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Bans on electronic cigarette sales to minors and smoking among high school students
Journal of Health Economics 54 (2017) 17–24
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Journal of Health Economics journal homepage: www.elsevier.com/locate/econbase
Bans on electronic cigarette sales to minors and smoking among high school students Rahi Abouk a , Scott Adams b,∗ a b
Department of Economics, Finance and Global Business, William Paterson University, United States Department of Economics, University of Wisconsin-Milwaukee, United States
a r t i c l e
i n f o
Article history: Received 5 January 2016 Received in revised form 6 January 2017 Accepted 8 March 2017 Available online 18 March 2017 JEL classifications: I18 K42
a b s t r a c t Many states have banned electronic cigarette sales to minors under the rationale that using e-cigarettes leads to smoking traditional combustion cigarettes. Such sales bans would be counterproductive, however, if e-cigarettes and traditional cigarettes are substitutes, as bans might push teenagers back to smoking the more dangerous combustion cigarettes. We provide evidence that these sales bans reduce the incidence of smoking conventional cigarettes among high school seniors. Moreover, we provide evidence suggesting that sales bans reduced e-cigarette usage as well. This evidence suggests that not only are e-cigarettes and smoking regular cigarettes positively related and not substitutes for young people, banning retail sales to minors is an effective policy tool in reducing tobacco use. © 2017 Elsevier B.V. All rights reserved.
Keywords: Electronic cigarettes Youth smoking Retail regulation
1. Introduction Electronic cigarettes (e-cigarettes) have become increasingly popular in the United States, especially among young people. Between 2012 and 2014, e-cigarette use (or vaping) increased fourfold among high school students (Arrazola et al., 2015). Ecigarettes are alluring to young people because they are perceived as harmless (Gilreath et al., 2015), and the array of flavors are more palatable (Kong et al., 2015). The pharmacological effects from ecigarettes, which contain nicotine, could lead to dependence given the high level of susceptibility of adolescent brains (Counotte et al., 2011). This has led many public health advocates to worry about complementarities between e-cigarettes and the more dangerous conventional cigarettes, with the former perhaps serving as a gateway to the latter (Leventhal et al., 2015; Primack et al., 2015; Dutra and Glantz, 2014). Despite the worries associated with e-cigarettes being targeted to minors and the overwhelming growth in popularity among young people, there was no Federal regulation of the product until the FDA announced it would regulate e-cigarettes in mid-2016. The link between e-cigarettes and smoking is not straightforward, however, and existing studies cannot rule out the influence of unobservable factors that might drive both experimentation with
∗ Corresponding author. E-mail address: [email protected] (S. Adams). http://dx.doi.org/10.1016/j.jhealeco.2017.03.003 0167-6296/© 2017 Elsevier B.V. All rights reserved.
e-cigarettes and smoking. Moreover, additional studies have shown that e-cigarettes are actually a relatively safe substitute for conventional cigarettes (Cahn and Siegel (2011) and Polosa et al. (2013)). This implies that e-cigarettes could be part of a harm reduction strategy. In short, the question of whether e-cigarettes and regular cigarettes are substitutes or complements is not resolved. In this study, we test for the effects of restricting youth access to e-cigarettes on smoking traditional combustion cigarettes in a sample of high school seniors using the 2007–2014 Monitoring the Future surveys. If e-cigarettes are a complement to regular cigarettes, we should find that prohibiting sales of e-cigarettes reduces the incidence of adolescents smoking conventional cigarettes. If there is substitution between e-cigarettes and conventional cigarettes, the bans would be counterproductive. Restricting youth access might then increase the prevalence of conventional cigarette smoking, as well as the intensity. Our individual-level evidence suggests that e-cigarette bans do not increase smoking. In fact, the sum of the evidence suggests a decrease in the incidence of smoking. This provides the first causal evidence in population data showing e-cigarettes are likely a complement, rather than a substitute, for smoking combustion cigarettes among adolescents. In terms of smoking intensity, however, the effect of e-cigarette bans is essentially zero. The rationale behind prohibiting sales of e-cigarettes to young people likely rests in the belief that there is some harm to using the product, even if the harm is less than that of conventional cigarettes. If the goods are complements, this would suggest bans are a good
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harm reduction strategy. Another justification for a sales ban would be that e-cigarettes have harmful pollutants that would potentially negatively affect bystanders. This externality justification is the primary reason that smoking is banned in indoor places. The evidence that e-cigarettes have negative externalities is limited. Certainly, the exposure from e-cigarette toxins is less dangerous than conventional cigarettes (Czogala et al., 2014; Schripp et al., 2013). There is evidence, however, of heightened exposure to several known carcinogens for those in a room where e-cigarettes are used (Grana et al., 2014; Schober et al., 2014), and multiple questions exist about the environmental impact of e-cigarette production and waste (Lerner et al., 2015). E-cigarettes also heighten one’s propensity to remain at bars longer, which is potentially dangerous in terms of binge drinking and the associated dangers (Abouk et al., 2016). We are not the first to assess the effect of youth e-cigarette sales bans on smoking using population data. Most notably, Friedman (2015) uses state-level biennial data from the National Survey on Drug Use and Health (2002–2013) to assess the effect of e-cigarette sales bans on the prevalence of smoking among adolescents, and Pesko et al. (2016) uses the Youth Risk Behavior Surveillance System for ninth-twelfth graders from 2007 to 2013, again using aggregated data. Their findings suggest e-cigarettes and conventional cigarettes are substitutes, and the bans are counterproductive. Because they use aggregate data, they also can control for aggregate trends in smoking among older young adults that can legally purchase e-cigarettes after a ban.1 Our more granular individual-level evidence comes to a different conclusion than these two studies, and we discuss the potential reasons for the differences in the final section of the paper. 2. Data We use data from the Monitoring the Future (MTF) surveys from 2007 to 2014. These contain information on approximately 50,000 eight, tenth, and twelfth graders from 420 public and private secondary schools in the United States, fielded annually during the spring semester. The schools are located across 46 states and the District of Columbia and are meant to be representative of the U.S. population. The MTF does not include all states every year, which is a limitation of the study.2 We primarily consider twelfth graders who are underage, which means either younger than 18 or 19, depending on the state of residence. Our restriction to 12th graders is for two reasons. First, past experience with restrictions on tobacco sales has suggested that those closer to the age of majority are likely to purchase cigarettes in retail establishments (Abouk and Adams, 2017). Second, smoking conventional cigarettes and e-cigarette use is more common among older students. We will, however, briefly discuss the results of our estimations for 10th and 8th graders later. Our aim is to capture a time period where we would expect a meaningful change in retail purchases of e-cigarettes in light of a ban. Bans were passed in 2010 in California, Minnesota, New Jersey, New Hampshire, and Utah. The years 2007–2009 give us a three year pre-treatment window for these earlier bans and 2011–2014 give us a four year post treatment window. We know the month the survey questions were posed to the student and her age in months, which allows us to exploit monthly variation in the legality of sales to minors. Table 1 shows the effective dates of the ban in each state, with minimum legal ages specified in the parentheses. The distri-
1 Starr and Hall (2016), however, bring into question whether Friedman (2015) adequately captured pre-existing trends in smoking rates. 2 Our main results are robust to focusing on only states included every year so we do not believe this limitation affects the interpretation of our results.
Fig. 1. Trend in smoking among underage 12th graders 2007–2014. Notes: Data come from the Monitoring the Future.
bution and timing of the bans appear to be exogenous with regard to youth smoking. States with high numbers of youth smokers per capita, like Tennessee and Arkansas, pass bans in the same years as low smoking states, like Colorado and Washington, respectively. Neighboring states, like Maryland and Virginia, pass bans years apart. Pennsylvania has no ban on sales, but every state that borders it does. We also estimated a regression of lagged smoking rates on state bans, and we found no correlation between passage of the laws and smoking rates, further suggesting policy exogeneity. The main variables of interest in our study are 30-day smoking prevalence and intensity. The MTF survey asks whether respondents smoked in the past 30 days and the number of cigarettes they smoke on a daily basis. Those numbers appear in Table 2. The control states, which we define as those states that never passed any restriction on e-cigarettes to minors, have an almost identical smoking rate to the states that passed a ban pre-treatment. Columns (3) and (4) illustrate the statistics in states implementing the sales bans, before and after the ban, respectively. They show that the smoking prevalence declines from 17.4% to 11.5% after the bans take effect. We note that although there were downward trends in smoking among youths nationally over this time period, the reduction implied by Table 2 is particularly large in those states with e-cigarette bans. Fig. 1 offers visual evidence of the effects of e-cigarette sales bans. Among 12th graders, smoking rates were similar across treatment and control states through 2010. This confirms that pre-treatment smoking conditions and trends were not diverging, further suggesting policy exogeneity. We also tested statistically whether the pre-treatment trends were different in the treatment and control states. We do this by first dropping the post-treatment period for the treated states in the sample. Then, we interact year dummies with an indicator variable set to one for treated states and zero otherwise. Finally, we regress the prevalence of smoking on the interaction terms explained above, individual-level explanatory variables (listed in Table 2), state-level policy variables, and state and year-month dummies. A test of the joint significance of the estimated coefficients of the interaction terms provides information on whether the assumption of parallel pretreatment trends in the control and treated states is valid. We failed to reject the null of parallel trends at p = 0.530.3 Fig. 1 also shows that the states even-
3 We acknowledge, however, that the pre-treatment window is shorter than what is ideal for a test like this. This is a limitation of the study, but we have done our best with the data we have to show the assumption of parallel trends holds.
