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The short- and long-run effects of smoking cessation on alcohol consumption

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Abstract

This paper examines the short- and long-term effects of quitting smoking on alcohol consumption using the Lung Health Study, a randomized smoking cessation program. The paper estimates the relationship between smoking and alcohol consumption using several self-reported and objective smoking measures, while also implementing a two-stage least squares estimation strategy that utilizes the randomized smoking cessation program assignment as an instrument for smoking. The analysis leads to three salient findings. First, self-reported and clinically verified smoking measures provide mixed evidence on the short-term impact of quitting smoking on alcohol consumption. Second, the long-term impact of smoking on alcohol consumption, measured with the historic 5 years smoking behavior, suggests that those with the highest average cigarette consumption and those with the longest smoking history see the largest increase in alcohol consumption. Specifically, abstaining from smoking or reducing the average cigarette consumption to the mean level lowers alcohol consumption by roughly 25% per week. As a result, these findings present comprehensive evidence that smoking and drinking are complements in the long-term and that the public health and finance benefits in smoking cessations treatments are undervalued.

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Notes

  1. See Surgeon General Report 2014 for more details on smoking.

  2. Excessive alcohol consumption is defined as 15 or more drinks per week or 5 or more drinks per session for men, and 8 or more drinks per week or 4 or more drinks per session for women.

  3. For a detailed discussion on the expenses please see Report “Shoveling Up II: The Impact of Substance Abuse on Federal, State and Local Budgets” from the National Center on Addiction and Substance Abuse at Columbia University.

  4. For a general overview on the literature see Pierani and Tiezzi (2009).

  5. The application of different data and identification strategies may be an explanation for the mixed results among similar demand equations. For example, Bask and Melkersson (2004) use aggregate time series sales volume data from Sweden and estimate demand equations based on the rational addiction model. Picone et al. (2004) rely on individual level data from the Health and Retirement Survey (HRS) and apply an instrumental variable approach, using prices and lagged consumption as instruments for current consumption. Whereas Decker and Schwartz (2000) concentrate on the estimation of the relationship between cigarette and alcohol prices on alcohol and cigarette consumption levels, respectively. Another reason for the diverse results could be that the price variation is not exogenous.

  6. In the budget constraint, the price of the composite good \(Y_t\) is normalized to one to represent the numeraire. \(P_{A}\) and \(P_{C}\) represent the relative price of alcoholic beverages and cigarettes, and \(I_{t}\) represents total income. Two habit stocks, \(S_t\) and \(H_t\), represent the influence of past consumption over time that affects current demand. \(\delta \) and \(\gamma \) represent the discount rates of the addictive stocks.

  7. Bask and Melkersson (2003) formally extend the rational addiction model to two addictive goods, and apply a standard technique by approximating the instantaneous utility function by a quadratic function in its arguments, which yields an alcohol demand equation of the form: \(a[t]=\beta _{10}+(1+r)\beta _{11}a[t-1]+\beta _{11}a[t+1]+\beta _{12}c[t-1]+\beta _{13}c[t]+\beta _{14}c[c+1]+\beta _{15}p_a[t]. \) where a[t] represent alcoholic beverage consumption at time t, c[t] the cigarette consumption at time t, and \(p_a[t]\) the price of alcoholic beverages at time t. Similar to the inter-temporal link of the consumption in addictive good in Becker and Murphy (1988), past and future consumption of cigarettes are here positive correlated with alcohol consumption (\(\beta _{12}>0, \beta _{14}>0\)).

  8. At the time of selection, participants should live no more than 75 miles away from the hospital and should have no intentions to move away from the area. This was done to minimize attrition. Location identifiers are not provided in the dataset allowing no analysis stratifying sample by location. The hospitals were selected by the NHLBI based on expertise in COPD, staff, and plausibility of recruitment plan. A list of hospitals participating can be found online http://www.biostat.umn.edu/lhs/centers.html.

  9. Medication use includes medicine for tuberculosis, theophylline or other xanthines, beta-blockers, insulin, any corticosteriods, antipsychotic drugs, nitroglyercine, digitalis, anticoagulants and antiarrhythmics. Alcoholics and extreme alcohol users were also excluded as part of the protocol changes in about 1/3 of the study population.

  10. The clinical trial ended in 1994.

  11. The treatment is a classical intent-to treat and cessation rates at the first follow-up visit do not present different levels of cessation by treatment group.

  12. See the Alcohol Alert on alcohol and Tobacco from the NIAAA in 2007 for a good overview.

  13. Cotinine level was collected with a saliva sample at the time of the interview and is measured in terms of ng/ml.

  14. To get a better understanding of the magnitude of mismeasurement in the self-reported monthly smoking variable I test whether there are significant differences between the annual self-reported and annual verified smoking status variables. I find that the first year self-reported and verified smoking status variables are significantly different at the 1% level. Specifically, that 32% of participants responded that they quit smoking by the first annual follow-up visit, while the validated smoking variable suggests that only 27% quit smoking. With this discrepancy in mind, there seems to be the possibility of over-reporting on quit status for the self-reported smoking variable. However, the difference in self-reported vs. validated cessation levels is significantly lower than in other areas of misreporting—see for example the work of Meyer et al. (2009).

  15. The 1990 NHIS suggest an average level 12.50 years of completed education.

  16. The average alcoholic beverage consumption level at baseline was 6.3 drinks per week among drinkers.

  17. Unobservable confounders such as cigarette tax and price changes should not bias the 2SLS estimates because the random treatment assignment within a state around a hospital should expose all treatment and control participants to the same tax/price changes. Thus, these changes should also be uncorrelated with the random treatment assignment. In any case, cigarette and alcohol taxes only changed marginally during the sample period across all locations. Alcohol state taxes did not change in the 5 years for all states, and cigarette state taxes only increased in Minnesota (from 38 cents per pack to 43 cents in 1992), Pennsylvania (from 18 to 31 cents in 1993), Utah (from 23 to 26.5 cents per pack in 1993), and Maryland (from 16 cents to 36 cents in 1993).

