u.s. demand for tobacco products in a system framework · u.s. demand for tobacco products in a...

U.S. DEMAND FOR TOBACCO PRODUCTS IN A SYSTEMFRAMEWORK

YUQING ZHENGa,*, CHEN ZHENb, DANIEL DENCHc and JAMES M. NONNEMAKERd

aUniversity of Kentucky Department of Agricultural Economics Lexington, KY, USAbUniversity of Georgia Department of Agricultural and Applied Economics Athens, GA, USA

cCity University of New York Graduate Center, New York City, NY, USAdRTI International, Public Health Research Division, Research Triangle Park, NC, USA

ABSTRACTThis study estimated a system of demand for cigarettes, little cigars/cigarillos, large cigars, e-cigarettes, smokeless tobacco,and loose smoking tobacco using market-level scanner data for convenience stores. We found that the unconditionalown-price elasticities for the six categories are �1.188, �1.428, �1.501, �2.054, �0.532, and �1.678, respectively.Several price substitute (e.g., cigarettes and e-cigarettes) and complement (e.g., cigarettes and smokeless tobacco)relationships were identified. Magazine and television advertising increased demand for e-cigarettes, and magazineadvertising increased demand for smokeless tobacco and had spillover effects on demand for other tobacco products. Wealso reported the elasticities by U.S. census regions and market size. These results may have important policy implications,especially viewed in the context of the rise of electronic cigarettes and the potential for harm reduction if combustibletobacco users switch to non-combustible tobacco products. Copyright © 2016 John Wiley & Sons, Ltd.

Received 03 June 2015; Revised 27 November 2015; Accepted 13 June 2016

JEL Classification: D12; I18; M37

KEY WORDS: advertising; cigar; cigarette; e-cigarettes; smokeless tobacco

1. INTRODUCTION

Historically, cigarettes have dominated the tobacco market. For example, in the United States, cigaretteshave consistently accounted for more than 80% of the tobacco products marketed, causing more than480,000 deaths each year (about one in five deaths) (U.S. Department of Health and Human Services, 2014).Noncigarette tobacco products—including little cigars, cigarillos, large cigars, chewing tobacco, snuff,dissolvables, pipe tobacco, roll-your-own tobacco, and electronic cigarettes (e-cigarettes or electronic nicotinedelivery systems)—although becoming more popular, have received much less attention in the economics andpublic health literature.

Recently, noncigarette tobacco products have exhibited a steady gain in market share. Figure 1 pre-sents the value of shipments for cigarettes versus noncigarette tobacco products for 2000 through 2011,during which the value of shipments for cigarettes declined from $41.6 billion to $31.2 billion. Mean-while, the value of shipments for noncigarette tobacco products increased from $4 billion to $6 billion(market share rose from 9% to 16%), mainly driven by larger shipments in chewing and smoking tobaccoand cigars.

*Correspondence to: Department of Agricultural Economics, University of Kentucky, 317 Charles E. Barnhart Building, Lexington, KY40546-0276, USA. E-mail: [email protected]

Copyright © 2016 John Wiley & Sons, Ltd.

HEALTH ECONOMICSHealth Econ. 26: 1067–1086 (2017)Published online 11 July 2016 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/hec.3384

Understanding the demand for noncigarette tobacco products and how the demand for noncigarette tobaccoproducts interacts with the demand for cigarettes is important. As cigarettes become more expensive and aremore regulated compared with other tobacco products, smokers might turn to other products and thus not quitusing tobacco, which may hinder smoking cessation or lead to other health problems. For examples, the WorldHealth Organization (2006) reported that pipe smokers face a substantially higher risk (than the risk faced bynonsmokers) of diseases such as chronic obstructive pulmonary disease, oral, head, and neck cancers, laryngealcancer, oesophageal cancer, and lung cancer. Smokeless tobacco products contain addictive levels of nicotine,heavy metals, and typically several carcinogens that cause head, neck, and throat cancers with high rates ofpremature mortality. Currently, little is known about the potential health benefits or harms of e-cigarettes(Benowitz and Goniewicz, 2013), although a recent study by Grana et al. (2014) suggests that e-cigaretteemissions might cause and/or worsen respiratory diseases. Indeed, adult multiple tobacco product use hasbecome more prevalent, suggesting that some tobacco products may be complements. Based on a U.S. nationaladult survey, Lee et al. (2014) reported that 32.1% of adults currently use one or more tobacco products, with6.9% using cigarettes with another product (i.e., dual use), 1.3% using two noncigarette products, and 2.4%using three or more products (i.e., polytobacco use).

This research estimates a system of demand for six broad category levels that cover almost all tobaccoproducts: cigarettes, little cigars and cigarillos, large cigars, e-cigarettes, smokeless tobacco (including chewingtobacco, snuff, and dissolvables), and loose smoking tobacco (including pipe and roll-your-own tobacco). Weestimate this demand system using the Nielsen ScanTrack market-level scanner data collected from U.S.convenience stores. We also include advertising data collected by Kantar Media. This study provides own-and cross-price elasticities, and advertising elasticities for the six categories of tobacco products. Our resultshave implications for predicting the demand-side response of a tax change (or a regulation-induced costincrease) on one or several tobacco products.

The rest of the paper is organized as follows. Section 2 provides a literature review of the demand forvarious tobacco products. Section 3 describes the model we used. Section 4 describes the data and the productcategory classification used in this study. Section 5 presents the results. Section 6 contains concluding remarksand discussion.

Figure 1. Tobacco products by value of shipments (2000–2011)

Y. ZHENG ET AL.1068

Copyright © 2016 John Wiley & Sons, Ltd. Health Econ. 26: 1067–1086 (2017)DOI: 10.1002/hec

2. LITERATURE ON THE DEMAND FOR TOBACCO PRODUCTS

The economics and public health literature is rich in studies on cigarette demand. In contrast, there are only afew studies on smokeless tobacco demand and even fewer studies on the demand for cigars, e-cigarettes, andloose smoking tobacco. For cigarette demand, a large number of studies (we list only a few here) have exam-ined a wide range of issues including effects of advertising expenditures (Baltagi and Levin, 1986; Hanewinkelet al., 2010), price and price-related promotions (Tauras et al., 2006), advertising bans (Saffer and Chaloupka,2000; Qi, 2013), smoking bans (Farrelly et al., 1999), tobacco control program expenditures (Farrelly et al.,2003), smuggling (Gruber et al., 2003), taxes (Evans and Ringel, 1999; Gruber and Koszegi, 2004), and othertobacco control interventions. Gallet and List (2003) conducted a meta-analysis of 86 published studies oncigarettes and found an average price elasticity of �0.48, income elasticity of 0.42, and advertising elasticityof 0.10. Chaloupka et al. (2002) summarized that most of the elasticity estimates from the United States andother high-income countries tend to fall between �0.25 and �0.50. However, researchers utilizing retail U.S.scanner data generated more elastic elasticities for cigarette demand, such as Da Pra and Arnade (2009) (usingsupermarket, drug, and convenience store data) and Adhikari et al. (2012) (using supermarket data).1 Table Ibriefly summarizes the literature on the own-price and advertising elasticities for tobacco products.

