1 Introduction

The FDA is currently assessing whether specific e-cigarette products are sufficiently appropriate for public health to be legally sold in the United States. To date, 23 unflavored e-cigarette products from three companies have been approved, thousands of e-cigarette products remain under review, and more than one million e-cigarettes have been denied (which recently includes Juul e-cigarettes).Footnote 1 Approval can be rescinded at any time if insufficient evidence exists that these products are benefiting public health. E-cigarettes that are under review, or that are denied marketing orders but these orders are being judicially appealed, can often be sold through enforcement discretion.

One key question in determining whether e-cigarettes are appropriate for the protection of public health is the impact that e-cigarette use has on combustible tobacco use. If e-cigarettes can be shown to causally reduce use of combustible tobacco, which is more dangerous (National Academies of Sciences, Engineering, and Medicine, 2018), such a finding would demonstrate an important public health benefit of e-cigarettes. Trends in cigarette use and e-cigarette use over time support the notion that e-cigarettes may be reducing youth cigarette use in aggregate. In 2009, public health leaders set a goal of reducing youth cigarette use from 19.5% in 2009 to 16.0% by 2019 (Office of Disease Prevention & Health Promotion, 2021). Youth cigarette use reached 6% in 2019, so this objective was exceeded by 386%, potentially due to e-cigarette availability during the decade. This trend has continued its acceleration, and by 2021, high school student cigarette use reached 1.9% (Gentzke et al., 2022). Cigar use has also declined sizably, to 2.1% in 2021 (Gentzke et al., 2022). While these trends are suggestive of a beneficial effect of e-cigarettes on teen combustible tobacco use, these trends alone are insufficient for establishing e-cigarettes as the causal factor.

Natural experiments, such as from policy changes, can be used to provide causal evidence towards the question of the effect of e-cigarettes on teen cigarette use (Pesko, 2022a). In this paper, the gradual roll-out across states of an e-cigarette minimum legal sale age (MLSA) is used as a form of natural experiment. MLSAs prohibit the sale of e-cigarettes to individuals under specific ages; before MLSAs, it was legal to sell e-cigarettes to minors. Five states implemented e-cigarette MLSAs by the end of 2010, seven by the end of 2011, 12 by the end of 2012, 24 by the end of 2013, 39 by the end of 2014, and 47 by the end of 2015, before federal action applied a national MLSA in 2016 (Centers for Disease Control & Prevention, 2020; Pesko & Currie, 2019). Online Appendix Table 1 shows the dates of MLSA implementation.

Three studies explore the effect of e-cigarette MLSAs on e-cigarette use in the United States, but each has substantial methodologic limitations: two studies use a single cross-section of data (due to limited data availability at the time of writing) (Abouk & Adams, 2017; Dave et al., 2019b), and the third uses multiple waves of data (through 2014 only) but does not control for state fixed effects to address several likely sources of confounding (Dutra et al., 2018). Additionally, this third study includes cigarette use as a control variable despite evidence that it is endogenously impacted by e-cigarette MLSAs (Abouk & Adams, 2017; Dave et al., 2019b; Friedman, 2015; Pesko & Currie, 2019; Pesko et al., 2016a), and it assumes that no youth used e-cigarettes in 2009 despite e-cigarettes being available in the United States since 2006 (Consumer Advocates for Smoke-Free Alternatives Association, 2022). One study uses Canadian data through 2017 to estimate the effect of staggered adoption of e-cigarette MLSAs using a two-way fixed effect (TWFE) model. This study finds adoption of e-cigarette MLSAs reduces youth e-cigarette use by 4.3 percentage points (ppt), but does not examine effects on combustible cigarette use (Nguyen, 2020). Finally, a study produced concurrently to this published one implements a regression discontinuity design with data on 17- and 18-year-olds from 2014–2017 in the United States, finding that MLSA laws decreased underage e-cigarette use by 15–20% (DeSimone et al., 2022).

This current study estimates the effect of e-cigarette MLSAs in the United States on both e-cigarette use and combustible tobacco useFootnote 2 using multiple waves of National Youth Tobacco Survey (NYTS) data through 2017. By extending the analysis through 2017, this study covers the full time period leading up to a national e-cigarette MLSA in August 2016 (Sharpless, 2019). In addition to estimating a TWFE model, the current study is the first e-cigarette MLSA study to use a method to account for the presence of dynamic heterogeneity in treatment effects (Callaway & Sant’Anna, 2021; Goodman-Bacon, 2021). Additionally, the study improves on the previous study using NYTS data from 2009 to 2014 (Dutra et al., 2018) by not including endogenous control variables nor making assumptions about e-cigarette use in a given year.