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Table 1 Effective Date of Law Restricting e-cigarette sales to minors. State
Effective Date of Law restricting sales to minors
State
Effective Date of Law restricting sales to minors
Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maryland Minnesota
8/1/2013 (19) 8/22/2012 (19) 9/13/2013 (18) 8/16/2013 (18) 9/27/2010 (18) 3/25/2011 (18) 10/1/2014 (18) 6/12/2014 (18) 7/1/2014 (18) 7/1/2014 (18) 6/27/2013 (18) 7/1/2012 (18) 1/1/2014 (18) 7/1/2013 (18) 7/1/2014 (18) 7/1/2012 (18) 4/10/2014 (18) 5/28/2014 (18) 10/1/2012 (18) 8/1/2010 (18)
Mississippi Missouri Nebraska New Hampshire New Jersey New York North Carolina Ohio Oklahoma Rhode Island South Carolina South Dakota Tennessee Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming
7/1/2013 (18) 9/10/2014 (18) 4/9/2014 (18) 7/31/2010 (18) 3/12/2010 (19) 1/1/2013 (18) 8/1/2013 (18) 8/2/2014 (18) 11/1/2014 (18) 6/30/2014 (18) 6/7/2013 (18) 7/1/2014 (18) 7/1/2011 (18) 5/11/2010 (19) 7/1/2013 (18) 7/1/2014 (18) 7/28/2013 (18) 6/27/2014 (18) 4/20/2012 (18) 3/13/2013 (18)
Source: CDC, Morbidity and Mortality Weekly Report, Vol.63, No.49.
Table 2 Summary statistics for the sample of underage high school seniors, Monitoring the Future. Variables
(1) States without ban
Smoked past 30 days E-cig ban Monthly unemployment rate Tobacco tax Expenditure on tobacco control policy per capita 100% smoke-free ban (state-level) 100% vaping ban (state-level) Accumulated inspections per 1000 pop 12–17 Male 15 year-old 16 year-old 17 year-old 18 year-old Black Hispanic Other Weekly labor income Weekly non-labor income, other sources Mother high school Mother some college Mother college graduate Mother graduate degree Mother educ. Missing Father below high school Father high school Father some college Father college graduate Father educ. Missing Observations
0.1713 0 6.9169 2.2047 8.5273 0.3076 0.0068 2.3391 0.4459 0.0008 0.0095 0.9757 0.0136 0.1137 0.1247 0.1151 69.3688 23.1933 0.2383 0.2159 0.2897 0.1291 0.0336 0.1270 0.2685 0.1702 0.2287 0.0688 25,267
No. of cig | smoke Observations
5.0083 4328
(2) States with ban
(3)
(4)
All months
Before ban
After ban
0.1527 0.3612 7.9856 2.5579 10.1386 0.4620 0.0773 1.9142 0.4431 0.0005 0.0095 0.9464 0.0434 0.1033 0.1889 0.1799 60.8117 24.3562 0.2134 0.2092 0.2790 0.1399 0.0421 0.1503 0.2431 0.1740 0.2223 0.0775 24,550
0.1740 0 7.7347 2.5329 11.1801 0.5110 0.0260 0.9245 0.4407 0.0006 0.0106 0.9579 0.0307 0.1278 0.1722 0.1706 63.0554 25.7753 0.2193 0.2137 0.2721 0.1382 0.0420 0.1498 0.2537 0.1738 0.2172 0.0779 15,683
0.1149 1 8.4294 2.6020 8.2966 0.3754 0.1680 3.6647 0.4474 0.0005 0.0076 0.9259 0.0657 0.0599 0.2185 0.1962 56.8433 21.8464 0.2029 0.2014 0.2913 0.1430 0.0424 0.1512 0.2244 0.1745 0.2313 0.0768 8867
4.5819 3748
4.7303 2729
4.1845 1019
Note: All dollar values are in 2014 prices.
tually passing smoking bans show a steeper reduction in smoking rates than those not passing bans, particularly after 2011, which is when most of them became effective. As a second dependent variable, we construct a smoking intensity measure. The MTF asks respondents “how frequently have you smoked cigarettes during the past 30 days?” The options are: not at all, less than one, 1–5 cigarettes, half a pack, one pack, one and a half, or two and more packs. We interpolate the number of daily
cigarettes smoked by taking the midpoint of the bins, following Abouk and Adams (2017), to form an approximate count of the number of cigarettes smoked daily. The intensity of smoking shows a slight decline in Table 2 for the treated states, which we report in the bottom rows. We also acknowledge that interpolation was one of many ways to utilize this measure and discuss our alternative estimations later.
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One limitation of the MTF data is that prior to 2014, there was no separate e-cigarette question. It is possible that e-cigarette users may have reported yes to “smoking” cigarettes, interpreting the smoking questions as covering both conventional and e-cigarette use. We do not suspect this would differ systematically across control and treatment states, however, so bias is likely not driving our results. However, the interpretation of a reduction in smoking cannot rule out that the e-cigarette sales ban’s effect on e-cigarette use is contributing to the reduction in reported smoking. State-level policy covariates appear in the next several rows and appear to be similar across control and treated states, except for the per capita expenditure on tobacco control policies (in 2014 dollars) and state bans on smoking. Given we suspect that the ecigarette bans are passed separate from other state-level policies on combustible cigarettes, we do not think these controls should do anything but improve the efficiency of our estimates. The remaining columns of Table 2 include the means of the other control variables we will use in the analysis. These are individuallevel covariates that are measured in the MTF. They include a standard set of demographics, log weekly income from the respondent’s labor income and from other sources (allowances, etc.), and parental educational attainment. As our data follow a period of relatively stagnant economic activity, there is a decline in real weekly income. 3. Estimation To estimate the effect of the e-cigarette sales ban on smoking among twelfth graders who are underage, our main estimation uses the following linear probability model4 : S30isam = ˛ + s + ım + a + ωEcigbansam + Z sm ˇ + X isam + εisam (1) S30isam is a dummy variable indicating whether the respondent smoked cigarettes during the past 30 days. The subscripts i, s, a, and m denote individual, state, age, and month, respectively. The subscript a is included since state restrictions on sales vary by age, with some states prohibiting sales under 18 and others under 19. We restrict the sample to those ages that are eventually covered in each state. So, the vast majority of our treatment group sample is under 18, with two states (Alabama and New Jersey) including some 18 year olds. For states with only restrictions on cigarette sales (and not e-cigarettes), those below 18 are included.5 We include a series of age-in-years controls in the regression (a ). The vector s captures state fixed effects and ım captures year-month fixed effects. Ecigbansam is the policy variable that equals one if a state has a ban in place for a given month that prohibits selling e-cigarettes to minors, and zero otherwise. Zsm represents the vector of state-level policy covariates, such as the log of unemployment rate, the log of real tobacco tax, log of real per capita expenditure on tobacco control policy, whether an indoor smoking and vaping ban is in place. For the indoor smoking and vaping bans, we consider the proportion of the state’s population who are subject to a 100% ban in workplaces, restaurants, and bars acquired from the American Nonsmokers’ Rights (ANR) Foundation. We also include the accumulated tobacco retailer
4 An alternative to the linear probability model would be a nonlinear Probit model. However, for models with fixed effects, it raises the incidental parameters problem leading to biased estimates. For more discussion, see Greene et al. (2002). We verify that our results are robust to nonlinear models later in the paper. 5 Alaska and Utah ban sales to anyone under 19 in our sample. However, Alaska is not part of the MTF during our time frame. Utah, which passed a law in 2010 to cover anyone under 19, did not have a school sampled by the MTF after they passed a law. They functionally are a control group in early 2010 for the states passing a ban in early Spring 2010. Removing 18 year olds completely from the sample does not change the results, as shown later.
inspections per capita to ensure that the decline in smoking is not due to the enforcement of the minimum legal age laws for purchasing tobacco following the passage of the 2009 Tobacco Control Act (Abouk and Adams 2017). Xism is a vector of other explanatory variables, including gender, race dummies and ethnicity dummy variables, weekly income (labor and non-labor), and a set of dummy variables for the education level of the mother and father. All prices are adjusted for inflation using Bureau of Labor Statistics’ (BLS) Consumer Price Index (CPI), reflecting all prices in 2014 dollars. We also consider intensity of smoking as an outcome variable in a regression of the form: (Cigdailyisam |S30isam= 1) = ˛ + s + ım + a + Zsm ˇ +Xisam + ωEcigbansam + εisam .