Studies on the demand for other tobacco products generally focus on the effects of advertising expenditures,tobacco control program expenditures, price/taxation, restrictions on sales, and the substitutability and comple-mentarity to cigarettes. For example using survey data, Ringel et al. (2005) estimated logistic regression modelsof the probability of current cigar use to examine the effect of prices and regulations on youth cigar demand.They reported a price elasticity of participation of �0.34, which reflects how cigar price affects cigar smokingparticipation. Youth cigar demand was not found to be sensitive to state tobacco-control regulations. However,Ciecierski et al. (2011) found evidence that higher state expenditures on tobacco control programs in general(not necessarily targeting cigar users) reduced cigar use among college students. Da Pra and Arnade (2009) es-timated a price elasticity of demand of �0.50 for cigars in the United States. Nguyen and Grootendorst (2015)found that banning the sale of flavored cigarillos in Canada increased the use of cigarillos but decreased the useof large cigars among youth. More recently, Gammon et al. (2015) reported an own-price elasticity of �2.50for little cigars based on retail scanner data.

For smokeless tobacco, many researchers have shown that demand responded negatively to higher smoke-less tobacco taxes or prices (Ohsfeldt and Boyle, 1994; Chaloupka et al., 1997; Ohsfeldt et al., 1997; Tauraset al., 2007). Chaloupka et al. (1997) reported a price elasticity of �0.65 for youth and found that limits onyouth access to smokeless tobacco products were effective in reducing the initiation and frequency of smoke-less tobacco use. A recent study by Dave and Saffer (2013) found that the price elasticity for all users was�0.38, and the magazine advertising elasticity on smokeless tobacco demand was 0.06.

Findings have been mixed as to whether smokeless tobacco and cigarettes are substitutes or complements.On the one hand, Tauras et al. (2007) and Dave and Saffer (2013) found that smokeless tobacco products andcigarettes were economic complements in consumption, with the former reporting a cross-price elasticity ofsmokeless tobacco prevalence (with respect to cigarette price) of �0.72 among male high school studentsand the latter reporting a cross-price elasticity of smokeless tobacco participation of �0.77 for male users.Tauras et al. noted that complementarity could be a result of youth experimenting with both cigarettes andsmokeless tobacco as part of the tobacco uptake process. Based on aggregate annual time-series data, Baskand Melkersson (2003) reported negative cross-price elasticities between cigarettes and snus (Swedish moistsnuff) for the Swedish market, which indicates that use of snus might contribute to increased smoking.Alternatively, at least three studies found substitutions between cigarette smoking and smokeless tobaccouse. Adams et al. (2013) found a substantial increase in smokeless tobacco use among smokers after

1Using Nielsen scanner data from supermarkets, drug stores, and mass merchandisers, Tauras et al. (2006) estimated the own-price elastic-ity of market share for premium, discount, and deep discount cigarettes, which ranged from�0.2 (premium) to�0.62 (deep discount). Theauthors did not report the corresponding price elasticities of demand.

TOBACCO DEMAND SYSTEM 1069


implementation of smoking bans in bars, suggesting some compensatory behavior among smokers. Usingmarket-level scanner data, Adhikari et al. (2012) found that the cross-price elasticity of cigarettes sales withrespect to smokeless tobacco price was between 0.15 and 0.19. A recent study by O’Connor et al. (2014) foundthat snus, dissolvables, and nicotine lozenges, on average, served as substitutes for cigarettes.

We found that only a few studies have examined the demand for e-cigarettes and loose smoking tobacco.Based on single equations, Huang et al. (2014) used market-level scanner data collected from U.S. food, drug,mass, and convenience stores and reported a price elasticity of �1.2 for disposable e-cigarettes and �1.9 forreusable e-cigarettes. They found that e-cigarettes were neither substitutes or complements to cigarettes.Focusing on six European Union member countries, Stoklosa et al. (2016) found an own-price elasticity of�0.82 (static model) and �1.15 (dynamic model) for e-cigarettes and a substitution relationship betweene-cigarettes and cigarettes. Zheng et al. (2016) reported an own-price elasticity of �2.77 for e-cigarettes, basedon U.S. market level scanner data. They did not find a substitution relationship between e-cigarettes andcigarettes, based on a dynamic system model. Hanewinkel et al. (2008) analyzed the price responsiveness ofdemand for cigarettes and loose tobacco (known as roll-your-own tobacco in the United States) in Germanyand found loose tobacco to be a substitute for factory-made cigarettes. Estimating cigarettes, cigars, chewingtobacco, and smoking tobacco (defined by the authors as roll-your-own tobacco, loose leaf tobacco, andpipe tobacco) in a demand system, Da Pra and Arnade (2009) reported an own-price elasticity of �0.60 forsmoking tobacco.

Our study builds on the previous literature but makes several important contributions. First, we attempt toenrich the scant literature on the demand for e-cigarettes. Second, loose smoking tobacco has never beenstudied together with other tobacco products in published studies. Our study fills this gap. The literaturesuggests some substitution relationships between cigarettes and loose smoking tobacco and between cigarettesand e-cigarettes, and an ambiguous relationship between cigarettes and smokeless tobacco. Our demand systemallows us to identify all possible pairwise substitution and complementary relationships between the six broadcategories. The advantage of using a demand system over single equations includes better predictive power(Klaiber and Holt, 2010) and easy imposition or test of theoretical restrictions such as curvature (i.e., Slutskymatrix is negative semidefinite) (Barnett and Serletis, 2008), etc. For example, although there are plenty ofstudies estimating price elasticities for soft drinks (Andreyeva et al., 2010), only a few have used a demandsystem approach. Zhen et al. (2014) used Nielsen scanner data to estimate a demand system for 23 packagedfoods and beverages, in order to estimate the potential impact of a tax increase in sugar-sweetened beverages.Not controlling for possible substitution effects/complements effects such as higher consumption of wholemilk after the tax increase likely will lead to an overestimate of the health benefits of the tax increase. Inaddition, the property of aggregating perfectly over consumers of the AIDS model fits our use of aggregatedata at the market level rather than individual level data. Furthermore, our study includes advertising

Table I. A summary of the literature on the own-price and advertising elasticities for tobacco products

Tobacco product Own-price elasticity Note Authors Advertising elasticity

Cigarettes �0.48 Meta-analysis Gallet and List (2003) 0.10�0.25 to �0.50 High-income countries Chaloupka et al. (2002)�1.00 Da Pra and Arnade (2009)�0.75 Adhikari et al. (2012)

Cigars �2.50 Little cigars Gammon et al. (2015)�0.34 Elasticity of participation Ringel et al. (2005)�0.50 Da Pra and Arnade (2009)

E-cigarettes �1.20 to �1.90 Disposable and reusable Huang et al. (2014)�0.82 to �1.15 Stoklosa et al. (2016)�2.77 Zheng et al. (2016)

Smokeless tobacco �0.65 For youth Chaloupka et al. (1997)�0.38 Dave and Saffer (2013) 0.06

Loose smoking tobacco �0.60 Da Pra and Arnade (2009)

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expenditures (excluding point-of-sale advertising) on all tobacco products at the category level. Therefore,we add to the literature not only on the effectiveness of own advertising on the demand for tobaccoproducts, such as e-cigarettes, but also on the cross-category effects of tobacco product advertising, suchas the impact of e-cigarette advertising on cigarette demand (also known as the spillover effects) (Zhengand Kaiser, 2008).

