E-cigarettes and adult smoking: Evidence from Minnesota

Abstract

E-cigarettes provide nicotine in a vapor form, which is considered less harmful than the smoke from combustible cigarettes because it does not contain the toxins that are found in tobacco smoke. E-cigarettes may be effective in helping smokers to quit or they might simply provide smokers a method of bypassing smoking restrictions. There is very little causal evidence to date on how e-cigarette use impacts smoking cessation among adults. Minnesota was the first to impose a tax on e-cigarettes. This tax provides a plausibly exogenous deterrent to e-cigarette use. We utilize data from the Current Population Survey Tobacco Use Supplements from 1992 to 2015 to assess how the Minnesota tax increase impacted smoking cessation among adult smokers. Estimates suggest that the e-cigarette tax increased adult smoking and reduced smoking cessation in Minnesota, relative to the control group, and imply a cross elasticity of current smoking participation with respect to e-cigarette prices of 0.13. Our results suggest that in the sample period about 32,400 additional adult smokers would have quit smoking in Minnesota in the absence of the tax. If this tax were imposed on a national level about 1.8 million smokers would be deterred from quitting in a ten year period. The taxation of e-cigarettes at the same rate as cigarettes could deter more than 2.75 million smokers nationally from quitting in the same period. The public health benefits of not taxing e-cigarettes, however, must be weighed against effects of this decision on efforts to reduce vaping by youth.

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Notes

  1. 1.

    All e-cigs have certain components in common, including a power source or battery that heats a liquid (usually propylene glycol) containing nicotine into an aerosol that is then inhaled by the user.

  2. 2.

    See https://www.drugabuse.gov/publications/drugfacts/electronic-cigarettes-e-cigs.

  3. 3.

    In general, less than one in four cessation attempts is successful. For the average smoker, the expected number of quit attempts before quitting smoking successfully has been estimated as ranging from 6 to 30 attempts (Chaiton et al. 2016).

  4. 4.

    While the FDA continues to make e-cigs available and accessible in the market for adults, it has taken a more aggressive approach towards regulating access for youth and educating them about the dangers of e-cigs.

  5. 5.

    See https://ag.ny.gov/pdfs/FINAL_AG_FDA_Comment_Re_Deeming_Regulations.pdf.

  6. 6.

    https://www.publichealthlawcenter.org/sites/default/files/E-Cigarette-Legal-Landscape-50-State-Review-September15-2019.pdf.

  7. 7.

    In this case, the endogeneity can reflect both reverse causality with e-cig use affecting smoking and vice versa as well as selection on unobserved factors (for instance, a propensity for addictive behaviors, risk tolerance, time preference) that may affect participation in both behaviors.

  8. 8.

    E-cig sales in 2010 and 2011 were very limited and consequently the price data for e-cigs in these years may be inaccurate.

  9. 9.

    We assume that the retail market for e-cigarettes can be characterized by the pure version of the Cournot model of oligopoly (Tirole 1988; Scherer and Ross 1990). Hence the retail price of e-cigarettes is given by P = [ε(/ε - h)]C, where ε is the market price elasticity of demand, h is the Herfindahl index, C is the sales-share weighted average of each retailer’s average cost (assumed to be independent of pods sold) of selling e-cigarettes, and ε > h. Define m as ε/ε - h; assume that ε and h are constant; and note that m > 1. Average cost is given by C = W*(1 + r) + T, where W* is wholesale price exclusive of tax, r is the wholesale tax rate and T denotes other costs incurred by the retailer per unit of sales. Hence P = m[W*(1 + r) + T]. Given these assumptions, the tax pass-through (the increase in P due to an increase in r with W* held constant) exceeds one: ∂P/∂rW* = m. Let W be the wholesale price inclusive of tax. Then

    P/W = k, k = m([1 + (mT/W)]. We use a value of k of 1.33 in the computations above. We realize that T/W will change as W increases due to an increase in r, but assume that this effect is small enough to be ignored. Since our estimate of the retail price in Minnesota in 2015 differs from the actual price by only 7 cents, our assumption is very reasonable. Put differently, the tax pass-through to the retail price is approximately 1.33. Cotti et al. (2020) provides corroborating evidence since they find a similar tax pass-through of 1.55.

  10. 10.

    Because the values are close we can conclude that price discounting is not a problem.

  11. 11.