2 Background

E-cigarettes are a relatively new tobacco product that was first imported into the United States in August 2006 (Consumer Advocates for Smoke-Free Alternatives Association, 2022). E-cigarettes began to be sold in stores tracked by the Nielsen retail scanner data in 2010 (U.S. Department of Health & Human Services, 2016). National e-cigarette sales revenue is estimated to have increased from $500 million in 2012 to $6.6 billion in 2018 (Cowen & Company Equity Research, 2019).Footnote 3 In late 2017, Juul e-cigarettes became the most commonly used e-cigarette. In 2018, Juul reached 75% e-cigarette market share (Kaplan, 2021).Footnote 4 According to the NYTS, youth e-cigarette use in the past 30 days (current use) rose from 1.5% in 2011 to 27.5% in 2019, before falling precipitously in 2020 (19.7%) and 2021 (11.3%).

Lillard (2020) provides a theoretical framework for hypothesizing how the emergence of a new tobacco product, e-cigarettes, affects consumer tobacco purchasing decisions. His model posits that the demand for tobacco products is a derived demand based on the demand for nicotine. The choice of products is determined by the shadow price of nicotine, which is driven by the cost of the product, the efficiency of nicotine delivery, and the health and social effects of different products. Depending on these factors, different categories of nicotine products could theoretically be complements or substitutes.

In terms of prices, e-cigarettes are generally cheaper than cigarettes. One study using Nielsen Retail Scanner data from 2013 to 2019 found a national cigarette pack price (including excise taxes) of $6.71 per pack versus $4.82 per fluid milliliter (ml) (or $3.37 per the equivalent amount in a Juul pod that is roughly equivalent to one pack of cigarettes) (Cotti et al., 2022; Prochaska et al., 2022). In 2020, the average American resided in a location with $3.08 in cigarette taxes and $0.34 in e-cigarette taxes (Cotti et al., 2021). Therefore, consumers have a financial incentive to use e-cigarettes instead of cigarettes, in part because of lower average taxes.

In terms of efficacy of nicotine delivery referred to in Lillard (2020), one recent study finds that a Juul-experienced user can receive a nicotine boost from a 40 mg / ml pod (which is commonly used in the United States) similar to a cigarette user (Prochaska et al., 2022). However, fourteen countries prohibit e-cigarettes from being sold with a nicotine concentration exceeding 20 mg / ml (Kennedy et al., 2017), so e-cigarettes sold in these countries may have considerably lower nicotine delivery efficacy than cigarettes.

In terms of health, the National Academies of Sciences, Engineering, and Medicine in the United States state that e-cigarettes are not without risk, but compared to combustible tobacco cigarettes, they contain fewer toxicants and are likely to be far less harmful than combustible tobacco cigarettes (National Academies of Sciences, Engineering, and Medicine, 2018). One survey of 137 experts’ perceptions of e-cigarette harms relative to cigarettes, conducted in August 2020, found an average response of 37 percent (Allcott & Rafkin, 2022). Despite e-cigarettes being less harmful products, the public significantly over-estimates the risks of e-cigarettes, and this trend has grown over time. According to the Health Information National Trends Survey, 38.2% of individuals correctly believed e-cigarettes to be less harmful than cigarettes in 2012, and this has declined to only 11.2% in 2020.Footnote 5 Consumers’ desired uses of e-cigarettes are found to be more strongly related to health risk perceptions than perceived nicotine levels (Viscusi, 2016) or prices (Marti et al., 2019).

While not specifically discussed in Lillard (2020)’s demand for nicotine model, flavors are also important drivers of consumer demand for e-cigarettes (Buckell et al., 2019; Pesko et al., 2016b). While federal law prohibits cigarettes from being flavored with anything except menthol since 2009 (Courtemanche et al., 2017), e-cigarettes are regularly sold flavored. One study using Nielsen retail scanner data from 2013 to 2019 finds that 38.7% of e-cigarette liquid volume is sold unflavored, 21.9% is mentholated, and 39.4% is non-mentholated flavored (Cotti et al., 2022). The FDA attempted to ban non-mentholated flavored e-cigarette cartridges in 2020; additionally, as of March 2022, six states had implemented some version of an e-cigarette flavor ban (Truth Initiative, 2022).