3. TWO-STAGE BUDGETING DEMAND SYSTEM

Assuming weakly separable preferences between tobacco products and an outside good (a numéraire all othergoods combined), we estimated tobacco category choices using a two-stage budgeting model. In the first stageof our model, the smoker allots total income among tobacco products as a group and the outside good. In thesecond stage, the smoker chooses among six categories of tobacco products given the group expenditure ontobacco determined from the first stage. We obtained the conditional elasticities (on a given level of groupexpenditure on tobacco products) from the second stage and then converted them into unconditional elasticitiesby using the first-stage estimates.

At both stages, we used the nonlinear version of Deaton and Muelbauer’s (1980) Almost Ideal DemandSystem. This model has many desirable properties, including the ability to provide a first-order approximationto any demand system at a point and exactly satisfy the axioms of choice and to allow for a convenient spec-ification for nonhomothetic behavior. Following the literature on multistage demand, we first specified thesecond-stage demand as

wimt ¼ αi þX6

j¼1γij ln pjmt þ βi lnxmt � lnpmtð Þ; (1)

where i and j (i, j=1…6) index product category (see Figure 2 for category index numbers), m and t index theNielsen market and period, respectively; α, β, and γ are parameters; ln stands for natural logarithm; wimt is thebudget share of category i in market m and period t within tobacco products; pjmt is a panel RWGEKS priceindex of category j in market m and period t; xmt is the per capita total tobacco expenditure.

The panel RWGEKS price index is a panel price index developed by Zhen (2014) based on the multilateralGEKS index (Gini, 1924) and the time-series GEKS developed in De Haan and van der Grient (2011) for high-or medium-frequency scanner data. The advantage of using price indices rather than unit values (i.e., costs perunit) to represent category-level prices is that the GEKS price index eliminates chain drift in scanner data priceindices (De Haan and van der Grient, 2011) and reduces the simultaneity bias arising from any within-categoryquality–quantity trade-off consumers may pursue (Deaton, 1988). For example, as cigarette prices increase,consumers may economize by switching from premium brands to discount brands. A price index is a simpleway to account for this type of within-category substitution without explicitly modeling brand-level demand.

Figure 2. Six tobacco categories used in this study



The log of the group price index defined as

ln pmt ¼ α0 þX6

i¼1αi ln pimt þ 0:5

X6

i¼1

X6

j¼1γij ln pimt ln pjmt: (2)

Theoretical restrictions were imposed, including adding up∑6i¼1αi ¼ 1, and∑6

i¼1βi ¼ ∑6i¼1γij ¼ 0 for all j,

homogeneity ∑6j¼1γij ¼ 0 for all i, and symmetry γij= γji for all i, j.

The literature summarized in the previous section has shown that many factors other than price and income,such as socioeconomic characteristics and advertising, can affect demand for tobacco products (e.g., Farrellyet al., 2003). We used a demographic translation procedure to introduce such variables into the demand systemfollowing Pollak and Wales (1981). Specifically, the parameter αi in equations (1) and (2) takes a linear func-tion in the following form:

αi ¼ αi0 þX5

j¼1; j≠3αijlnadvjmt þ αi6lnBlackmt þ αi7lnHispmt þ αi8lnAsianmt

þαi9lnUImt þ αi10lnPovertymt þ αi11lnAge1519mt þ αi12lnAge65plusmt þ αi;DDummymt ;

(3)

where advjmt is the real advertising expenditure on category j in market m and period t2; Blackmt, Hispmt, andAsianmt are the percentages of the population that are black, Hispanic, and Asian in market m and period t, re-spectively; UI stands for the unemployment rate; Poverty is poverty rate; Age1519 and Age65plus are propor-tions of population that are between ages 15 and 19, and older than 65 respectively; Dummymt is a vector ofdummy variables controlling for time and space variation in demand, including quarterly dummy variables(×3), dummy variables for Nielsen markets (×29), and region specific monthly trend (×3). The four regionsare Northeast, South, Midwest, and West as defined by the Census Bureau. We included the monthly time trendto control for factors not accounted for such as smoking bans and anti-smoking laws, and we allowed sucheffects to be region specific because many of the regulations are local. Finally, αi0 through αi12 and vectorαi,D are parameters to be estimated.

Reserving capital letters for the first-stage demand, we specified the first-stage demand similar to equations(1) through (3):

Wgmt ¼ Ag þX2

k¼1Γgk lnPkmt þ Bg ln INCmt � ln Pmtð Þ; (4)

ln Pmt ¼ A0 þX2

g¼1Ag ln Pgmt þ 0:5

X2

g¼1

X2

k¼1Γgk ln Pgmt ln Pkmt; (5)

Ag ¼ Ag0 þ Ag1lnADVmt þ Ag2lnBlackmt þ Ag3lnHispmt þ Ag4lnAsianmt þ Ag5lnUImt

þ Ag6lnPovertymt þ Ag7lnAge1519mt þ Ag8lnAge65plusmt þ Ag;DDummymt ;

(6)

where g and k (g, k=1, 2) index tobacco products and the outside good; Wgmt is the budget share of the aggre-gate product g (g=1 for tobacco products as a group and g=2 for the outside good) in market m and period t;Pgmt is the aggregate product price index, where for tobacco products P1mt is a weighted average price index ofthe Stone–Laspeyres type (Hausman, 1997, p. 218) and for the outside good P2mt is obtained by solving theconsumer price index equation lnCPImt=W1mtP1mt+W2mtP2mt for P2mt (Wohlgenant, 1989); INC is per capitaincome; ADV is total advertising expenditures on tobacco products; and A, B, and Γ are parameters. Adding up,homogeneity, and symmetry restrictions were imposed as well. Given the conditional price elasticities (ηij)

2Because we have advertising expenditures on all cigars and could not break them down by little cigars/cigarillos versus large cigars, wehave j ≠ 3 in equation (3); in addition, loose smoking tobacco was not advertised for our data period.

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estimated from the two-stage demand specifications, we used the following relation (Edgerton, 1997) to recoverthe unconditional price elasticities (εij) for tobacco products:

εij ¼ ηij þ ηix∂lnx∂lnpj

¼ ηij þ ηix∂lnx∂lnP

∂lnP∂lnpj

¼ ηij þ ηixwj 1þ Θ1ð Þ; (7)

where ηix is the second-stage expenditure elasticity for product i, wj is the conditional budget share of tobaccoproduct j in the second stage, and Θ1 is the first-stage price elasticity for the tobacco group.