    See https://www.publichealthlawcenter.org/sites/default/files/States-with-Laws-Taxing-ECigarettes-September152019.pdf. More recently Pennsylvania and California have enacted large e-cig taxes, which can be evaluated as additional waves of data become available. D.C. imposed a tax on e-cigs in late 2015 after the 2015 CPS-TUS data were collected. We limit our data to waves prior to 2018 to draw a sharp contrast between the first state to enact an e-cig tax and all other states and to have a long-enough post period for potential effects to develop.

  12. 12.

    More information on the CPS-TUS can be found at: https://cancercontrol.cancer.gov/brp/tcrb/tus-cps/questionnaires.html.

  13. 13.

    The algorithm generates a weight for each state, including a zero weight. These weights are then used to weight the means from each state and then the weighted means are aggregated into what is called a synthetic control group. The weights are computed such that the synthetic control group mean parallels the treatment state in the pre treatment period. Lagged values of the dependent variable were used as matching variables.

  14. 14.

    To clarify, let the standard DD model written as Ygt = α + ϕg + βga + binary period variables, where g is a binary variable equal to 1 for the treatment group and is equal to 0 for the control group, a is a binary variable for the post period and the subscript t is a period indicator. The first Donald and Lang model is the difference between the treatment and control groups: Y1t - Y0t = ϕ + βa. The second Donald and Lang model, which corrects for serial correlation, is: Y1t - Y0t - (Y1t-1 - Y0t-1) = βΔa.

  15. 15.

    SMN is the synthetic control group formed by applying the weights generated by the smoking participation matching process. We do not generate new weights specifically for matching prices, since we want to analyze the tax pass-through and effects of the tax on prices based on the same control group for which we analyze smoking outcomes.

  16. 16.

    Note that a relatively large t-statistic is needed to achieve statistical significance due to the small sample sizes with group-period aggregated data.

  17. 17.

    The simultaneous increase in other non-cigarette tobacco prices would probably have had a small positive effect on cigarette smoking offsetting some of the effects of higher cigarette taxes.

  18. 18.

    This figure is obtained by multiplying 600,000 by the percentage increase in smoking participation divided by 100 (600,000 × 0.054 = 32,400). If MN data apply to the entire US, 0.054 X 34 million = 1.83 million.

  19. 19.

    A JUUL pod contains the nicotine equivalent of a pack of cigarettes and costs about $4.00. The combined federal cigarette tax and state average cigarette tax is $2.73 per pack. A tax of $2.73 with a pass-through of 1.33 (see note 8) would raise the price of e-cigs by $3.63, which is an increase of 62% relative to an average of the initial and the final price. Divide that figure by 100 and then multiply the result by the arc cross-price elasticity of 0.13 to get an increase in smoking participation of 0.081 or 8.1%. Multiplication of the former number by 34 million gives 2.75 million.

  20. 20.

    Currently, approximately 1.3 million smokers quit each year, which implies a quit rate of 0.038 (3.8%). If there are no starters or relapsers, there would be (0.962)10 X 34 million = 23 million remaining smokers ten years hence and 11 million quitters over that period. If the net percentage reduction in the number of smokers is less than 3.8%, we overestimate the number of quitters.

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Acknowledgements

This project was funded by grant number R01-DA039968 entitled “The Economics of Electronic Nicotine Delivery Systems: Advertising and Outcomes”, from the National Institute of Health to the National Bureau of Economic Research, Inc. We would like to thank Charles Courtemanche, Catherine Maclean, Michael Pesko, and W. Kip Viscusi for helpful comments and suggestions. This study employs data from the A.C. Nielsen Company, which was purchased from the Kilts Center of the University of the Chicago Booth School of Business. Results are calculated (or derived) based on data from The Nielsen Company (US), LLC and marketing databases provided by the Kilts Center for Marketing Data Center at The University of Chicago Booth School of Business. Information about the data and access are available at http://research.chicagobooth.edu/nielsen/. We are grateful to the A.C. Nielsen Company and the Kilts Center for providing the data and for instructions in its use. The conclusions drawn from the Nielsen data are those of the researchers and do not reflect the views of Nielsen. Nielsen is not responsible for, had no role in, and was not involved in analyzing and preparing the results reported herein. Copyright © 2017. The Nielsen Company (US), LLC. All Rights Reserved.

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Saffer, H., Dench, D., Grossman, M. et al. E-cigarettes and adult smoking: Evidence from Minnesota. J Risk Uncertain 60, 207–228 (2020). https://doi.org/10.1007/s11166-020-09326-5

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Keywords

  • E-cigarettes
  • Cigarette tax
  • Smoking cessation

JEL Classifications

  • I18