  18. I test and reject that the treatment has a direct effect on drinking in the results section by running different types of reduced form regressions between drinks per week and treatment assignment.

  19. I also estimate Eqs. (1) and (2) where the dependent variable is the difference in the drinking level from baseline to the first post treatment year. These results are present very similar results.

  20. Additionally, I also generate a quasi objectively-verified cigarette consumption measure, and regressions yield similar average effects of quitting smoking on drinking as the smoking indicator variable. The quasi objectively-verified measure compares the verified smoking status variable to the self-reported cigarette consumption per day. If the self-reported cigarette per day variable and objective smoking dummy variable do not suggest the same smoking status, i.e. dummy variable indicates smoker (equal to one), but self reported cigarette consumption measures equals zero. In all cases of a mismatch I drop those observations, leaving me with a quasi objective cigarette measure as I drop all misreported observations on the extensive margin.

  21. To date, a typical approach in the literature is to include a 1 year lagged smoking status variable in the regression. However, the rational addiction model specifically outlines the importance on the effect of the intensity of past cigarette consumption on alcohol consumption.

  22. I also estimate 2SLS models using the interaction of intensity of smoking at baseline by treatment status as an instrument. The results do no lead me to significantly different conclusions.

  23. See Wooldridge (2010), 2nd Edition.

  24. The non-sustained quitter measure, which is only available for follow-up years two through five, only includes individuals who smoke in at least one of the follow-up waves.

  25. However, among the sample of drinkers at randomization, I tested for differences in demographic characteristics by treatment groups and did not find a significant effect.

  26. I tested the hypothesis that all coefficient are equal across gender and marital status in Table 5, which was rejected at the 1% level. Moreover, the hypothesis that the coefficients of smoking are equal across gender was rejected at the 10% level.

  27. I find that some sub-sample Probit estimates suggest that smoking decreases the probability of drinking by about 4–6%. IV-Probit estimates indicate no significant effect and present significantly different marginal effects. I conclude that there is no different effect on the extensive margin of drinking among the sub-samples.

  28. The Center for Disease Control defines excessive drinking as being a heavy drinker, defined as consuming 15 or more drinks per week for men, or binge drinker, defined as consuming 5 or more drinks per session per day for men. Heavy drinking for females is defined as consuming more than 7 drinks per week and binge drinking as consuming more than 3 drinks per occasion.

  29. For example, I classify participants into the very low average smoking bin if a person smoked on average 1–5 cigarettes per day in each months of the last year. The low smoking bin includes participants with an average cigarettes consumption of 6–10 cigarettes per day in the last year. The heaviest smokers are represented in the highest bin that includes those who consume on average 36 or more cigarettes per day.

  30. Results are not different with the inclusion of depreciation rates.

  31. A first stage regression of the smoking stock (measured in month smoking) on treatment status shows an F-Statistics of 573.

  32. Estimates utilizing depreciation rates on the smoking stock leave more weight on smoking behavior in more recent time periods. Moreover, estimates utilizing depreciation rates of 20% provide similar results.

  33. The average cigarette stock (13 and 12 respectively) and month non-smoking stock (19 and 17 respectively) for 6 and 12% depreciation rates are multiplied with the coefficient to reach an average effect.

  34. As mentioned earlier, Appendix Table 9 presents individual fixed effects panel results that account for correlation between omitted factors affecting smoking and drinking. Again these results estimate the impact of the objective smoking measures on alcohol consumption across 5 years, and the implied average affects are within the 95% confidence interval bounds to the results presented in Table 4.

  35. I also conduct sensitivity analysis on the impact of the smoking stock on alcohol consumption at year one, three and four. The short-term impact is smaller than the long-term impact, and the impact at year three and four do not change the conclusions. Lastly, concerns about the findings due to analytical method are tested by conducting regression in the form of Eq. (1) in year five. These results present similar large impacts of smoking on drinking as in Table 7.

  36. I control for marital status, income, gender and age and limit the sample to participants between the ages of 35–65.

  37. Typical smoking cessation programs see long-term cessation rates between 10 and 20%. See for example Rodgers et al. (2005).

  38. The CDC and the LHS suggest that 15% of smokers consume at excessive alcohol levels, and 1% of US smokers, which is about 2 million smokers, include about 300,00 excessive drinkers.

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Authors and Affiliations

  1. Leonard Davis Institute of Health Economics, University of Pennsylvania, 308 Colonial Penn Center, 3641 Locust Walk, Philadelphia, PA, 19104-6218, USA

    Benjamin Ukert

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  1. Benjamin Ukert
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Correspondence to Benjamin Ukert.

Additional information

I thank Rusty Tchernis, Charles Courtemanche, Tom Mroz, Ian McCarthy, Guy David, John Cawley, seminar participants at Georgia State University, the Southern Economic Association and Western Economic Association International for helpful feedback, the editor, and two referees.

Appendix

Appendix

See Tables 8, 9, 10 and 11.

Table 8 Cigarette consumption per day and smoking status by month prior to the first annual follow-up visit
Full size table
Table 9 The impact of smoking on drinking in the 5 year panel fixed effects setting
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Table 10 The effect of smoking on weekly alcoholic beverage consumption among excessive drinkers
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Table 11 The effect of the smoking stock on weekly alcoholic beverage consumption among sub-samples after 5 years
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Ukert, B. The short- and long-run effects of smoking cessation on alcohol consumption. Int J Health Econ Manag. 17, 495–519 (2017). https://doi.org/10.1007/s10754-017-9220-1

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