4. DATA DESCRIPTIONS

Our data on purchase quantities and dollar sales were obtained from Nielsen ScanTrack, which collectsobservations of data purchases at the point of sale from convenience stores, food stores, drug stores, and massmerchandisers across the United States. We use convenience store retail sales data because according toNielsen, convenience stores have accounted for the majority of retail cigarettes sales (86.9% compared with6.7% and 6.4% in grocery and drug stores, respectively, in 2014). Nielsen’s convenience store sample repre-sents all convenience store types and includes chain stores, non-chain and independent convenience stores,and convenience stores found in gas stations. The complete 30 convenience channel markets are listed inTable II.3 We used data in 4-week periods from November 2009 through April 2013, resulting in a sample sizeof 1284 market and four-weekly period combinations for each product category. The definitions and summarystatistics for the variables used in this study are reported in Table III.

Figure 2 shows a breakdown of the six tobacco categories used in this study. The first category, cigarettes,includes all cigarettes sold in convenience stores with varying characteristics, such as brand, strength, tar level,package type (pack, carton, half carton, canister, tray), box type (hard, soft, round corner box, flask, other),menthol and nonmenthol, and filtered or not filtered. The second category, little cigars/cigarillos, includes allcigars labeled as little cigars, cigarillos, small cigars, or any other label indicative of a smaller than normal cigarsize.4 The third category, large cigars, includes any cigars that are labeled large or not otherwise labeled to in-dicate a smaller than normal cigar size. Both sizes of cigars are differentiated by their brand, cigar size, numberof cigars within a pack, style (filter tip, versus nontip), and flavor. The e-cigarette category includes dispos-ables, starter kits, and replacement cartridges. During the sample period, e-liquid was not sold in any substantialamount in convenience stores. The same is true of e-hookah and e-cigars. The defining characteristics includebrand, flavor, and milligrams of nicotine where specified. Although the model does not differentiate withinproduct categories, the defining characteristics above are used to define a unique product when calculatingthe price indices. Smokeless includes various types of noncombustible tobacco, including moist snuff, drysnuff, loose leaf, plug, twists, and dissolvable tobacco. The defining characteristics include brand, cut (e.g.,long, regular, fine, thick), package size (ounces or count in the case of dissolvable, plug, and twist smokelesstobacco), and flavor. Roll-your-own tobacco includes cigarette and general smoking tobacco, and its definingcharacteristics include brand, size in ounces, flavor, and container type. Pipe tobacco and roll-your-owntobacco are combined into the loose smoking tobacco category because of insignificant sales volumes in the data.

Figure 3 illustrates the average GEKS price indices and per capita sales (quantity) over our data period.Panel ((3)a) shows that e-cigarettes prices dropped significantly over this period, while the prices for the otherproduct categories remained quite stable after the federal tax increase in 2009 for many tobacco products. Panel((3)b) shows that the overall consumption trend was flat for cigarettes, cigars, and loose smoking tobacco and

3We excluded nicotine replacement therapy (NRT) products in the demand system because NRT sales in convenience stores are limited.4This is based on the product string Nielsen derived from product packaging, which is updated every few years. New product categories,such as e-cigarettes, are updated more frequently. Our classification of cigars does not match the federal tax definitions of little and largecigars, which are solely based on weight.



was up for smokeless tobacco and e-cigarettes. Figure 4 illustrates the average GEKS price indices and percapita sales across markets, displaying large variations of price and sales across different markets.

County-level demographic and economic data were aggregated to the Nielsen market level and used ascovariates in the demand model. Race/ethnicity category and age percentages in each market were derived from

Table III. Definitions and summary statistics of variables (N= 1284)

Variable Description Mean Min. Max. S.D.

Second-stage variablesw1mt Budget share for cigarettes 0.881 0.803 0.946 0.031w2mt Budget share for little cigars/cigarillos 0.023 0.007 0.042 0.006w3mt Budget share for large cigars 0.018 0.006 0.044 0.009w4mt Budget share for e-cigarettes 0.003 0.000 0.018 0.003w5mt Budget share for smokeless tobacco 0.074 0.021 0.152 0.029w6mt Budget share for loose smoking tobacco 0.002 0.00003 0.020 0.003p1mt GEKS price index for cigarettes 1.029 0.789 1.596 0.166p2mt GEKS price index for little cigars/cigarillos 1.054 0.787 1.578 0.161p3mt GEKS price index for large cigars 1.025 0.776 1.481 0.151p4mt GEKS price index for e-cigarettes 0.822 0.488 1.644 0.165p5mt GEKS price index for smokeless tobacco 1.060 0.713 1.670 0.215p6mt GEKS price index for loose smoking tobacco 1.072 0.796 1.482 0.172adv1mt Real (2013) advertising expenditures for

cigarettes per 1000 people$15.540 $1.153 $32.005 $6.657

adv2mt Real advertising expenditures for cigarsper 1000 people

$2.200 $0.098 $6.732 $1.398

adv4mt Real advertising expenditures for e-cigarettesper 1000 people

$2.694 $0.000 $18.774 $4.116

adv5mt Real advertising expenditures for smokelesstobacco per 1000 people

$9.463 $0.054 $29.135 $8.260

xmt Per capita expenditures on tobacco products $17.532 $6.074 $35.977 $6.178

First-stage variablesW1mt Budget share for tobacco products 0.014 0.003 0.032 0.006W2mt Budget share for the outside good 0.986 0.968 0.997 0.006ADVmt Per capita real advertising expenditures on

tobacco products$29.00 $10.39 $54.24 $9.14

INCmt Per capita 4-week income $1301 $1035 $1929 $194

Variables shared by both stagesBlackmt Percentage of population that is black 0.137 0.019 0.305 0.077Hispmt Percentage of population that is Hispanic 0.154 0.024 0.460 0.127Asianmt Percentage of population that is Asian 0.045 0.011 0.238 0.044Age1519mt Percentage of population between

ages 15 and 190.068 0.055 0.079 0.004

Age65Plusmt Percentage of population older than 65 0.134 0.091 0.223 0.024Povertymt Poverty rate 0.151 0.106 0.189 0.023UImt Unemployment rate 0.086 0.049 0.147 0.016

Table II. Nielsen convenience channel markets

Atlanta Little Rock Philadelphia

Birmingham Los Angeles PhoenixBoston Louisville PortlandChicago Miami Raleigh/DurhamCincinnati Minneapolis Richmond/NorfolkCleveland Nashville San AntonioDallas/Ft. Worth New Orleans/Mobile San FranciscoDenver New York SeattleDetroit Oklahoma City/Tulsa St. LouisHouston Orlando Tampa

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2010 Census data (U.S. Census Bureau, 2015n.d.) at the county level. We summed the population totals andsubpopulation totals across each county and divided each market’s subpopulation for race/ethnicity (white[base group in the model], black, Hispanic, and Asian) by the total population to get the percentage from eachdemographic category. All non-Hispanic race counts exclude those that identify as Hispanic from their counts.In other words we are not double-counting the Hispanic population in our definition of white, black or Asian.