(2)
We estimate initially by OLS. We recognize that the categorical nature of the dependent variable and the truncated specification in Eq. (2) suggests alternative estimations would be appropriate. Even with interpolation, the count data on numbers of cigarette smoked might be more suited for Poisson regression techniques. All of these estimation techniques do not lend themselves to the easy interpretations of OLS, however, so we continue to use linear models in our base specifications and discuss the robustness to model selection later in the paper. 4. Results 4.1. Basic estimations of the effect on smoking Table 3 presents the estimates from model (1) in columns (1)–(3) and model (2) in column (4). In column (1), we include only state and month-year fixed effects. The estimated impact of the policy of restricting sales to minors is negative and significant at the 0.05 level. The estimates suggest that smoking rates decline by 2.01 percentage point after enactment of the ban. Given a mean smoking rate of 16.2%, this implies that the prevalence of smoking declines by about 12% after the ban.6 In column (2), we add the individual level controls, including the age fixed effects, and the e-cigarette ban effect remains strongly negative. The other individual-level covariates have the effects we have come to expect in the smoking literature. Girls smoke less than boys. Whites smoke more than other races and ethnic groups. Smoking increases with income but decreases with parental education. In column (3), we add the state-level policy covariates that might influence smoking rates. These have no observable effect except for smoking bans and tobacco retailer inspections, which show an expected modest, negative effect on smoking. The effect of the tobacco tax is small and not-significant, which is line with work by Hansen et al. (2017) suggesting recently taxes have had minimal effect on adolescent smoking. Most importantly, there is actually a stronger 2.54 percentage point change in the estimated effect of e-cigarette sales bans on smoking, which suggests a 15% reduction in smoking among individuals after the e-cigarettes bans. It is reasonable to question if the number of 17 and 18 year olds using e-cigarettes is enough to reduce the smoking rates by 2.54 percentage points. Arrazola et al. (2015) presented evidence that over 13% of high school students were using e-cigarettes in 2014. We find in our MTF sample that for high school seniors, it is approximately 17%. Regardless of the exact number, there are a substantial enough number of e-cigarette users to allow for a 2.54 percentage
6 Given that 16.2% of underage 12th graders smoke, a −0.0201 percentage point = −0.1240 or 12.4% decline in decline in smoking could be interpreted as −0.0201 0.1621 prevalence of smoking.
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Table 3 Effect of e-cigarette sales ban on smoking of underage high school seniors, linear probability model, 2007–2014 Monitoring the Future Survey. Variables
(1) (2) Smoked past 30 days (mean = 0.162)
(3)
(4) No. cig | smoked (mean = 4.798)
E-cig ban
−0.0201** (0.0097)
−0.0217** (0.0094)
Age-in-year dummies State and month-year dummies
No Yes
0.0340*** (0.0049) −0.1336*** (0.0082) −0.0881*** (0.0056) −0.0468*** (0.0064) 0.0166*** (0.0011) 0.0159*** (0.0016) −0.0204** (0.0089) −0.0174 (0.0113) −0.0441*** (0.0106) −0.0462*** (0.0091) −0.0225** (0.0100) −0.0154* (0.0089) −0.0401*** (0.0109) −0.0549*** (0.0107) −0.0566*** (0.0107) −0.0076 (0.0088) Yes Yes
−0.0254** (0.0097) 0.0453 (0.0353) 0.0332 (0.0248) 0.0159 (0.0127) −0.0225* (0.0125) 0.0019 (0.0223) −0.0009* (0.0005) 0.0339*** (0.0049) −0.1323*** (0.0084) −0.0867*** (0.0058) −0.0464*** (0.0064) 0.0167*** (0.0011) 0.0159*** (0.0016) −0.0205** (0.0089) −0.0173 (0.0113) −0.0441*** (0.0106) −0.0462*** (0.0091) −0.0225** (0.0100) −0.0154* (0.0089) −0.0401*** (0.0109) −0.0549*** (0.0106) −0.0565*** (0.0107) −0.0074 (0.0088) Yes Yes
0.1917 (0.7862) 1.8826 (1.3581) −0.4093 (0.8555) 0.5882 (0.6172) −0.2616 (0.6021) 1.1614 (1.4484) −0.0796*** (0.0195) 1.0375*** (0.1982) −0.9390 (0.7344) −1.7830*** (0.2689) −0.3166 (0.3526) 0.2497*** (0.0718) 0.2876*** (0.0650) −1.1855*** (0.3689) −1.1021** (0.4564) −1.5365*** (0.4634) −1.2390** (0.5393) 1.8579* (1.0693) −1.2263*** (0.4102) −1.4120** (0.5446) −1.9018*** (0.5625) −1.9901*** (0.6574) −0.0079 (0.6441) Yes Yes
Observations R-squared
49,817 0.0203
49,817 0.0538
49,817 0.0544
8076 0.0735
Log(unemployment rate) Log(tobacco tax) Log(rTCPpc + 1) 100% smoke-free ban (state-level) 100% vaping ban (state-level) Accumulated inspections per 1000 pop 12–17 Male Black Hispanic Other Log weekly labor income Log weekly non-labor income Mother high school Mother some college Mother college graduate Mother graduate degree Mother educ. Missing Father high school Father some college Father college graduate Father graduate degree Father educ. Missing
Note: Each column is a separate regression. Estimates are weighted using MTF sampling weights and standard errors are clustered at the state level. * p < 0.1. ** p < 0.05. *** p < 0.01.
point reduction in smoking. So, we are confident that the magnitude of these estimated effects are plausible. In column (4), we assess whether there is any effect on intensity of smoking among those who smoke.7 If there is some substitutability among dual users of both types of tobacco products, however, there might be a positive effect on intensity that is not reflected in the estimates at the extensive margin. In column (4), we estimate the effect of e-cigarette sales bans on the number of cigarettes smoked, conditional on being a smoker (Eq. (2)). We find no signif-
7
We caution that some of the state-year cells for these estimations become small so these results are somewhat less informative than the estimates at the extensive margin.
icant effect on smoking intensity among smokers.8 So, the effect is observed entirely at the extensive margin, with prevalence of smoking being reduced by the sales bans. This is suggestive that e-cigarettes and traditional cigarettes are complements. Banning sales of e-cigarettes curtails smoking combustion cigarettes at the extensive margin. At the intensive margin, there is no evidence of substitution.
8 We tested for whether non-linear models resulted in different results, including Tobit to assess the impact of truncated data, interval regression as an alternative to interpolation, and Poisson regression. In no case was there any significant effect found on the numbers of cigarettes smoked.
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Table 4 Effect of e-cigarettes sales ban on prevalence of e-cigarette use in various grades, linear probability model, 2014 Monitoring the Future Survey.
E-cig ban Observations
Underage 12th graders (mean = 0.171)
10th graders (mean = 0.162)
8th graders (0.087)
−0.1017*** (0.0139) 3523
0.0677 (0.0426) 3770
0.0021 (0.0167) 3908
Note: Each cell reports the results from a single regression. Only 2014 data used for the analysis since e-cigarette use information is available in this year only. All individual-level covariates listed in previous tables are taken into account. State and month dummies are also included in each model. Estimates are weighted using MTF sampling weights and standard errors are clustered at the state level. * p < 0.1. ** p < 0.05. *** p < 0.01.
4.2. Confirming the mechanism of the effects on smoking and robustness checks Using the most straightforward approach, we estimate a significant decline in smoking rates. The explanation most sensible for these finding is that e-cigarettes and conventional cigarettes are complements and are dually used. We can offer additional suggestive evidence that these goods are indeed complements. In the 2014 MTF, questions on e-cigarette use were asked for the first time. About 6 in 10 current cigarette smokers were also users of e-cigarettes among the high school students in our sample. Dual use of the product is therefore quite high and complementarities are likely.9 Dual use is also high among 10th graders. However, less than half of eighth grade smokers also use e-cigarettes, so dual use is higher among the older students. We also note that the highest prevalence of use of both products among 12th graders was a reason our main analysis focusses on high school seniors. We also test whether the monthly variation in implementing retail sales bans in 2014 had an impact on the prevalence of ecigarette usage. We find significant and substantial evidence that it does for twelfth graders and report this result in Table 4. There is not a decrease in e-cigarette use among 8th and 10th graders. This is consistent with both the lower rates of e-cigarette use among younger adolescents, as well as their lower likelihood of using retail establishments to purchase the product. The smaller effect on 8th graders might actually be related to their lower amount of dual use. We caution that results are merely suggestive, as the data only allow for identification from 2014, which substantially limits identification to within state monthly changes that year.10 They do suggest that we should see stronger smoking effects for 12th graders, a point we return to in the final section. In Table 5, we engage in some additional tests to confirm the robustness of our results. We also separate the effects by gender to assess whether there are differing effects for boys and girls. In row (1) of Table 5, we add state-specific linear time trends to the basic specification (1). These results suggest even more statistically significant effects than those reported in Table 3 and more substantial effects for boys than girls. The linear time trends account for the potential that pre-existing trends might be contributing to the estimated relationship in the previous tables. Another option that accounts for differences in the treatment and control states that pass bans is to simply remove from the estimation any state that passes no restrictions on sales to minors during our sample period. This means that identification comes purely from the timing of pas-
9 Future work will have more to say on this question as the MTF collects additional years of e-cigarette data. 10 Only four states potentially provide identification as they changed their ecigarette legal status within the Spring of 2014.
sage among those states that restrict sales to young people. In row (2), we show that the results are similar when this restriction is made and the effects remain substantial for boys. We note that the larger impact for boys is expected and provides more support for the plausibility of our proposed mechanism. First, boys use e-cigarettes more than girls and smoke more. We therefore expect more of an effect for boys. Also, previous research suggests that girls are more successful than boys at being able to obtain illicit products while underage (Abouk and Adams, 2017; Grucza et al., 2013). So, we would expect less of an impact on their use of products based on the legal costs of purchasing. We caution, however, that the effects for girls remains negative throughout the series of tests and in some cases is not substantially below the effect for boys. In row (3), we test whether there was any anticipatory effect of the ban by moving the date of sales bans 6 months forward. For example, the state of New York banned e-cigarette sales to minors effective January 1st , 2013. We define a new variable in which a dummy variable is set to one in New York from July 1st , 2012 to December 31st 2012. If our results are not due to preexisting trends and there are no anticipatory effects, the estimated coefficients should be small. The effect indeed is not large. The effect of the sales ban itself remains substantively the same when the anticipatory effect is added. We consider two other estimations, the first of which divides our treatment group into early and late bans, with the 2010 and 2011 bans considered “early.” If e-cigarette bans were part of some larger anti-smoking movement that reached some states earlier than later, we might expect the bans to have differing effects depending on when they were passed. This is not the case, as there is no meaningful difference based on when bans were passed. Finally, there were some 18 year olds in our sample that could be part of the pre-ban treatment group and treatment group in states that consider 19 the age of majority for tobacco purchases. Since 18 year olds are not necessarily similar to other twelfth graders, we dropped these from one set of estimations. In row (5), we see that removing 18 year olds has no appreciable impact on our results. Finally, in row (6) we show the e-cigarette effects for boys and girls. There is no discernable difference, unlike with the effects on cigarette smoking. The reliance on just 2014 data and cross-month variation might be a reason that these results are not stronger for boys, as we would expect. We expect that future data on e-cigarette use across multiple years will shed more light on this question.
