Full length articleBehavioral economic substitution between conventional cigarettes and e-cigarettes differs as a function of the frequency of e-cigarette use
Introduction
In the present tobacco marketplace, the product landscape is shifting such that prices for conventional cigarettes are increasing at the same time that alternative nicotine delivery products, like electronic cigarettes (e-cigarettes), are becoming more prevalent (King et al., 2013, McMillen et al., 2015). In fact, based on the recent Population Assessment of Tobacco and Health (PATH) data, the prevalence of e-cigarette use was 5.5% of adults, which represents approximately 13.7 million people in the U.S. (Kasza et al., 2016). Moreover, frequent e-cigarette use (using 20 out of the past 30 days) was reportedly 1.5%, which would represent approximately 3.7 million Americans. Interestingly, adults who are most likely to use e-cigarettes are those who are current cigarette smokers, compared to former and never smokers (Caraballo et al., 2016, Delnevo et al., 2016). Consequently, most e-cigarette users are, to some degree, dual users who are faced with many choices in the marketplace.
When making purchasing choices in the tobacco marketplace, the extent to which (i.e., frequency) an individual uses conventional or e- cigarettes or both, can impact their demand for different products. For example, greater conventional cigarette valuation was demonstrated by those who smoke cigarettes more frequently (Mackillop et al., 2008, Murphy et al., 2011). A gap in our knowledge, however, is how different frequency of use patterns for e-cigarettes will influence consumer behavior in the marketplace under various conditions. Therefore, forecast tools to model interactions between individuals’ product use patterns, types of products (e.g., conventional cigarettes and e-cigarettes), and their prices, are needed to clarify their effects on consumer behavior on a greater scale.
Behavioral economics, which involves the integration of psychology and consumer demand, can measure hypothetical purchase behavior for a commodity under different market conditions (Hursh, 1984). The hypothetical purchase task, for example, can be implemented to examine the number of commodities (e.g., cigarettes or e-cigarettes) an individual may hypothetically purchase at increasing prices (Jacobs and Bickel, 1999). Consistent with consumer-demand theory, commodity consumption has been demonstrated to decline with greater prices, generating what is known as a demand curve (Hursh, 1984, Mackillop et al., 2008). A behavioral economic demand curve can yield two important parameters that describe an individuals’ valuation for cigarettes, 1) the intensity of the demand (i.e., Q0; total purchases at free price); and 2) the elasticity of demand (i.e., alpha; sensitivity to price) (Hursh, 1984, Hursh and Silberberg, 2008). Consequently, this procedure can be used to experimentally demonstrate decreases in consumption of conventional or e-cigarettes as a function of increasing price (Grace et al., 2015a, Huang et al., 2014, MacKillop et al., 2012).
Behavioral economic procedures can also assess the interaction of multiple commodities available concurrently. That is, as the price of commodity A increases, a concurrently available and constantly priced commodity B can act as a substitute (i.e., consumption increases), complement (i.e., consumption decreases), or not impact consumption of the other product (i.e., independence) (Bickel et al., 1995). The interaction that emerges between the two commodities is a product of the valuation of the alternative as defined by its magnitude and the relative prices of both commodities (Bickel et al., 1995). Therefore, in addition to empirical measurement of conventional and e-cigarettes alone, identifying how alternative nicotine products interact when available together can help to predict the way in which consumers, may substitute, complement, or alter purchasing independently as a function of the valuation and relative prices of the products in a variety of marketplace conditions.
Several reports have previously demonstrated that conventional and e-cigarettes interact. For example, a study conducted in New Zealand (N = 210) reported that daily smokers substituted concurrently available e-cigarettes for conventional cigarettes when the price of cigarettes increased (Grace et al., 2015a). Moreover, purchasing patterns can differ based on the availability of alternative products. Previous studies have shown that consumption of conventional cigarettes is reduced when alternative commodities (i.e., e-cigarettes, de-nicotinized cigarettes, gum, and/or money) are available (Grace et al., 2015b, Johnson et al., 2004, Johnson and Bickel, 2003, Quisenberry et al., 2016). This second finding emphasizes an additionally relevant variable for predicting consumer behavior − marketplace availability of alternative products.