The poverty rate estimates come from the Small Area Income and Poverty Estimates (SAIPE) from thecensus website. By year, we sum the total number of people in poverty across counties in each Nielsen marketand divide by the total population in each Nielsen market. Per capita income is available from the Bureauof Economic Analysis for the years 2009 to 2012 by county. We took the population-weighted average ofincome across counties to aggregate to Nielsen scanner markets. Unemployment data are available by countyfrom the Bureau of Labor Statistics on a monthly basis. For a more stable estimate, we took the quarterlyaverage of unemployed people summed across counties within markets, not seasonally adjusted, and dividedit by the quarterly average of people in the labor force summed across counties within markets, again notseasonally adjusted.

Figure 3. Average price and sales over time. [Colour figure can be viewed at wileyonlinelibrary.com]



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Advertising data, excluding point-of-sale advertising, on tobacco products came from Kantar Media’sStradegy database. Kantar Media tracks advertisements placed in over 400 consumer magazines, 100 U.S.markets for outdoor media, 6000 Web sites, 90 television networks, 200 newspapers, and 4000 radio stations.The database provides access to detailed information on product advertising, such as type and brand of productadvertised, placement of advertisements (e.g., specific magazine issue and page number), date of publication,and expenditure on advertisement, as well as copies of actual advertisements available for download(magazines only). For each media channel, we extracted information from Stradegy on the frequency of andexpenditures on tobacco product advertisements by brand, market, and media channel-specific information(e.g., publication name, television program name, Internet site category). Kantar’s Stadegy advertising datahas appeared in many other academic studies such as the marketing studies conducted by Naik and Raman(2003) and Borah and Tellis (2016).

The advertising measure we use for each product category was the sum of all advertising expenditures in thatcategory in each location and in each time period divided by population in the media market multiplied by1000. Because Kantar media markets are related to Nielsen media markets instead of Nielsen scanner markets,we linked by counties and took the weighted average of the media market expenditure measure, where the

Figure 4. Average price and sales across markets. [Colour figure can be viewed at wileyonlinelibrary.com]

Y. ZHENG ET AL.1076


http://wileyonlinelibrary.com

weights are the population in the media markets that are also in the Nielsen scanner market. Note, there can bemore than one media market per Nielsen scanner market and they are not all encompassing, meaning that someof the counties that are in the media market are not in the Nielsen scanner market. Table IV shows the averageadvertising expenditures across all markets for various tobacco categories studied in this study (note that loosesmoking tobacco was not advertised), broken down by eight media channels. It is important to note that we donot have data on point-of-sale advertising, which is where most cigarette (and potentially other tobacco prod-ucts) advertising expenditures occur. In the case that magazine advertising and point-of-sale advertising arelikely positively correlated, not including point-of-sale advertising might lead to an upward bias in the effectof cigarette magazine advertising on cigarette demand. As shown, advertising expenditures for cigarettes stillexceeded expenditures on cigars, e-cigarettes, and smokeless tobacco by a large margin. Magazine advertisingaccounted for the majority of the advertising expenditures on each of the four tobacco categories. Althoughcigarette advertising on television has been banned and cigar companies spent little on television advertising,e-cigarettes companies have become the biggest spender on television advertising, reaching $0.73 per 1000people in a 4-week period.

5. ESTIMATION AND RESULTS

The model was estimated using the PROC MODEL procedure in SAS 9.3. Because the six budget shares in thesecond stage and the two budget shares in the first stage sum to one, we must drop one equation in each stage toavoid having a singular error covariance. The loose smoking tobacco equation in the second stage and thenuméraire equation (representing all other consumption goods) in the first stage were dropped in the estimation,and their parameters were calculated using the aforementioned adding-up restrictions. We used the iterative,seemingly unrelated regression for the second-stage demand to account for possible nonzero residual correla-tion across equations and nonlinear least squares for the first-stage demand. The estimated parameters for thefirst and second stages are reported in Appendix Tables A-1 and A-2, respectively. The model provides asatisfactory fit for the data in that the adjusted R2’s ranged from 0.763 for the e-cigarette equation to 0.991for the smokeless tobacco equation.5 Based on the estimated parameters and elasticity formula for the nonlinearAlmost Ideal Demand System model in the literature (i.e., the formula provided by Green and Alston (1990)on price elasticities), we calculated the elasticities and report the full results for both stages in AppendicesTables B-1 and B-2, and discuss the conditional price and advertising elasticities here.

5.1. Conditional price elasticities

The first panel of Table V reports the Marshallian price elasticities conditional on total tobacco expenditures.The column headings indicate the price change and the row headings indicate the quantity change. So, allthe diagonal elements are own-price elasticities. We found that all conditional own-price elasticities of demandare negative and statistically significant (at the 5% level), ranging from �0.519 for smokeless tobacco to�0.980 for cigarettes, �1.423 for little cigars/cigarillos, �1.498 for large cigars, �1.678 for loose smokingtobacco, and �2.053 for e-cigarettes.

The conditional cross-price elasticities, positive for substitutes and negative for complements, measure thepercentage change in conditional demand for tobacco category i (rows in the table) with respect to a 1% pricechange in tobacco category j (columns in the table), while holding other factors constant. Among other things,we found that e-cigarettes are a substitute for cigarettes (cross-price elasticity [with respect to e-cigarette price]= 0.004) and vice versa (cross-price elasticity = 1.859), whereas large cigars, smokeless tobacco, and loose

5The high R2 around 0.99 for several categories seems extraordinarily high at first glance but is common in empirical demand system studiesbecause all prices of substitutes and complements are accounted for. For example, in Deaton and Muelbauer’s (1980) seminal work on theAIDS model, the R2 for food (one category) in their system is 0.999, while the lowest R2 in the system is 0.79.



Table IV. Average advertising expenditures per 1000 people per 4-week period (excluding point-of-sale advertising) forvarious tobacco categories

Media Cigarettes Cigars E-cigarettes Smokeless

Internet $0.010 $0.084 $0.115 $0.212Outdoor — $0.002 $0.002 $0.001Spot radio — $0.004 $0.005 —Net radio — $0.007 $0.006 —Newspaper $0.001 $0.006 $0.010 $0.143News magazine — $0.051 $0.097 $0.131Magazine $15.529 $2.046 $1.730 $8.977Television — $0.001 $0.727 —

Table V. Estimated elasticities

Product category

Conditional price elasticities from the second stage AIDS model

CigarettesLittle cigars/cigarillos Large cigars E-cigarettes Smokeless tobacco

Loose smokingtobacco

Cigarettes �0.980*** �0.004 �0.007*** 0.004*** �0.045*** �0.001**Little cigars/cigarillos 0.003 �1.423*** 0.624*** �0.032 0.034 �0.062***Large cigars �0.068 0.803*** �1.498*** �0.033 0.032 0.065***E-cigarettes 1.859*** �0.225 �0.188 �2.053*** 0.327 0.058Smokeless tobacco �0.291*** 0.013 0.003 0.010 �0.519*** 0.037***Loose smoking tobacco �0.354 �0.778*** 0.486*** 0.047 1.016*** �1.678***