5. Discussion and conclusion In this paper, we offer the first national-level population estimates that suggest electronic cigarettes threaten to undo decades of progress in reducing smoking among adolescents. We find evidence consistent with e-cigarettes and combustion cigarettes being complements among high school seniors. Specifically, we show that curtailing access to e-cigarettes among minors through retail sales restrictions reduces traditional smoking, and we provide suggestive evidence it reduces e-cigarette use as well. Our results and conclusions differ from Friedman (2015) and Pesko et al. (2016), the two other existing studies that estimate the effects of e-cigarettes on young people. Those studies take a different approach than ours. In particular, Friedman (2015) uses state-level biennial data from the National Survey on Drug Use and Health (NSDUH) (2002–2013) to assess the effect of e-cigarette sales bans on the prevalence of smoking among adolescents, and Pesko et al. (2016) uses the Youth Risk Behavior Surveillance System (YRBSS) for ninth-twelfth graders from 2007 to 2013. Both pool younger and older age groups (12–17 year olds and 9th-12th graders, respectively). They also control for aggregate smoking
R. Abouk, S. Adams / Journal of Health Economics 54 (2017) 17–24
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Table 5 Robustness Checks for underage 12th graders on Effects of E-cigarette Bans on Smoking Incidence, 2007–2014 Monitoring the Future Survey.
(1) Including state-specific trend (2) Treated states only (3) Adding 6 month anticipatory effect 6 month period before ban Ban (4) Early vs. late ban Ban 2010–2011 Ban 2012–2014 (5) drop underage 18 year-old (6) Effect of e-cigarette ban on e-cigarette use (2014 only)
Overall
Male
Female
−0.0383*** (0.0118) −0.0439** (0.0164)
−0.0721*** (0.0187) −0.0595** (0.0210)
−0.0142 (0.0146) −0.0310 (0.0188)
−0.0059 (0.0081) −0.0279** (0.0115)
0.0015 (0.0157) −0.0340** (0.0152)
−0.0122 (0.0145) −0.0245 (0.0149)
−0.0393 (0.0169)** −0.0378 (0.0123)*** −0.0253** (0.0100)
−0.0807 (0.0195)*** −0.0676 (0.0212)*** −0.0343** (0.0134)
−0.0057 (0.0212) −0.0181 (0.0153) −0.0196 (0.0129)
−0.1017*** (0.0139)
−0.0927*** (0.0263)
−0.0986*** (0.0184)
Note: Each cell reports the effect of an e-cig sales ban using OLS estimation. All individual and state explanatory variables from Table 2 along with state and month-year dummies are included. Results in row (6) do not include state-level policy variables as the analysis is done using 2014 data only. In row (1), a linear month-year, state-specific trend is considered. The sample includes underage 12th graders. MTF sample weights are used in all regression and numbers in parenthesis are standard errors clustered at state level. * p < 0.1. ** p < 0.05. *** p < 0.01.
trends among young adults. The more granular data we use exploits monthly variation that can more exactly identify when a sales ban takes effect and its effect on potential smokers. We find significant negative effects using individual-level data for 12th graders. We additionally tested for effects on 10th graders and found smaller but still significant reductions in smoking. We found virtually zero effect on eight graders. We contend this likely reflects less usage among younger children. Given our results differ dramatically from Friedman (2015) and Pesko et al. (2016), future work will determine what explains the difference once more data become available. Since the individuallevel MTF used by us and the aggregated NSDUH and YRBSS data used by Friedman and Pesko et al. are not comparable, replication exercises are not yet possible. Pooling younger and older adolescents may lead to smaller effects, as we find that eighth graders are not affected by smoking bans in the MTF. Yet, we do not observe 9th or 11th graders in our sample. Aggregating smoking rates in the MTF by state and time for 12th graders and estimating the effect of bans results in weaker but still negative estimates. Future work should explore whether aggregate or individual-level are driving the different estimates or whether pooling younger and older respondents is leading to the different results. The MTF data have several limitations as well that future work might be able to overcome. First, we cannot capture the dynamic effects of the e-cigarette bans because we do not follow the same young people over time. In particular, we do not know if we are estimating a reduction in smoking because new users are no longer choosing to smoke or past users stop using all tobacco products following an e-cigarette ban. Future work will hopefully be able to follow the same individuals over time to capture dynamic changes in their conventional cigarette use in response to bans. This could perhaps help determine if e-cigarettes are a gateway to smoking regular cigarettes or the products are predominantly dually-used. Second, the MTF only asks questions of e-cigarette smokers beginning in 2014. Future work should address separately the effect of bans on e-cigarettes and combustible cigarettes once a longer panel of individual-level data on e-cigarette and traditional cigarette use becomes available. Currently, without explicit measures of e-cigarette and combustible cigarette use prior to 2014,
the interpretation of a reduction in smoking cannot rule out that the e-cigarette sales ban’s effect on e-cigarette use is contributing to the reduction in reported smoking. From a policy perspective, we think our results as a whole do offer support to the idea that sales bans to minors are a viable policy strategy to limit the use of combustion cigarettes among young people. We also show that the bans reduce the use of e-cigarettes themselves, thus indicating that sales bans on e-cigarettes are likely an effective tool for reducing exposure to tobacco in multiple forms. To the extent that e-cigarettes create negative externalities, the welfare gains from these sales bans are likely large as well. The limited evidence to date on the externalities associated with ecigarettes at the current time, however, limit our ability to make strong statements on this point.
References Abouk, R., Adams, S.J., 2017. Compliance inspections of tobacco retailers and youth smoking American. Am. J. Health Econ., 10–32. Abouk, R., Adams, S.J., Moghtaderi, A., 2016. E-cigarettes and Excessive Drinking, Unpublished Paper. Arrazola, R.A., Singh, T., Corey, C.G., Husten, C.G., Neff, L.J., Apelberg, B.J., McAfee, T., 2015. Tobacco use among middle and high school students—United States, 2011–2014. MMWR Morb. Mortal. Wkly. Rep. 64 (14), 381–385. Cahn, Z., Siegel, M., 2011. Electronic cigarettes as a harm reduction strategy for tobacco control: a step forward or a repeat of past mistakes? J. Public Health Policy 32 (1), 16–32. Counotte, D.S., Smit, A.B., Pattij, T., Spijker, S., 2011. Development of the motivational system during adolescence, and its sensitivity to disruption by nicotine. Dev. Cognit. Neurosci. 1 (4), 430–443. Czogala, J., Goniewicz, M.L., Fidelus, B., Zielinska-Danch, W., Travers, M.J., Sobczak, A., 2014. Secondhand exposure to vapors from electronic cigarettes. Nicotine Tob. Res. 16 (6), 655–662. Dutra, L.M., Glantz, S.A., 2014. Electronic cigarettes and conventional cigarette use among US adolescents: a cross-sectional study. JAMA Pediatr. 168 (7), 610–617, http://dx.doi.org/10.1001/jamapediatrics.2013.5488. Friedman, A.S., 2015. How does electronic cigarette access affect adolescent smoking? J. Health Econ. 44, 300–308. Gilreath, T.D., Leventhal, A., Barrington-Trimis, J.L., Unger, J.B., Cruz, T.B., Berhane, K., Pentz, A.M., 2015. Patterns of alternative tobacco product use: emergence of hookah and E-cigarettes as preferred products amongst youth. J. Adolesc. Health 58 (2), 181-1. Grana, R., Benowitz, N., Glantz, S.A., (13 May 2014). E-cigarettes: a scientific review. Circulation 129 (19), 1972–1986. Greene, W., Han, C., Schmidt, P., 2002. The Bias of the Fixed Effects Estimator in Nonlinear Models. Stern School of Business, NYU (Unpublished Manuscript).