Examination of these interactions may help shed light on the impact of conventional cigarette taxation or bans, and or e-cigarette subsidies/vouchers. However, such price modifications will not impact the market homogeneously. That is, perhaps increasing cigarette price will cause some smokers to quit smoking entirely and others to increase consumption of alternatives more readily. Importantly, hypothetical purchases are correlated with purchases of laboratory-based real and potentially real cigarettes (Wilson et al., 2016). Therefore, hypothetical purchases provide reasonable indications for how individuals may consume these products in the real world.
Therefore, the present study examines how an individual will purchase both conventional cigarettes and e-cigarettes alone and in combination as a function of price and the frequency with which they currently use e-cigarettes. Based upon our earlier studies with conventional cigarettes, we hypothesized that the frequency of e-cigarette use would 1) reduce demand (lower intensity and raise elasticity) for conventional cigarettes, 2) increase demand for e-cigarettes, and 3) interact with conventional cigarettes whereby demand would decrease for cigarettes giving rise to increased substitution for e-cigarettes.
Section snippets
Participants
Participants (N = 385) who were U.S. registrants of Amazon Mechanical Turk, a crowd-sourcing service, accessed the Human Intelligence Test (HIT) titled “Hypothetical purchase research on cigarettes and e-cigarettes”. Participant eligibility requirements included being at least 18 years of age, smoke 10 or more cigarettes per day, and have at least a 90% approval rating from previous HITs. The participants who were eligible to accept the HIT implied consent when the participant indicated they
Results
Demographic variables of the participants in each of the frequency groups are presented in Table 2. Frequency groups did not differ significantly in any demographic variables, number of reported cigarettes smoked per day, Fagerstrom score, or perceived health risk of cigarettes. All participants were also asked a “yes” or “no” question about whether they were trying to quit smoking cigarettes or had immediate plans to do so. The percent of participants quitting cigarettes in each group
Discussion
Frequency of use of e-cigarettes differentially affected how individuals consumed both conventional cigarettes and e-cigarettes in response to different hypothetical marketplace conditions, regardless of similar conventional cigarette use patterns in the real world. The following key findings from the present study revealed that in general: 1) the demand for conventional cigarettes alone decreased with greater frequency of e-cigarette use, 2) the demand for e-cigarettes alone increased with
Conflict of interest
KMC has received grant funding from the Pfizer, Inc, to study the impact of a hospital-based tobacco cessation intervention, and has received funding as an expert witness in litigation filed against the tobacco industry. SES and WKB report no competing interests.
Role of funding source
This research was support by NIH grants U19CA157345, P01CA138389, P01CA200512, Virginia Tech Carilion Research Institute (VTCRI), and a small grant from the International Tobacco Control (ITC). The funding agencies had no additional role in study design, data collection, analysis and interpretation of the data, nor in the preparation and submission of the report, including the decision to submit.
Contributors
SES performed the study, data analyses, and wrote the first draft of the manuscript. KMC contributed conceptual feedback on the study and manuscript drafts. WKB conceived the study design, facilitated data analysis strategies, and edited manuscript drafts. All authors contributed to and approved the final manuscript.
References (38)
- et al.
Electronic nicotine delivery system use among U.S. adults, 2014
Am. J. Prev. Med.
(2016) - et al.
Chapter 29 the economics of smoking
Handb. Heal. Econ.
(2000) - et al.
Substitutes for tobacco smoking: a behavioral economic analysis of nicotine gum, denicotinized cigarettes, and nicotine-containing cigarettes
Drug Alcohol Depend.
(2004) - et al.
The potential for using excise taxes to reduce smoking
J. Health Econ.
(1982) - et al.
Validity of a demand curve measure of nicotine reinforcement with adolescent smokers
Drug Alcohol Depend.
(2011) - et al.
A comparison of measures of relative reinforcing efficacy and behavioral economics: cigarettes and money in smokers
Behav. Pharmacol.
(1999) - et al.
The behavioral economics of concurrent drug reinforcers: a review and reanalysis of drug self – administration research
Psychopharmacology (Berl.)
(1995) - et al.
Deconstructing relative reinforcing efficacy and situating the measures of pharmacological reinforcement with behavioral economics: a theoretical proposal
Psychopharmacology (Berl.)
(2000) - et al.
The behavioral economics of tobacco products: innovations in laboratory methods to inform regulatory science
- et al.
patterns of electronic cigarette use among adults in the United States
Nicotine Tob. Res.
(2016)