3 Methods

The NYTS is a nationally-representative survey on middle and high school youth’s tobacco use. Since 2000, the NYTS is collected in the spring of each year.Footnote 6 The NYTS was the first national survey to collect information on e-cigarette use. Between the years 2011 to 2017, the NYTS was collected annually, and 125,820 respondents under the age of 18 years of age provide information on e-cigarette use.Footnote 7 Additionally, NYTS data was also collected in non-sequential years 2000, 2002, 2004, 2006, and 2009, thus providing 125,409 additional respondents under the age of 18 years of age for cigarette and cigar outcomes. When combined with the more recent waves of NYTS data, there are 251,229 respondents in total under the age of 18 for cigarette and cigar outcomes.Footnote 8 Individuals over the age of 18 or with missing age are excluded because an e-cigarette MLSA was never lower than this age nationally during the time period studied.Footnote 9 The NYTS data is imbalanced, and per year is collected by between 30 to 42 states between 2000 to 2017.

The Centers for Disease Control and Prevention (CDC) originally released the National Youth Tobacco Survey with state and county information through 2015 and state information through 2017; therefore, this study is carried out at the state-level since that is the level at which geocoded data is consistently available.Footnote 10 Similar to this study, several other published studies have used geocoded NYTS data to perform state-level policy evaluation research (Dutra et al., 2018; Feng & Pesko, 2019; Pesko & Robarts, 2017).

Our primary analysis uses a new estimator proposed by Callaway and Sant’Anna (2021) (henceforth referred to as C&S) to expunge potential biases arising in the standard TWFE estimator with staggered treatment adoption in the presence of dynamic heterogeneity in treatment effects (Callaway & Sant’Anna, 2021; Goodman-Bacon, 2021). For example, such bias could be introduced if (1) earlier-adopting (e-cigarette MLSA) states are poor controls for later-adopting states due to dynamic treatment effects across adoption timing, or (2) heterogeneity in adoption timing gives greater (less) weight to jurisdictions that implement e-cigarette MLSAs around (away from) the mid-point panel. This issue of heterogenous treatment effect dynamics may be particularly problematic in the context of studying e-cigarette MLSAs since all states adopt these policies between 2010 and 2016. Consequently, there are many instances of earlier-treated units serving as a counterfactual for later-treated units, thus elevating this concern.Footnote 11 The Stata package -csdid- is used to estimate C&S models.

Our baseline model is as follows:

$${Y}_{i,s,t}={\beta }_{0}+{\beta }_{1}{MLSA}_{s,t}+{X}_{i,s,t}{\beta }_{X}+{\sigma }_{s}+{\tau }_{t}+{\varepsilon }_{i,s,t}.$$
(1)

MLSAs,t is an indicator for whether an e-cigarette MLSA is in place at the start of the survey year. Outcomes are six available measures of tobacco use: e-cigarette use during lifetime, current e-cigarette use (i.e., use in the past 30 days), current cigarette use, daily cigarette use, current cigar use (i.e., cigars, cigarillos, or little cigars), and daily cigar use. NYTS does not collect information on daily e-cigarette use during the time period studied.

Xi,s,t are available individual-level demographics of sex (male, female, missing), age (indicators for each age), and race/ethnicity (White non-Hispanic, Black non-Hispanic, other/multiple race non-Hispanic, Hispanic, missing). Year and state fixed effects are controlled for. Standard errors are clustered at the level of state.

I estimate alternative versions of this primary model. I remove individual controls for the C&S estimator to explore sensitivity of this estimator to the inclusion of any controls. I also estimate the effect of state-level time varying controls in TWFE models.Footnote 12 Additionally, I modify the baseline TWFE estimate by adding a vector of state-level policy and environment characteristics: cigarette taxes, e-cigarette taxes, cigar taxes, smoking and vaping restrictions, Tobacco-21 laws (state + local population-weighted), beer taxes, medical and recreational marijuana laws, minimum wage, poverty rate, and unemployment rate (all as of the start of the survey year and averaged over the first two quarters over which NYTS data is collected). These time-varying control variables could otherwise be correlated with both e-cigarette MLSA adoption and tobacco product outcomes, and are directly controlled for in order to remove these potential sources of confounding. Please see data appendix for further details on the time-varying variables.