Product category

Conditional advertising elasticities

Cigarettes Cigars E cigarettesSmokelesstobacco

Cigarettes �0.0003 0.0001 �0.0002 �0.001***Little cigars/cigarillos 0.005 0.003 �0.004 0.004***Large cigars 0.0005 �0.008 �0.009*** 0.003E-cigarettes 0.136*** �0.040 0.047*** 0.065***Smokeless tobacco �0.003 0.001 0.004*** 0.002***Loose smoking tobacco �0.012 �0.017 0.008 0.016***

Product category

Unconditional price elasticities from the two stages of AIDS model

CigarettesLittle cigars/cigarillos Large cigars E-cigarettes

Smokelesstobacco


Cigarettes �1.188 �0.009 �0.0111 0.004 �0.063 �0.002Little cigars/cigarillos �0.170 �1.428 0.620 �0.033 0.020 0.005Large cigars �0.210 0.800 �1.501 �0.034 0.021 0.065E-cigarettes 1.814 �0.226 �0.189 �2.054 0.323 0.058Smokeless tobacco �0.442 0.009 0.000 0.009 �0.532 0.037Loose smoking tobacco �0.609 �0.784 0.481 0.046 0.995 �1.678

Product category

Unconditional price elasticities from unrestricted single equation

CigarettesLittle cigars/cigarillos Large cigars E-cigarettes

Smokelesstobacco


Cigarettes �1.002*** 0.076*** �0.029*** �0.005 �0.068*** �0.001Little cigars/cigarillos �0.541*** �1.610*** 0.269*** 0.011 �0.179 �0.080Large cigars �1.082*** 2.355*** �1.552*** �0.042 0.193 �0.491***E-cigarettes 1.858 7.253*** �2.538*** �2.824*** �2.429 2.716***Smokeless tobacco �0.339*** 0.114** �0.068** �0.012 0.534*** �0.028Loose smoking tobacco 1.114** �0.638 0.504 0.094 0.519 �0.775***

Note: **denotes statistically significant at 5% level and ***denotes statistically significant at 1% level for panels 1, 2, and 4. Own-price andown-advertising elasticities are marked in bold.

Y. ZHENG ET AL.1078


smoking tobacco are all complements to cigarettes. That is, a 10% increase in the price of e-cigarettes results ina 0.04% increase in conditional cigarette demand, and a 10% increase in the price of cigarettes results in an18.59% increase in conditional e-cigarette demand. Such results, while providing new insight into howe-cigarette prices affect cigarette demand, also confirm earlier findings by Huang et al. (2014) that cigarettesare substitutes for e-cigarettes (cross-price elasticity = 0.54) and by Tauras et al. (2007) and Dave and Saffer(2013) that smokeless tobacco products and cigarettes are economic complements in consumption. Our systemestimation of the cross-price elasticity between cigarettes and smokeless tobacco is much smaller at �0.045compared with �0.72 and �0.77 reported by the latter two studies.

We also found that large cigars are a substitute for little cigars/cigarillos (which is consistent with expecta-tions) and loose smoking tobacco is a complement to little cigars/cigarillos; loose smoking tobacco and littlecigars/cigarillos are substitutes for large cigars; cigarettes are complements to smokeless tobacco, while loosesmoking tobacco is a substitute for smokeless tobacco; and little cigars/cigarillos are a complement to loosesmoking tobacco, while large cigars and smokeless tobacco are substitutes for loose smoking tobacco. Suchresults might have important policy implications. For example, if e-cigarette prices increase by 10%, ourdemand system predicts that the conditional cigarette demand will increase by 0.04%, while the conditionale-cigarette demand will decrease by 20.53%.

5.2. Conditional advertising elasticities

Advertising effects are shown in the second panel of Table V. The column headings indicate the advertisingchange and the row headings indicate the quantity change. We found evidence of positive own-advertisingeffects as well as opposing cross-category advertising effects. We estimated that the elasticity of e-cigaretteadvertising (mainly magazine and television ads, as shown in Figure 3) on its own demand was 0.047, andthe elasticity of smokeless advertising (almost all were magazine ads) on its own demand was 0.002. Therefore,the aggressive e-cigarette advertising may have partly contributed to e-cigarettes’ exponential sales increase inthe United States. We found that cigarette magazine advertising increased demand for e-cigarettes but did notincrease demand for cigarettes. However, the insignificant own effect of cigarette magazine advertising may notsuggest ineffective overall cigarette marketing efforts because our data do not account for point-of-sales market-ing (e.g., tobacco display over the counter).6 We also found that e-cigarette advertising reduced demand forlarge cigars and smokeless tobacco magazine advertising decreased demand for cigarettes. E-cigaretteadvertising and smokeless tobacco magazine advertising reinforced demand for each other.7

5.3. Unconditional price elasticities

The third panel of Table V presents the unconditional price elasticities based on the first- and second-stagedemand estimates. The unconditional own-price elasticities are �1.188 for cigarettes, �1.428 for littlecigars/cigarillos, �1.501 for large cigars, �2.054 for e-cigarettes, �0.532 for smokeless tobacco, and�1.678 for loose smoking tobacco. The unconditional price elasticities largely resemble closely the conditionalprice elasticities. Our unconditional own-price elasticity for cigarettes is above the �0.25 to �0.50 range sum-marized by Chaloupka et al. (2002). For one reason, as we discussed earlier, studies using retail scanner datatended to yield more elastic own-price elasticities for cigarettes. In our case, because of possible substitutionbetween convenience stores and other retail outlets, our own-price elasticity estimates based on conveniencestore data may be more elastic than overall demand. Second, with more substitutes for cigarettes in the marketin recent years, especially e-cigarettes, smokers may become more sensitive to cigarette prices. Finally, because

6We are not aware of a data source for point-of-sale advertising data that can be used for econometric modeling.7We also re-estimated the second-stage demand by constructing an advertising stock following Dave and Saffer’s (2013) specification (i.e.,current month’s advertising and a decay-weighted sum of advertising over the past 6 months). Although the sample size decreased by morethan 10% (30 markets with six lags) by doing so, our results remain very robust.



our data are sales data, the sales elasticities may be more responsive to prices than consumption elasticities ifsmokers stockpile to take advantage of temporary price discounts.

Compared with other elasticity estimates in the literature, our unconditional own-price elasticities for largecigars and loose smoking tobacco are more elastic than the ones estimated by Da Pra and Arnade (2009)(�1.501 vs. �0.50 for large cigars and �1.678 vs. �0.60 for loose smoking tobacco). Our unconditionalown-price elasticity for smokeless tobacco compares favorably with the one reported by Dave and Saffer(2013) (�0.532 vs. �0.38). With respect to e-cigarettes, our estimated own-price elasticity of �2.054 (whichis an overall elasticity for reusable and disposable e-cigarettes) is comparable to the one reported by Huanget al. (2014) on reusable e-cigarettes (�1.90) but is more elastic than the one on disposable e-cigarettes (�1.20).