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Grucza, R.A., Plunk, A.D., Hipp, P.R., Cavazos-Rehg, P., Krauss, M.J., Brownson, R.C., Bierut, L.J., 2013. Long-term effects of laws governing youth access to tobacco. Am. J. Public Health 103 (8), 1493–1499. Hansen, B., Sabia, J., Rees, D., 2017. Have Cigarette Taxes Lost Their Bite? New estimates of the relationship between cigarette taxes and youth smoking. Am. J. Health Econ. 3 (1), 60–75. Kong, G., Morean, M.E., Cavallo, D.A., Camenga, D.R., Krishnan-Sarin, S., 2014. Reasons for electronic cigarette experimentation and discontinuation among adolescents and young adults. Nicotine Tob. Res. 17 (7), 847–854. Lerner, C.A., Sundar, I.K., Watson, R.M., Elder, A., Jones, R., Done, D., Rahman, I., 2015. Environmental health hazards of e-cigarettes and their components: oxidants and copper in e-cigarette aerosols. Environ. Pollut. 198, 100–107. Leventhal, A.M., Strong, D.R., Kirkpatrick, M.G., et al., 2015. Association of electronic cigarette use with initiation of combustible tobacco product smoking in early adolescence. JAMA 314 (7), 700–707, http://dx.doi.org/10. 1001/jama.2015.8950.
Pesko, M.F., Hughes, J.M., Faisal, F.S., 2016. The influence of electronic cigarette age purchasing restrictions on adolescent tobacco and marijuana use. Prev. Med. 87, 207–212. Polosa, R., Rodu, B., Caponnetto, P., Maglia, M., Raciti, C., 2013. A fresh look at tobacco harm reduction: the case for the electronic cigarette. Harm Reduct. J. 10 (1), 19. Primack, B.A., Soneji, S., Stoolmiller, M., Fine, M.J., Sargent, J.D., 2015. Progression to traditional cigarette smoking after electronic cigarette use among US adolescents and young adults. JAMA Pediatr. 169 (11), 1018–1023, http://dx. doi.org/10.1001/jamapediatrics.2015.1742. Schober, W., Szendrei, K., Matzen, W., Osiander-Fuchs, H., Heitmann, D., Schettgen, T., Jörres, R. A., Fromme, H. (July 01, 2014). Use of electronic cigarettes (e-cigarettes) impairs indoor air quality and increases FeNO levels of e-cigarette consumers. Int. J. Hyg. Environ. Health 217 (6), 628–637. Schripp, T., Markewitz, D., Uhde, E., Salthammer, T., 2013. Does e-cigarette consumption cause passive vaping? Indoor Air 2013 (23), 25–31. Starr, M., Hall, T., 2016. Do Restrictions on Sales of E-cigarettes to Youth Really Increase Teen Smoking?, Unpublished Manuscript.
Contents lists available at ScienceDirect
Journal of Health Economics journal homepage: www.elsevier.com/locate/econbase
Bans on electronic cigarette sales to minors and smoking among high school students Rahi Abouk a , Scott Adams b,∗ a b
Department of Economics, Finance and Global Business, William Paterson University, United States Department of Economics, University of Wisconsin-Milwaukee, United States
a r t i c l e
i n f o
Article history: Received 5 January 2016 Received in revised form 6 January 2017 Accepted 8 March 2017 Available online 18 March 2017 JEL classifications: I18 K42
a b s t r a c t Many states have banned electronic cigarette sales to minors under the rationale that using e-cigarettes leads to smoking traditional combustion cigarettes. Such sales bans would be counterproductive, however, if e-cigarettes and traditional cigarettes are substitutes, as bans might push teenagers back to smoking the more dangerous combustion cigarettes. We provide evidence that these sales bans reduce the incidence of smoking conventional cigarettes among high school seniors. Moreover, we provide evidence suggesting that sales bans reduced e-cigarette usage as well. This evidence suggests that not only are e-cigarettes and smoking regular cigarettes positively related and not substitutes for young people, banning retail sales to minors is an effective policy tool in reducing tobacco use. © 2017 Elsevier B.V. All rights reserved.
Keywords: Electronic cigarettes Youth smoking Retail regulation
1. Introduction Electronic cigarettes (e-cigarettes) have become increasingly popular in the United States, especially among young people. Between 2012 and 2014, e-cigarette use (or vaping) increased fourfold among high school students (Arrazola et al., 2015). Ecigarettes are alluring to young people because they are perceived as harmless (Gilreath et al., 2015), and the array of flavors are more palatable (Kong et al., 2015). The pharmacological effects from ecigarettes, which contain nicotine, could lead to dependence given the high level of susceptibility of adolescent brains (Counotte et al., 2011). This has led many public health advocates to worry about complementarities between e-cigarettes and the more dangerous conventional cigarettes, with the former perhaps serving as a gateway to the latter (Leventhal et al., 2015; Primack et al., 2015; Dutra and Glantz, 2014). Despite the worries associated with e-cigarettes being targeted to minors and the overwhelming growth in popularity among young people, there was no Federal regulation of the product until the FDA announced it would regulate e-cigarettes in mid-2016. The link between e-cigarettes and smoking is not straightforward, however, and existing studies cannot rule out the influence of unobservable factors that might drive both experimentation with
∗ Corresponding author. E-mail address: [email protected] (S. Adams). http://dx.doi.org/10.1016/j.jhealeco.2017.03.003 0167-6296/© 2017 Elsevier B.V. All rights reserved.
e-cigarettes and smoking. Moreover, additional studies have shown that e-cigarettes are actually a relatively safe substitute for conventional cigarettes (Cahn and Siegel (2011) and Polosa et al. (2013)). This implies that e-cigarettes could be part of a harm reduction strategy. In short, the question of whether e-cigarettes and regular cigarettes are substitutes or complements is not resolved. In this study, we test for the effects of restricting youth access to e-cigarettes on smoking traditional combustion cigarettes in a sample of high school seniors using the 2007–2014 Monitoring the Future surveys. If e-cigarettes are a complement to regular cigarettes, we should find that prohibiting sales of e-cigarettes reduces the incidence of adolescents smoking conventional cigarettes. If there is substitution between e-cigarettes and conventional cigarettes, the bans would be counterproductive. Restricting youth access might then increase the prevalence of conventional cigarette smoking, as well as the intensity. Our individual-level evidence suggests that e-cigarette bans do not increase smoking. In fact, the sum of the evidence suggests a decrease in the incidence of smoking. This provides the first causal evidence in population data showing e-cigarettes are likely a complement, rather than a substitute, for smoking combustion cigarettes among adolescents. In terms of smoking intensity, however, the effect of e-cigarette bans is essentially zero. The rationale behind prohibiting sales of e-cigarettes to young people likely rests in the belief that there is some harm to using the product, even if the harm is less than that of conventional cigarettes. If the goods are complements, this would suggest bans are a good
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harm reduction strategy. Another justification for a sales ban would be that e-cigarettes have harmful pollutants that would potentially negatively affect bystanders. This externality justification is the primary reason that smoking is banned in indoor places. The evidence that e-cigarettes have negative externalities is limited. Certainly, the exposure from e-cigarette toxins is less dangerous than conventional cigarettes (Czogala et al., 2014; Schripp et al., 2013). There is evidence, however, of heightened exposure to several known carcinogens for those in a room where e-cigarettes are used (Grana et al., 2014; Schober et al., 2014), and multiple questions exist about the environmental impact of e-cigarette production and waste (Lerner et al., 2015). E-cigarettes also heighten one’s propensity to remain at bars longer, which is potentially dangerous in terms of binge drinking and the associated dangers (Abouk et al., 2016). We are not the first to assess the effect of youth e-cigarette sales bans on smoking using population data. Most notably, Friedman (2015) uses state-level biennial data from the National Survey on Drug Use and Health (2002–2013) to assess the effect of e-cigarette sales bans on the prevalence of smoking among adolescents, and Pesko et al. (2016) uses the Youth Risk Behavior Surveillance System for ninth-twelfth graders from 2007 to 2013, again using aggregated data. Their findings suggest e-cigarettes and conventional cigarettes are substitutes, and the bans are counterproductive. Because they use aggregate data, they also can control for aggregate trends in smoking among older young adults that can legally purchase e-cigarettes after a ban.1 Our more granular individual-level evidence comes to a different conclusion than these two studies, and we discuss the potential reasons for the differences in the final section of the paper. 2. Data We use data from the Monitoring the Future (MTF) surveys from 2007 to 2014. These contain information on approximately 50,000 eight, tenth, and twelfth graders from 420 public and private secondary schools in the United States, fielded annually during the spring semester. The schools are located across 46 states and the District of Columbia and are meant to be representative of the U.S. population. The MTF does not include all states every year, which is a limitation of the study.2 We primarily consider twelfth graders who are underage, which means either younger than 18 or 19, depending on the state of residence. Our restriction to 12th graders is for two reasons. First, past experience with restrictions on tobacco sales has suggested that those closer to the age of majority are likely to purchase cigarettes in retail establishments (Abouk and Adams, 2017). Second, smoking conventional cigarettes and e-cigarette use is more common among older students. We will, however, briefly discuss the results of our estimations for 10th and 8th graders later. Our aim is to capture a time period where we would expect a meaningful change in retail purchases of e-cigarettes in light of a ban. Bans were passed in 2010 in California, Minnesota, New Jersey, New Hampshire, and Utah. The years 2007–2009 give us a three year pre-treatment window for these earlier bans and 2011–2014 give us a four year post treatment window. We know the month the survey questions were posed to the student and her age in months, which allows us to exploit monthly variation in the legality of sales to minors. Table 1 shows the effective dates of the ban in each state, with minimum legal ages specified in the parentheses. The distri-
1 Starr and Hall (2016), however, bring into question whether Friedman (2015) adequately captured pre-existing trends in smoking rates. 2 Our main results are robust to focusing on only states included every year so we do not believe this limitation affects the interpretation of our results.