4 Results

Table 1 shows descriptive statistics for sample respondents over the 2011 to 2017 time period that is used for e-cigarette analyses, and the 2000 to 2017 period that is used for cigarette and cigar analyses. Between 2011 to 2017, 14.2% of youth report ever using e-cigarettes, and 5.5% report currently using e-cigarettes. Over the same time period, 6.6% report current cigarette use, 1.3% report daily cigarette use, 6.0% report current cigar use, and 0.6% report daily cigar use. When including the earlier waves of data (2000 to 2009), combustible tobacco use rates are higher and tobacco control policies are weaker.

Table 1 Descriptive statistics for estimation sample, NYTS
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Figure 1 compares the effect of e-cigarette MLSAs on all six outcomes using C&S and TWFE estimators. Table 2 presents these same results in tabular form. Using C&S, e-cigarette MLSAs are associated with decreases in ever e-cigarette use (2.2 ppt, 23.7% of pre-treatment sample mean, p < 0.05) and imprecisely estimated reductions in current e-cigarette use. E-cigarette MLSAs are also associated with increases in cigarette use, which is precisely estimated for daily cigarette use (0.5 ppt, 32.9%, p < 0.05). Since cigarette use rates are declining over this time period, these “increases” in cigarette use are likely accounted for by reduced smoking cessation. E-cigarette MLSAs are also associated with increases in current cigar and daily cigar use, which is estimated precisely for daily cigar use (0.3 ppt, 38.5%, p < 0.05).Footnote 13

Fig. 1
figure 1

Main results. Notes: These results are also presented in Table 1. All coefficient estimates are derived from separate regressions. All regressions control for state and survey year fixed effects. Unless otherwise stated, all regressions control for individual characteristics (shown in Table 1). The final specification adds state-level time-varying controls (also shown in Table 1)

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Table 2 Main results
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As shown in Fig. 1 and Table 2, the previously reported C&S results from 2011 to 2017 do not vary if dropping individual-level demographics. For combustible measures, the magnitudes of the coefficients do not vary if adding the earlier waves back to 2000, though precision is lower. Lower precision may be due in part to adding several years of data in which e-cigarettes were not widely available. The TWFE point estimate though (continuing to use earlier data if available) is larger than the C&S estimate for current cigarette use, and now suggests a statistically-significant increase in cigarette use of 0.8 ppt (6.9%; p < 0.10)., which is very close to e-cigarette MLSA point estimates ranging from 0.8 to 1.0 ppt in other research on general youth populations (Dave et al., 2019b; Friedman, 2015; Pesko & Currie, 2019). For cigar use, the TWFE estimate is smaller than the C&S estimate and is now statistically-insignificantly negative. Combustible tobacco use TWFE results are unaffected by adding state controls.

Figure 2 presents event study coefficients in waves before and after an MLSA is implemented in that particular state. Event study models are estimated with C&S to expunge bias due to heterogenous dynamic effects.Footnote 14 For e-cigarette use outcomes, there is a shorter pre-period due to e-cigarette data only becoming available in 2011. There is some evidence of noise in the data, demonstrated by many of the outcomes having at least one statistically significant pre-period (p < 0.10). There are three sources of imbalance that could be contributing to this noise: 1) “traditional” event study imbalance in that some states do not contribute to each event period’s time bin depending on when they adopted their MLSA (this particularly affects states adopting early or late and is partially resolved in the figure by suppressing the endpoints), 2) imbalance from many states not being surveyed in any given year, and 3) imbalance from the NYTS not being collected annually prior to 2011. Reassuringly, statistically significant pre-period coefficients appear to represent random fluctuations (consistent with data imbalance) rather than a monotonically increasing or decreasing pre-period trend that suggests omitted variable bias. Overall, these event study figures provide suggestive evidence that the parallel trends assumption is satisfied. In the post-period, coefficients generally align in the direction of the previously reported C&S estimates, and for some outcomes statistically significant post-period coefficients are found.

Fig. 2
figure 2

Callaway & Sant’anna event study estimates. Notes: All figures are derived from separate regressions using the main C&S specification in Fig. 1 (including individual controls). Endpoints are included in the estimation but suppressed from these figures due to imbalance in which units contribute to these time-bins

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Figure 3 shows heterogeneity in C&S estimates (using data from 2011 to 2017) by sex and age. Individuals < 16 years of age are more responsive to e-cigarette MLSAs than older teens, which is consistent with younger teens being less likely to have ever tried e-cigarettes. Therefore, the pool of people that can be affected by e-cigarette MLSAs in terms of ever e-cigarette use is larger for younger teens than for older teens. For cigarette and cigar outcomes (current and daily), the effects of e-cigarette MLSAs appear significantly larger for males and older teens, which is consistent with these groups being more likely to use e-cigarettes.