In the last panel of Table V, we report for comparison purpose the unconditional price elasticities estimatedfrom single equation using iterated seemingly unrelated regression as well. We used a log–log (or double log)specification. Unlike demand systems, no homogeneity or symmetry conditionals were imposed in singleequation estimation. Results show that the own-price elasticities for cigarettes, little cigars/cigarillos, and largecigars are very comparable with our system estimates. The single-equation estimate of e-cigarettes’ own-priceelasticity is more elastic at �2.824 while that of loose smoking tobacco is much less elastic at �0.775. Themost striking result is that the single-equation estimate of own-price elasticity for smokeless tobacco is positive(0.534) and statistically significant. In addition, the single-equation estimates do not report a substitute orcomplement relationship between cigarettes and e-cigarettes. Overall, our use of demand system estimationgenerated estimates that differ from the single-equation estimates. We believe the system approach arguablybetter captures own-price effects, substitutions, and complements than the single-equation approach. Oneevidence is the positive own-price elasticity estimated from the single-equation approach.

5.4. Additional specifications (robustness checks)

A few robustness checks are described in this subsection to see how sensitive the results are to using differentregions and specifications.8 For the reason of space, we compare only the conditional own-price elasticities andown-advertising elasticities and report the findings in Table VI. The first column presents the baseline esti-mates, which were reported in the first two panels of Table V.

In the first robustness check, we re-estimated the system for the four regions defined by the Census Bureau:Northeast, South, Midwest, and West. The results, under the headings of the four regions, show that the own-price elasticity for cigarettes is similar across the four regions while the most elastic region is the South for littlecigars/cigarillos and smokeless tobacco, the Midwest for large cigars, and the West for e-cigarettes and loosesmoking tobacco. The Northeast does not have the highest elasticity (absolute value) for any of the six tobaccocategories, likely because of the region’s higher income level. Advertising was found to increase demand fore-cigarettes in the South and Midwest and increase demand for smokeless tobacco in the Northeast and Midwest.9

Because of the high local cigarette taxes, Chicago and New York City are notorious for casual cigarettessmuggling by their residents, either across borders (e.g., between New York City and New Jersey where cigarettetax is much lower) or from Indian reservations. Therefore, cigarette might be more price elastic in the two citiesbecause of smuggling. In the second check, we excluded Chicago and New York City markets to examine theelasticities for the remaining markets. Results, under the heading of ‘excluding NYC and Chicago’, confirmedour expectation in that the cigarette own-price elasticity is about 10% less elastic than the baseline estimate.

In the third check, we estimated the elasticities for large markets versus small markets. We ordered themarkets by their population at the end of 2012 and grouped the top 15 markets into the ‘large markets’ and

8We thank the handling editor and two anonymous reviewers of the journal for suggesting many insightful ways to conduct the robustnesschecks.

9As to advertising, one might argue that if there are public aspects to advertising (e.g., a billboard), using total advertising expendituresrather than per capita advertising expenditures is more appropriate. The results using total advertising expenditures are almost identical(not reported in the table to conserve space) to the baseline scenario.

Y. ZHENG ET AL.1080


Table

VI.

Conditio

nalow

n-priceandadvertisingelasticities

underdifferentscenarios—

robustness

check

Product

category

Conditio

nalow

n-priceelasticities

Baseline

Northeast

South

Midwest

West

Excluding

NYCand

Chicago

Large

markets

Small

markets

200

bootstraps

No

homogeneity

orsymmetry

Cigarettes

�0.980***

�1.002***

�1.014***

�0.970***

�1.002***

�0.900***

�0.929***

�0.946***

�0.978***

�0.967***

Little

cigars/cigarillos

�1.423***

�0.634**

�2.281***

�0.747***

�1.049***

�1.576***

�1.089***

�2.438***

�1.351***

�1.449***

Large

cigars

�1.498***

�1.181***

�1.003***

�1.603***

�0.208

�0.149

0.320

�0.925***

�1.452***

�1.594***

E-cigarettes

�2.053***

�2.629***

�2.386***

�1.757***

�2.682***

�1.640***

�2.271***

�2.321***

�2.106***

�1.949***

Smokelesstobacco

�0.519***

� 0.712***

�1.792***

�0.318***

0.045

�0.240***

�0.509***

�1.280***

�0.522***

�0.568***

Loose

smokingtobacco

�1.678***

0.581

�0.825***

�1.509***

�10.844***

�6.343***

�5.862***

�2.929***

�1.544**

�1.670***

Product

category

Conditio

nalow

n-advertisingelasticities

Cigarettes

�0.0003

�0.00005

0.001

0.00002

�0.0002

0.0003

0.001

�0.001

�0.0003

�0.0002

Little

cigars/cigarillos

0.003

�0.004

0.004

0.003

0.006

0.010

0.003

0.008

0.003

0.002

Large

cigars

�0.008

�0.002

0.019

0.004

�0.0005

0.0129

0.007

0.020

�0.007

�0.008

E-cigarettes

0.047***

0.108

0.190***

0.094***

�0.040

0.208***

0.107***

0.059**

0.046**

0.042**

Smokelesstobacco

0.002***

0.002**

�0.009**

0.010***

�0.003

0.004

0.004

0.003

0.002

0.002***

Sam

plesize

1284

126

653

254

251

1200

640

644

1284*200

1284

**Statistically

significant

at5%

level.

***S

tatistically

significant

at1%

level.



the rest into the ‘small markets’. Results show that demand is more elastic in the small markets except for thecase of loose smoking tobacco. In both the second and third robustness checks, smokeless tobacco advertisingwas not found statistically significant.

The fourth check stems from the consideration that standard errors might be clustered especially within eachNielsen market. Because SAS’s PROC MODEL procedure, the procedure we used for the system estimation,does not allow us to obtain clustered standard errors, we followed Cameron and Trivedi’s (2005, p.845) sug-gestion to bootstrap the standard errors by resampling with replacement over market clusters to achieve theequivalent results of cluster-robust standard errors (treating market as the cluster). Results from 200 bootstrapsshow that the only noticeable differences are that loose smoking tobacco’s own-price elasticity becomes statis-tically significant at the 5% level instead of the 1% level in the baseline scenario and own-advertising elasticityfor smokeless tobacco becomes statistically significant at the 10% level instead of the 1% level in the baselinescenario. Results from 400 and 500 bootstraps barely changed from the results from 200 bootstraps.

In our baseline estimates, homogeneity and symmetry restrictions were imposed to reduce the number ofparameter estimates. In the fifth check, we present the results without the homogeneity or symmetry restric-tions.10 The last column of Table VI show that both own-price and own-advertising elasticities without thetwo restrictions actually resemble the baseline estimates very well. Other key results, such as the substitutionrelation between cigarettes and e-cigarettes, and the complement relationship between cigarettes and smokelesstobacco continue to hold when the two restrictions were relaxed.