Fig. 1. Trend in smoking among underage 12th graders 2007–2014. Notes: Data come from the Monitoring the Future.
bution and timing of the bans appear to be exogenous with regard to youth smoking. States with high numbers of youth smokers per capita, like Tennessee and Arkansas, pass bans in the same years as low smoking states, like Colorado and Washington, respectively. Neighboring states, like Maryland and Virginia, pass bans years apart. Pennsylvania has no ban on sales, but every state that borders it does. We also estimated a regression of lagged smoking rates on state bans, and we found no correlation between passage of the laws and smoking rates, further suggesting policy exogeneity. The main variables of interest in our study are 30-day smoking prevalence and intensity. The MTF survey asks whether respondents smoked in the past 30 days and the number of cigarettes they smoke on a daily basis. Those numbers appear in Table 2. The control states, which we define as those states that never passed any restriction on e-cigarettes to minors, have an almost identical smoking rate to the states that passed a ban pre-treatment. Columns (3) and (4) illustrate the statistics in states implementing the sales bans, before and after the ban, respectively. They show that the smoking prevalence declines from 17.4% to 11.5% after the bans take effect. We note that although there were downward trends in smoking among youths nationally over this time period, the reduction implied by Table 2 is particularly large in those states with e-cigarette bans. Fig. 1 offers visual evidence of the effects of e-cigarette sales bans. Among 12th graders, smoking rates were similar across treatment and control states through 2010. This confirms that pre-treatment smoking conditions and trends were not diverging, further suggesting policy exogeneity. We also tested statistically whether the pre-treatment trends were different in the treatment and control states. We do this by first dropping the post-treatment period for the treated states in the sample. Then, we interact year dummies with an indicator variable set to one for treated states and zero otherwise. Finally, we regress the prevalence of smoking on the interaction terms explained above, individual-level explanatory variables (listed in Table 2), state-level policy variables, and state and year-month dummies. A test of the joint significance of the estimated coefficients of the interaction terms provides information on whether the assumption of parallel pretreatment trends in the control and treated states is valid. We failed to reject the null of parallel trends at p = 0.530.3 Fig. 1 also shows that the states even-
3 We acknowledge, however, that the pre-treatment window is shorter than what is ideal for a test like this. This is a limitation of the study, but we have done our best with the data we have to show the assumption of parallel trends holds.
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Table 1 Effective Date of Law Restricting e-cigarette sales to minors. State
Effective Date of Law restricting sales to minors
State
Effective Date of Law restricting sales to minors
Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maryland Minnesota
8/1/2013 (19) 8/22/2012 (19) 9/13/2013 (18) 8/16/2013 (18) 9/27/2010 (18) 3/25/2011 (18) 10/1/2014 (18) 6/12/2014 (18) 7/1/2014 (18) 7/1/2014 (18) 6/27/2013 (18) 7/1/2012 (18) 1/1/2014 (18) 7/1/2013 (18) 7/1/2014 (18) 7/1/2012 (18) 4/10/2014 (18) 5/28/2014 (18) 10/1/2012 (18) 8/1/2010 (18)
Mississippi Missouri Nebraska New Hampshire New Jersey New York North Carolina Ohio Oklahoma Rhode Island South Carolina South Dakota Tennessee Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming
7/1/2013 (18) 9/10/2014 (18) 4/9/2014 (18) 7/31/2010 (18) 3/12/2010 (19) 1/1/2013 (18) 8/1/2013 (18) 8/2/2014 (18) 11/1/2014 (18) 6/30/2014 (18) 6/7/2013 (18) 7/1/2014 (18) 7/1/2011 (18) 5/11/2010 (19) 7/1/2013 (18) 7/1/2014 (18) 7/28/2013 (18) 6/27/2014 (18) 4/20/2012 (18) 3/13/2013 (18)
Source: CDC, Morbidity and Mortality Weekly Report, Vol.63, No.49.
Table 2 Summary statistics for the sample of underage high school seniors, Monitoring the Future. Variables
(1) States without ban
Smoked past 30 days E-cig ban Monthly unemployment rate Tobacco tax Expenditure on tobacco control policy per capita 100% smoke-free ban (state-level) 100% vaping ban (state-level) Accumulated inspections per 1000 pop 12–17 Male 15 year-old 16 year-old 17 year-old 18 year-old Black Hispanic Other Weekly labor income Weekly non-labor income, other sources Mother high school Mother some college Mother college graduate Mother graduate degree Mother educ. Missing Father below high school Father high school Father some college Father college graduate Father educ. Missing Observations
0.1713 0 6.9169 2.2047 8.5273 0.3076 0.0068 2.3391 0.4459 0.0008 0.0095 0.9757 0.0136 0.1137 0.1247 0.1151 69.3688 23.1933 0.2383 0.2159 0.2897 0.1291 0.0336 0.1270 0.2685 0.1702 0.2287 0.0688 25,267
No. of cig | smoke Observations
5.0083 4328
(2) States with ban
(3)
(4)
All months
Before ban
After ban
0.1527 0.3612 7.9856 2.5579 10.1386 0.4620 0.0773 1.9142 0.4431 0.0005 0.0095 0.9464 0.0434 0.1033 0.1889 0.1799 60.8117 24.3562 0.2134 0.2092 0.2790 0.1399 0.0421 0.1503 0.2431 0.1740 0.2223 0.0775 24,550
0.1740 0 7.7347 2.5329 11.1801 0.5110 0.0260 0.9245 0.4407 0.0006 0.0106 0.9579 0.0307 0.1278 0.1722 0.1706 63.0554 25.7753 0.2193 0.2137 0.2721 0.1382 0.0420 0.1498 0.2537 0.1738 0.2172 0.0779 15,683
0.1149 1 8.4294 2.6020 8.2966 0.3754 0.1680 3.6647 0.4474 0.0005 0.0076 0.9259 0.0657 0.0599 0.2185 0.1962 56.8433 21.8464 0.2029 0.2014 0.2913 0.1430 0.0424 0.1512 0.2244 0.1745 0.2313 0.0768 8867
4.5819 3748
4.7303 2729
4.1845 1019
Note: All dollar values are in 2014 prices.
tually passing smoking bans show a steeper reduction in smoking rates than those not passing bans, particularly after 2011, which is when most of them became effective. As a second dependent variable, we construct a smoking intensity measure. The MTF asks respondents “how frequently have you smoked cigarettes during the past 30 days?” The options are: not at all, less than one, 1–5 cigarettes, half a pack, one pack, one and a half, or two and more packs. We interpolate the number of daily
cigarettes smoked by taking the midpoint of the bins, following Abouk and Adams (2017), to form an approximate count of the number of cigarettes smoked daily. The intensity of smoking shows a slight decline in Table 2 for the treated states, which we report in the bottom rows. We also acknowledge that interpolation was one of many ways to utilize this measure and discuss our alternative estimations later.
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One limitation of the MTF data is that prior to 2014, there was no separate e-cigarette question. It is possible that e-cigarette users may have reported yes to “smoking” cigarettes, interpreting the smoking questions as covering both conventional and e-cigarette use. We do not suspect this would differ systematically across control and treatment states, however, so bias is likely not driving our results. However, the interpretation of a reduction in smoking cannot rule out that the e-cigarette sales ban’s effect on e-cigarette use is contributing to the reduction in reported smoking. State-level policy covariates appear in the next several rows and appear to be similar across control and treated states, except for the per capita expenditure on tobacco control policies (in 2014 dollars) and state bans on smoking. Given we suspect that the ecigarette bans are passed separate from other state-level policies on combustible cigarettes, we do not think these controls should do anything but improve the efficiency of our estimates. The remaining columns of Table 2 include the means of the other control variables we will use in the analysis. These are individuallevel covariates that are measured in the MTF. They include a standard set of demographics, log weekly income from the respondent’s labor income and from other sources (allowances, etc.), and parental educational attainment. As our data follow a period of relatively stagnant economic activity, there is a decline in real weekly income. 3. Estimation To estimate the effect of the e-cigarette sales ban on smoking among twelfth graders who are underage, our main estimation uses the following linear probability model4 : S30isam = ˛ + s + ım + a + ωEcigbansam + Z sm ˇ + X isam + εisam (1) S30isam is a dummy variable indicating whether the respondent smoked cigarettes during the past 30 days. The subscripts i, s, a, and m denote individual, state, age, and month, respectively. The subscript a is included since state restrictions on sales vary by age, with some states prohibiting sales under 18 and others under 19. We restrict the sample to those ages that are eventually covered in each state. So, the vast majority of our treatment group sample is under 18, with two states (Alabama and New Jersey) including some 18 year olds. For states with only restrictions on cigarette sales (and not e-cigarettes), those below 18 are included.5 We include a series of age-in-years controls in the regression (a ). The vector s captures state fixed effects and ım captures year-month fixed effects. Ecigbansam is the policy variable that equals one if a state has a ban in place for a given month that prohibits selling e-cigarettes to minors, and zero otherwise. Zsm represents the vector of state-level policy covariates, such as the log of unemployment rate, the log of real tobacco tax, log of real per capita expenditure on tobacco control policy, whether an indoor smoking and vaping ban is in place. For the indoor smoking and vaping bans, we consider the proportion of the state’s population who are subject to a 100% ban in workplaces, restaurants, and bars acquired from the American Nonsmokers’ Rights (ANR) Foundation. We also include the accumulated tobacco retailer
4 An alternative to the linear probability model would be a nonlinear Probit model. However, for models with fixed effects, it raises the incidental parameters problem leading to biased estimates. For more discussion, see Greene et al. (2002). We verify that our results are robust to nonlinear models later in the paper. 5 Alaska and Utah ban sales to anyone under 19 in our sample. However, Alaska is not part of the MTF during our time frame. Utah, which passed a law in 2010 to cover anyone under 19, did not have a school sampled by the MTF after they passed a law. They functionally are a control group in early 2010 for the states passing a ban in early Spring 2010. Removing 18 year olds completely from the sample does not change the results, as shown later.