Fig. 3
figure 3

Callaway & Sant’anna estimates by sex and age. Notes: All coefficient estimates are derived from separate regressions. All regressions control for state and survey year fixed effects and demographics

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Figures 4 through 6 provide a set of robustness checks. Figure 4 shows that results from Fig. 1 are largely unchanged when survey weights provided by the NYTS are applied, with one possible exception being larger C&S estimates for current cigarette use that are closer to TWFE estimates. Figure 5 drops five states with county-level MLSAs (Pesko & Currie, 2019) to reduce concern of bias from uncontrolled local MLSAs. These results are generally consistent with Fig. 1 main results, though there is some attenuation in the C&S effect on daily cigarette use. Figure 6 meanwhile shows that the results in Fig. 1 are relatively unchanged when dropping five state-year pairs that had an MLSA occur within a given NYTS survey year (January to May) and four state-year pairs with statewide/districtwide Tobacco-21 laws in place, thus reducing concerns about confounding from these sources.Footnote 15

Fig. 4
figure 4

Callaway & Sant’anna and two-way fixed effect estimates, weighted. Notes: All coefficient estimates are derived from separate regressions and use survey weights provided by NYTS. All regressions control for state and survey year fixed effects and demographics

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Fig. 5
figure 5

Callaway & Sant’anna and two-way fixed effect estimates, dropping states with local MLSA. Notes: The five states with local MLSAs are New Mexico, New York, Oregon, Pennsylvania, and Washington. See Online Appendix Table 1. All regressions control for state and survey year fixed effects and demographics

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Fig. 6
figure 6

Callaway & Sant’anna and two-way fixed effect estimates, dropping state-year pairs with partial MLSA adoption and Tobacco 21 laws. Notes: Each survey period is between January through May of a given year. Therefore, the four NYTS data state-year pairs with Tobacco 21 laws are CA-2017, DC-2017, HI-2016, and HI-2017. The five state-year pairs with MLSAs implemented between the survey period of a given year are CO-2011, KY-2014, NE-2014, WI-2012, and WY-2013. All regressions control for state and survey year fixed effects and demographics

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5 Discussion

This study contributes the strongest evidence to date on the effect of e-cigarette MLSAs in the United States on e-cigarette use by leveraging multiple waves of national survey data and using both C&S and TWFE estimators. This study shows that e-cigarette MLSAs work as intended in the United States by reducing youth e-cigarette initiation; however, at the expense of higher daily combustible tobacco use. Overall, results from this study suggest that e-cigarettes have public health benefit in reducing high-frequency combustible tobacco use among youth, which is an important input for the FDA to consider as they decide whether to allow e-cigarettes to be legally sold or not.

The FDA may also wish to carefully review other natural experiment-style studies that similarly explore the effect of policies designed to reduce e-cigarette availability or appeal on combustible tobacco product use outcomes. For example, there are 15 fixed effect studies using variation in e-cigarette tax rates, MLSAs, or advertising, with 13 studies finding that e-cigarettes and cigarettes are substitutes (Abouk et al., 2022a; Abouk et al., 2021; Cotti et al., 2022; Dave et al., 2019a; Dave et al., 2019b; Friedman, 2015; Friedman & Pesko, 2022; Pesko et al., 2020; Pesko & Currie, 2019; Pesko et al., 2016a; Pesko & Warman, 2022; Saffer et al., 2020; Tuchman, 2019), one study finding they are largely unrelated goods (though some evidence of substitution is present) (Allcott & Rafkin, 2022), and one study finding they are complements (Abouk & Adams, 2017). The current paper provides another data point in favor of substitution. The evidence from natural experiments therefore leans heavily towards e-cigarette reducing cigarette use at the population level.

In the United States, the MLSA for all tobacco (including e-cigarettes) is now 21 years of age. Results from this study suggest that raising MLSAs for combustible tobacco, but leaving them lower for e-cigarettes, could have public health benefit over raising both ages to 21 (Pesko, 2022b). Additionally, 56 countries ban e-cigarette sales to minors and 28 countries ban e-cigarette sales altogether, so a sizable number of countries do neither (Institute for Global Tobacco Control, 2020). To the extent that the United States situation is generalizable to these countries, the current study would therefore provide evidence on likely effects of implementing e-cigarette MLSAs in places without them: lower youth e-cigarette initiation but at the expense of higher regular combustible tobacco use rates.