Finally, we also re-estimated the second-stage demand using three stage nonlinear least squares. Most of thetobacco products are heavily taxed (except e-cigarettes), making taxes the ideal instrumental variables (taxesaffect the market equilibrium prices through manufacturer responses but are unlikely correlated with shocksto tobacco product demand). At this time, we have obtained tax rates on cigarettes and are working on attainingtax rates on other tobacco products, which involve calculations in a standardized method across states becauseother tobacco products can be taxed as a percentage of price at the manufacturer, wholesale, or retail level orcan be taxed per unit weight. We used cigarette tax as instrument for cigarette price. We used instrumentsfor other prices and expenditures following Hausman’s (1997) strategy to create instrumental variables. Forthe price of category i (other than cigarette) in market m in period t (pimt), we used the weighted prices ofthe other markets for category i in period t as instrument. The weight is the inverse of the distance betweenthe two markets. The instrument for total tobacco expenditure in market m in period t (xmt), say t correspondsto January 2010, is the average tobacco expenditure in market m in January in years other than 2010. Whenusing instruments for expenditures and cigarette prices alone, we found that the conditional own-price elastic-ities for the six categories in the order listed in Table VI are �0.980, �0.987, �1.536, �2.037, �0.510, and�0.703 (statistically insignificant). When using instruments for expenditures and all six prices, the conditionalown-price elasticities for the six categories in the same order are �0.962, �5.548, �2.077, �5.057, 0.608, and�9.937 (statistically insignificant), respectively. Full results are available upon request. We did not focus on theestimation based on the instrumental variables because we believe taxes are much better instrumental variablesfor our case, which is evidenced by the robustness of cigarettes’ own-price elasticity in above cases (�0.980with no instrument vs. �0.980 and �0.962 with cigarette tax as instrument).

6. DISCUSSION

This study aims to provide more insight into consumer demand for cigarettes and noncigarette tobacco productsby adopting a system approach. By using the Nielsen ScanTrack market-level data, we reported the own- andcross-price elasticities of demand (conditional elasticities on tobacco product expenditures and unconditional

10Wald tests reject both the homogeneity and symmetry restrictions (though we failed to reject symmetry for 10 of the 15 pairs of param-eters), which is a typical result found in the literature on demand systems (Deaton and Muelbauer, 1980; Klaiber and Holt, 2010). Wecontinue to focus on the restricted results because restricted model provides better forecasting power (Kastens and Brester, 1996; Klaiberand Holt, 2010) and as Table VI shows later, the difference in unrestricted and restricted results is not large.

Y. ZHENG ET AL.1082


elasticities) and own-and cross-category advertising elasticities for cigarettes, little cigars/cigarillos, large ci-gars, e-cigarettes, smokeless tobacco, and loose smoking tobacco.11 The study findings may have importantpolicy implications, especially in light of recent changes in the tobacco product market arising from the intro-duction of electronic cigarettes. The risks associated with combustible tobacco products, such as cigarettes andcigars, are well established and considerable. While there are still some risks associated with non-combustibletobacco products, evidence suggests these products (e.g. low-nitrosamine smokeless tobacco products and e-cigarettes) have considerably lower risk than combustible tobacco products. This suggests that tobacco controlpolicies should differentially impact combustible versus non-combustible products (e.g., Fagerström andBridgman, 2014; Farsalinos and Le Houezec, 2015; Mainous et al., 2015). For example, a comprehensive to-bacco tax plan which differentially imposes higher taxes on combustible products and lower taxes on non-combustible products would potentially lower demand for combustible products by promoting either cessationfrom tobacco products or switching from combustible to non-combustible tobacco products. Similarly, whileadvertising should be restricted as much as possible for combustible tobacco products, fewer restrictions shouldbe placed on non-combustible products.

Our finding that cigarettes and e-cigarettes are substitutes suggests that such a differential tax as discussedabove (e.g., one proposed by Chaloupka et al., 2015) would lead to lower cigarette consumption and greatere-cigarette consumption which could lead to population level harm reduction. Our advertising results are alsoconsistent with such a strategy, i.e., allowing advertising of e-cigarettes and smokeless tobacco products wouldincrease consumption of these products relative to combustible tobacco products. On the other hand, if a newpolicy were to prohibit e-cigarette television ads, similar to what is imposed for cigarettes, the model predicts adrop in consumer demand for e-cigarettes, a minor increase in the demand large cigars, and a slight decrease indemand for smokeless tobacco.

In addition, average e-cigarette price declined considerably during our sample period (Figure 3). If this trendwere to continue, our model would predict a large increase in e-cigarette demand and slight decrease incigarette demand. However, if this trend were reversed by tax impositions on e-cigarettes, we expect to see alarge decrease in e-cigarette demand because of the high price elasticity.

Finally, our results should be interpreted with the following caveats in mind. First, we excluded nicotine re-placement therapy (NRT) products in the demand system because sales of NRT products are extremely low inconvenience stores. Previous studies found that NRT products are substitutes for cigarettes (e.g., Chaloupkaand Tauras, 2004). Second, our advertising data do not capture point-of-sale marketing. Therefore, our resultsdo not address the overall impacts of tobacco advertising on demand. Third, although convenience stores ac-count for the largest share of the tobacco market among all retail channels, future research should accountfor potential substitutions between convenience stores and other retailer types to gain a more complete pictureof the tobacco retail environment.12 Fourth, for new and emerging tobacco products, such as e-cigarettes, thedemand parameters may vary over time as smokers develop experience and habits on these products. Thepotential parametric instability may bias predictions generated from a model assuming stable preferences suchas ours. Finally, our estimate of the elasticity for e-cigarettes lumps all types of e-cigarette products together. Ifa policy differentially affected the price of different types of e-cigarette products, our results would not addressthe shifts in demand that might result from this. However, our results do shed light on potential shifts in demandthat might result from policies that would affect all types of e-cigarette products similarly.

CONFLICT OF INTEREST

None declared.

11No advertising elasticities on loose smoking tobacco were estimated.12For example, anecdotal evidence suggests that e-cigarette sales may be shifting from convenience stores to the emerging vape shopswhere consumers can buy cheaper refillable vaporizers made by smaller manufacturers (Esterl, 2014).



ACKNOWLEDGEMENTS

This work was funded by the Center for Tobacco Products at the U.S. Food and Drug Administration (FDA)under contract no. HHSF2232010110005B. We are grateful to Patricia Hall and Rebecca Bess at FDA andMatthew Farrelly and Annice Kim at RTI International for helpful comments, and to Kristin Arnold and DorisGammon for assembling Kantar Media data for us. The findings and conclusions in this article are those of theauthors and do not represent the views of FDA.

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SUPPORTING INFORMATION

Additional supporting information may found in the online version of this article at the publisher’s web site:

Table AI. Estimated parameters for the first-stage demand (N=1284, NLS used)Table AII. Estimated parameters for the second-stage demand (N=1284, ITSUR used)Table BI. Estimated elasticities based on the first-stage demand (N=1284, NLS used)Table BII. Estimated conditional elasticities based on second-stage demand estimates (N=1284, ITSUR used)

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