inspections per capita to ensure that the decline in smoking is not due to the enforcement of the minimum legal age laws for purchasing tobacco following the passage of the 2009 Tobacco Control Act (Abouk and Adams 2017). Xism is a vector of other explanatory variables, including gender, race dummies and ethnicity dummy variables, weekly income (labor and non-labor), and a set of dummy variables for the education level of the mother and father. All prices are adjusted for inflation using Bureau of Labor Statistics’ (BLS) Consumer Price Index (CPI), reflecting all prices in 2014 dollars. We also consider intensity of smoking as an outcome variable in a regression of the form: (Cigdailyisam |S30isam= 1) = ˛ + s + ım + a + Zsm ˇ +Xisam + ωEcigbansam + εisam .
(2)
We estimate initially by OLS. We recognize that the categorical nature of the dependent variable and the truncated specification in Eq. (2) suggests alternative estimations would be appropriate. Even with interpolation, the count data on numbers of cigarette smoked might be more suited for Poisson regression techniques. All of these estimation techniques do not lend themselves to the easy interpretations of OLS, however, so we continue to use linear models in our base specifications and discuss the robustness to model selection later in the paper. 4. Results 4.1. Basic estimations of the effect on smoking Table 3 presents the estimates from model (1) in columns (1)–(3) and model (2) in column (4). In column (1), we include only state and month-year fixed effects. The estimated impact of the policy of restricting sales to minors is negative and significant at the 0.05 level. The estimates suggest that smoking rates decline by 2.01 percentage point after enactment of the ban. Given a mean smoking rate of 16.2%, this implies that the prevalence of smoking declines by about 12% after the ban.6 In column (2), we add the individual level controls, including the age fixed effects, and the e-cigarette ban effect remains strongly negative. The other individual-level covariates have the effects we have come to expect in the smoking literature. Girls smoke less than boys. Whites smoke more than other races and ethnic groups. Smoking increases with income but decreases with parental education. In column (3), we add the state-level policy covariates that might influence smoking rates. These have no observable effect except for smoking bans and tobacco retailer inspections, which show an expected modest, negative effect on smoking. The effect of the tobacco tax is small and not-significant, which is line with work by Hansen et al. (2017) suggesting recently taxes have had minimal effect on adolescent smoking. Most importantly, there is actually a stronger 2.54 percentage point change in the estimated effect of e-cigarette sales bans on smoking, which suggests a 15% reduction in smoking among individuals after the e-cigarettes bans. It is reasonable to question if the number of 17 and 18 year olds using e-cigarettes is enough to reduce the smoking rates by 2.54 percentage points. Arrazola et al. (2015) presented evidence that over 13% of high school students were using e-cigarettes in 2014. We find in our MTF sample that for high school seniors, it is approximately 17%. Regardless of the exact number, there are a substantial enough number of e-cigarette users to allow for a 2.54 percentage
6 Given that 16.2% of underage 12th graders smoke, a −0.0201 percentage point = −0.1240 or 12.4% decline in decline in smoking could be interpreted as −0.0201 0.1621 prevalence of smoking.
R. Abouk, S. Adams / Journal of Health Economics 54 (2017) 17–24
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Table 3 Effect of e-cigarette sales ban on smoking of underage high school seniors, linear probability model, 2007–2014 Monitoring the Future Survey. Variables
(1) (2) Smoked past 30 days (mean = 0.162)
(3)
(4) No. cig | smoked (mean = 4.798)
E-cig ban
−0.0201** (0.0097)
−0.0217** (0.0094)
Age-in-year dummies State and month-year dummies
No Yes
0.0340*** (0.0049) −0.1336*** (0.0082) −0.0881*** (0.0056) −0.0468*** (0.0064) 0.0166*** (0.0011) 0.0159*** (0.0016) −0.0204** (0.0089) −0.0174 (0.0113) −0.0441*** (0.0106) −0.0462*** (0.0091) −0.0225** (0.0100) −0.0154* (0.0089) −0.0401*** (0.0109) −0.0549*** (0.0107) −0.0566*** (0.0107) −0.0076 (0.0088) Yes Yes
−0.0254** (0.0097) 0.0453 (0.0353) 0.0332 (0.0248) 0.0159 (0.0127) −0.0225* (0.0125) 0.0019 (0.0223) −0.0009* (0.0005) 0.0339*** (0.0049) −0.1323*** (0.0084) −0.0867*** (0.0058) −0.0464*** (0.0064) 0.0167*** (0.0011) 0.0159*** (0.0016) −0.0205** (0.0089) −0.0173 (0.0113) −0.0441*** (0.0106) −0.0462*** (0.0091) −0.0225** (0.0100) −0.0154* (0.0089) −0.0401*** (0.0109) −0.0549*** (0.0106) −0.0565*** (0.0107) −0.0074 (0.0088) Yes Yes
0.1917 (0.7862) 1.8826 (1.3581) −0.4093 (0.8555) 0.5882 (0.6172) −0.2616 (0.6021) 1.1614 (1.4484) −0.0796*** (0.0195) 1.0375*** (0.1982) −0.9390 (0.7344) −1.7830*** (0.2689) −0.3166 (0.3526) 0.2497*** (0.0718) 0.2876*** (0.0650) −1.1855*** (0.3689) −1.1021** (0.4564) −1.5365*** (0.4634) −1.2390** (0.5393) 1.8579* (1.0693) −1.2263*** (0.4102) −1.4120** (0.5446) −1.9018*** (0.5625) −1.9901*** (0.6574) −0.0079 (0.6441) Yes Yes
Observations R-squared
49,817 0.0203
49,817 0.0538
49,817 0.0544
8076 0.0735
Log(unemployment rate) Log(tobacco tax) Log(rTCPpc + 1) 100% smoke-free ban (state-level) 100% vaping ban (state-level) Accumulated inspections per 1000 pop 12–17 Male Black Hispanic Other Log weekly labor income Log weekly non-labor income Mother high school Mother some college Mother college graduate Mother graduate degree Mother educ. Missing Father high school Father some college Father college graduate Father graduate degree Father educ. Missing
Note: Each column is a separate regression. Estimates are weighted using MTF sampling weights and standard errors are clustered at the state level. * p < 0.1. ** p < 0.05. *** p < 0.01.
point reduction in smoking. So, we are confident that the magnitude of these estimated effects are plausible. In column (4), we assess whether there is any effect on intensity of smoking among those who smoke.7 If there is some substitutability among dual users of both types of tobacco products, however, there might be a positive effect on intensity that is not reflected in the estimates at the extensive margin. In column (4), we estimate the effect of e-cigarette sales bans on the number of cigarettes smoked, conditional on being a smoker (Eq. (2)). We find no signif-
7
We caution that some of the state-year cells for these estimations become small so these results are somewhat less informative than the estimates at the extensive margin.
icant effect on smoking intensity among smokers.8 So, the effect is observed entirely at the extensive margin, with prevalence of smoking being reduced by the sales bans. This is suggestive that e-cigarettes and traditional cigarettes are complements. Banning sales of e-cigarettes curtails smoking combustion cigarettes at the extensive margin. At the intensive margin, there is no evidence of substitution.
8 We tested for whether non-linear models resulted in different results, including Tobit to assess the impact of truncated data, interval regression as an alternative to interpolation, and Poisson regression. In no case was there any significant effect found on the numbers of cigarettes smoked.
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R. Abouk, S. Adams / Journal of Health Economics 54 (2017) 17–24
Table 4 Effect of e-cigarettes sales ban on prevalence of e-cigarette use in various grades, linear probability model, 2014 Monitoring the Future Survey.
E-cig ban Observations
Underage 12th graders (mean = 0.171)
10th graders (mean = 0.162)
8th graders (0.087)
−0.1017*** (0.0139) 3523
0.0677 (0.0426) 3770
0.0021 (0.0167) 3908
Note: Each cell reports the results from a single regression. Only 2014 data used for the analysis since e-cigarette use information is available in this year only. All individual-level covariates listed in previous tables are taken into account. State and month dummies are also included in each model. Estimates are weighted using MTF sampling weights and standard errors are clustered at the state level. * p < 0.1. ** p < 0.05. *** p < 0.01.
