price changes than absolute ones. These results also fit a story where smokers who paid earlier tax increases to continue smoking will be more likely to pay higher taxes and prices later. An alternative explanation is that smokers facing higher taxes and prices are more likely to search for discounts and find ways to avoid taxes. 6. Conclusion This paper begins by providing evidence that the effect of excise taxation on smoking prevalence is different across the age distribution of older adults. In general, a one dollar increase in the excise tax reduces smoking participation by 2.5% for adults age 30 to 74. An investigation of the age distribution finds that results are not uniform. The effect of a one dollar excise tax increase is largest for the 45 to 49 year old group, reducing smoking participation by over 7%. These findings are important to understand that taxation does not have a uniform effect for older smokers. The varying effect of cigarette taxes by age is also apparent in an analysis of gender and income. I find results consistent with the general findings in the literature that women and low income smokers are more responsive to cigarette taxes. I show that the effect of taxation on the prevalence of smoking is more widespread for women and happens earlier in life. Although, I show that these results is driven by the price sensitivity of female smokers in low income households there is still evidence of gender differences within low 25

27 income households. On the other hand, the smokers in high income households are much less affected by cigarette taxes, regardless of gender. Opponents of the cigarette excise tax cite the inherent regressive nature of the tax. While it is true that low income individuals are more likely to smoke they also are also more likely to respond to cigarette tax increases and reduce their smoking participation. There are substantial long term financial improvements for those low income smokers who are responsive to cigarette taxation. There is also the added benefit of reduced health problems. The final contribution in this paper is to examine the possibility of non-linear effects of taxes on older adult smoking participation. I provide evidence that the effectiveness of cigarette taxes on smoking participation is diminishing as tax and price levels increase. This result highlights that cigarette excise taxation should be seen a complement, not a substitute, of tobacco control programs. There are smokers who are unable or unwilling to quit at almost any price. This group of smokers will pay a larger portion of the cigarette tax revenue in the future unless they find alternative mechanisms to help them quit smoking. It has been over 50 years since the first Surgeon General s Report on Smoking and Health, which was issued on January 11, The fight against cigarette use has seen some success, but existing smokers still need help to quit. The results in this paper show that taxation is a good tool to reduce smoking participation, but does not have power over every smoker. These results are important for understanding which older adult smokers are price sensitive, how the effectiveness of cigarette taxation evolves, and why there is a need for tobacco cessation programs to complement cigarette taxation. 26

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31 8. Figures and Tables Notes: Smoking participation rates are shown for daily smokers and daily or occasional smokers ages 30 to