4.2. Confirming the mechanism of the effects on smoking and robustness checks Using the most straightforward approach, we estimate a significant decline in smoking rates. The explanation most sensible for these finding is that e-cigarettes and conventional cigarettes are complements and are dually used. We can offer additional suggestive evidence that these goods are indeed complements. In the 2014 MTF, questions on e-cigarette use were asked for the first time. About 6 in 10 current cigarette smokers were also users of e-cigarettes among the high school students in our sample. Dual use of the product is therefore quite high and complementarities are likely.9 Dual use is also high among 10th graders. However, less than half of eighth grade smokers also use e-cigarettes, so dual use is higher among the older students. We also note that the highest prevalence of use of both products among 12th graders was a reason our main analysis focusses on high school seniors. We also test whether the monthly variation in implementing retail sales bans in 2014 had an impact on the prevalence of ecigarette usage. We find significant and substantial evidence that it does for twelfth graders and report this result in Table 4. There is not a decrease in e-cigarette use among 8th and 10th graders. This is consistent with both the lower rates of e-cigarette use among younger adolescents, as well as their lower likelihood of using retail establishments to purchase the product. The smaller effect on 8th graders might actually be related to their lower amount of dual use. We caution that results are merely suggestive, as the data only allow for identification from 2014, which substantially limits identification to within state monthly changes that year.10 They do suggest that we should see stronger smoking effects for 12th graders, a point we return to in the final section. In Table 5, we engage in some additional tests to confirm the robustness of our results. We also separate the effects by gender to assess whether there are differing effects for boys and girls. In row (1) of Table 5, we add state-specific linear time trends to the basic specification (1). These results suggest even more statistically significant effects than those reported in Table 3 and more substantial effects for boys than girls. The linear time trends account for the potential that pre-existing trends might be contributing to the estimated relationship in the previous tables. Another option that accounts for differences in the treatment and control states that pass bans is to simply remove from the estimation any state that passes no restrictions on sales to minors during our sample period. This means that identification comes purely from the timing of pas-
9 Future work will have more to say on this question as the MTF collects additional years of e-cigarette data. 10 Only four states potentially provide identification as they changed their ecigarette legal status within the Spring of 2014.
sage among those states that restrict sales to young people. In row (2), we show that the results are similar when this restriction is made and the effects remain substantial for boys. We note that the larger impact for boys is expected and provides more support for the plausibility of our proposed mechanism. First, boys use e-cigarettes more than girls and smoke more. We therefore expect more of an effect for boys. Also, previous research suggests that girls are more successful than boys at being able to obtain illicit products while underage (Abouk and Adams, 2017; Grucza et al., 2013). So, we would expect less of an impact on their use of products based on the legal costs of purchasing. We caution, however, that the effects for girls remains negative throughout the series of tests and in some cases is not substantially below the effect for boys. In row (3), we test whether there was any anticipatory effect of the ban by moving the date of sales bans 6 months forward. For example, the state of New York banned e-cigarette sales to minors effective January 1st , 2013. We define a new variable in which a dummy variable is set to one in New York from July 1st , 2012 to December 31st 2012. If our results are not due to preexisting trends and there are no anticipatory effects, the estimated coefficients should be small. The effect indeed is not large. The effect of the sales ban itself remains substantively the same when the anticipatory effect is added. We consider two other estimations, the first of which divides our treatment group into early and late bans, with the 2010 and 2011 bans considered “early.” If e-cigarette bans were part of some larger anti-smoking movement that reached some states earlier than later, we might expect the bans to have differing effects depending on when they were passed. This is not the case, as there is no meaningful difference based on when bans were passed. Finally, there were some 18 year olds in our sample that could be part of the pre-ban treatment group and treatment group in states that consider 19 the age of majority for tobacco purchases. Since 18 year olds are not necessarily similar to other twelfth graders, we dropped these from one set of estimations. In row (5), we see that removing 18 year olds has no appreciable impact on our results. Finally, in row (6) we show the e-cigarette effects for boys and girls. There is no discernable difference, unlike with the effects on cigarette smoking. The reliance on just 2014 data and cross-month variation might be a reason that these results are not stronger for boys, as we would expect. We expect that future data on e-cigarette use across multiple years will shed more light on this question.
5. Discussion and conclusion In this paper, we offer the first national-level population estimates that suggest electronic cigarettes threaten to undo decades of progress in reducing smoking among adolescents. We find evidence consistent with e-cigarettes and combustion cigarettes being complements among high school seniors. Specifically, we show that curtailing access to e-cigarettes among minors through retail sales restrictions reduces traditional smoking, and we provide suggestive evidence it reduces e-cigarette use as well. Our results and conclusions differ from Friedman (2015) and Pesko et al. (2016), the two other existing studies that estimate the effects of e-cigarettes on young people. Those studies take a different approach than ours. In particular, Friedman (2015) uses state-level biennial data from the National Survey on Drug Use and Health (NSDUH) (2002–2013) to assess the effect of e-cigarette sales bans on the prevalence of smoking among adolescents, and Pesko et al. (2016) uses the Youth Risk Behavior Surveillance System (YRBSS) for ninth-twelfth graders from 2007 to 2013. Both pool younger and older age groups (12–17 year olds and 9th-12th graders, respectively). They also control for aggregate smoking
R. Abouk, S. Adams / Journal of Health Economics 54 (2017) 17–24
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Table 5 Robustness Checks for underage 12th graders on Effects of E-cigarette Bans on Smoking Incidence, 2007–2014 Monitoring the Future Survey.
(1) Including state-specific trend (2) Treated states only (3) Adding 6 month anticipatory effect 6 month period before ban Ban (4) Early vs. late ban Ban 2010–2011 Ban 2012–2014 (5) drop underage 18 year-old (6) Effect of e-cigarette ban on e-cigarette use (2014 only)
Overall
Male
Female
−0.0383*** (0.0118) −0.0439** (0.0164)
−0.0721*** (0.0187) −0.0595** (0.0210)
−0.0142 (0.0146) −0.0310 (0.0188)
−0.0059 (0.0081) −0.0279** (0.0115)
0.0015 (0.0157) −0.0340** (0.0152)
−0.0122 (0.0145) −0.0245 (0.0149)
−0.0393 (0.0169)** −0.0378 (0.0123)*** −0.0253** (0.0100)
−0.0807 (0.0195)*** −0.0676 (0.0212)*** −0.0343** (0.0134)
−0.0057 (0.0212) −0.0181 (0.0153) −0.0196 (0.0129)
−0.1017*** (0.0139)
−0.0927*** (0.0263)
−0.0986*** (0.0184)
Note: Each cell reports the effect of an e-cig sales ban using OLS estimation. All individual and state explanatory variables from Table 2 along with state and month-year dummies are included. Results in row (6) do not include state-level policy variables as the analysis is done using 2014 data only. In row (1), a linear month-year, state-specific trend is considered. The sample includes underage 12th graders. MTF sample weights are used in all regression and numbers in parenthesis are standard errors clustered at state level. * p < 0.1. ** p < 0.05. *** p < 0.01.
trends among young adults. The more granular data we use exploits monthly variation that can more exactly identify when a sales ban takes effect and its effect on potential smokers. We find significant negative effects using individual-level data for 12th graders. We additionally tested for effects on 10th graders and found smaller but still significant reductions in smoking. We found virtually zero effect on eight graders. We contend this likely reflects less usage among younger children. Given our results differ dramatically from Friedman (2015) and Pesko et al. (2016), future work will determine what explains the difference once more data become available. Since the individuallevel MTF used by us and the aggregated NSDUH and YRBSS data used by Friedman and Pesko et al. are not comparable, replication exercises are not yet possible. Pooling younger and older adolescents may lead to smaller effects, as we find that eighth graders are not affected by smoking bans in the MTF. Yet, we do not observe 9th or 11th graders in our sample. Aggregating smoking rates in the MTF by state and time for 12th graders and estimating the effect of bans results in weaker but still negative estimates. Future work should explore whether aggregate or individual-level are driving the different estimates or whether pooling younger and older respondents is leading to the different results. The MTF data have several limitations as well that future work might be able to overcome. First, we cannot capture the dynamic effects of the e-cigarette bans because we do not follow the same young people over time. In particular, we do not know if we are estimating a reduction in smoking because new users are no longer choosing to smoke or past users stop using all tobacco products following an e-cigarette ban. Future work will hopefully be able to follow the same individuals over time to capture dynamic changes in their conventional cigarette use in response to bans. This could perhaps help determine if e-cigarettes are a gateway to smoking regular cigarettes or the products are predominantly dually-used. Second, the MTF only asks questions of e-cigarette smokers beginning in 2014. Future work should address separately the effect of bans on e-cigarettes and combustible cigarettes once a longer panel of individual-level data on e-cigarette and traditional cigarette use becomes available. Currently, without explicit measures of e-cigarette and combustible cigarette use prior to 2014,
the interpretation of a reduction in smoking cannot rule out that the e-cigarette sales ban’s effect on e-cigarette use is contributing to the reduction in reported smoking. From a policy perspective, we think our results as a whole do offer support to the idea that sales bans to minors are a viable policy strategy to limit the use of combustion cigarettes among young people. We also show that the bans reduce the use of e-cigarettes themselves, thus indicating that sales bans on e-cigarettes are likely an effective tool for reducing exposure to tobacco in multiple forms. To the extent that e-cigarettes create negative externalities, the welfare gains from these sales bans are likely large as well. The limited evidence to date on the externalities associated with ecigarettes at the current time, however, limit our ability to make strong statements on this point.
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