# Price Responsiveness of Cigarette Demand in US: Retail Scanner Data (1994–2007)

Authors: Bishwa B. Adhikari; Chen Zhen; Jennifer W. Kahende; Joshua Goetz; Brett Loomis
Journal: Economics Research International
 (2012)

---

## Abstract

This paper investigates the changes in cigarette demand in response to the changes in cigarette prices; smokeless tobacco prices; adoption of clean indoor air laws (CIALs). We used an error-correction econometric method to estimate the cigarette sales adjustment path in response to changes in prices and CIAL coverage in the United States by utilizing scanner data from supermarkets. Finding from this study indicates that smokeless tobaccos are not perfect substitutes for cigarettes, but increases in the price of cigarettes are associated with an increase in smokeless tobacco sales. The error-correction econometric method suggest that the demand for cigarettes and smokeless tobacco is related to each other; a price increase in either product leads to an increase in demand for the other product. However, the adjustment paths are quite different; an increase in cigarette prices lowers cigarette sales in relatively faster rate than decreases in smokeless tobacco prices or adoption of smoke-free laws. Changes in cigarette demand in response to changes in cigarette prices occur relatively quickly; but the full effects of smokeless tobacco price change and the adoption of 100% smoke-free laws on cigarette demand take a longer time.


---

## Body

## 1.  Introduction

Cigarette smoking is associated with premature death, productivity losses, and a substantial burden to the health-care system in the United States [1, 2]. Smoking causes several cancers, heart diseases, stroke, chronic respiratory diseases, and several other diseases [2, 3]. In addition to the adverse health consequences and increased healthcare expenses, smoking is an expensive habit, and on average, approximately $1,000 annually is spent on smoking per household with a smoker [4]. The economic burden of smoking continues to grow in the United States due to various reasons such as aging population, cohort of smokers suffering from smoking-related chronic diseases, more expensive healthcare services over time [5, 6], and stagnant smoking prevalence in recent years [3].Smoking cessation is beneficial for all ages, but quitting at earlier ages is associated with greater health and economic benefits [7]. Tobacco control interventions such as increases in excise taxes, smoke-free air laws, individual and group counseling, telephone counseling, youth access laws, and health insurance coverage for cessation are effective public health interventions [8, 9]. For example, increasing cigarette excise tax is an effective policy for lowering both smoking prevalence and per capita cigarette consumption. Prior studies on the effects of cigarette prices on consumption estimated the elasticity of demand to be approximately −0.3 to −0.56 [10–13]. Similarly, ten percent increase in cigarette prices would likely decrease the smoking participation rate by 0.59 % to 0.97% in the US [14].Most studies that estimate the price responsiveness of cigarette demand use either person level survey data or aggregate data at the state or national level. This paper uses retail scanner data from supermarkets across the United States to estimate how quickly cigarette sales adjust to changes in cigarette price, changes in the price of smokeless tobacco, and coverage (adoption) of clean indoor air laws (CIALs). Retail scanner data are aggregate data from actual cigarette sales transactions. We used an error-correction econometric methodology [15] that allowed estimating the speed of cigarette sales adjusted in response to changes in cigarette price, changes in smokeless tobacco price, and the adoption of CIALs in the US.


## 2. Data and Methods

Cigarette and tobacco products sales data were obtained from ACNielsen Company. Data contained information on price, date of purchase, location of purchase, and product characteristics such as type of the product and brand name along with universal product codes (UPCs). Data were collected quarterly from a national sample of retail stores. The sampling area included all stores with annual total sales exceeding two million US. dollars. The sample included supermarkets and food stores with full-service pharmacies but excluded the convenience stores and mass merchandisers such as Wal-Mart. Scanner data from convenience stores were too expensive to collect, and sales data from mass merchandiser may not be representative. The data collection points were distributed in 50 US markets in metropolitan areas.ACNielsen used a stratified random sampling technique to select points of sales (grocery stores) within the sampling area. Sales data from the selected stores were weighted to estimate the total sales of tobacco products for each stratum. We defined one unit of cigarette tobacco as approximately one pack (i.e., 20 cigarettes). For smokeless tobacco products, we defined a unit of smokeless tobacco as one pack of loose leaf, one tin of snuff, or one plug of chew regardless of type. We deflated prices to 2005 using the personal consumption expenditures price index (PCEPI) [16–18].We obtained CIAL data from the American Nonsmokers’ Rights foundation (http://www.no-smoke.org/). We limited our CIAL data to jurisdictions that had 100 percent smoke-free laws in place and do not allow for separately ventilated smoking rooms and have no exemption for size of restaurants, restaurants with attached bars, free-standing bars, and nonhospitality workplaces. We used population data from the U.S. Census Bureau to determine the proportion of the population in each market covered by a 100 percent smoke-free law, and then we summed population of all the jurisdictions within the market that are covered by 100 percent smoke-free laws. County-level annual per capita personal income data were obtained from the Bureau of Economic Analysis, US Department of Commerce. Per capita income, average price for cigarettes and smokeless tobacco, total dollar sales, and per capita dollar sales were deflated using the PCEPI. Descriptive statistics for the variables used in this analysis are presented in Table 1.Table 1
Descriptive statistics for population, income, cigarettes, smokeless tobacco, and 100 percent smoke-free coverage, 1994–2007.
Variable
Mean
Std.dev.
Min
Max
Market population
4,315,888
3,238,290
966,087
20,000,000
Income
Income ($ per capita)
28,729.82
5,762.29
17,709.02
55,041.41
Real income ($ per capita)
33,586.46
5,295.50
23,465.99
56,560.95
Cigarettes
Total cigarette  sales ($)
20,700,000
10,300,000
3,858,406
63,200,000
Cigarette sales ($ per capita)
5.64
2.59
0.98
15.80
Real cigarette sales ($ per capita)
6.71
3.19
1.04
19.98
Cigarette sales (pack)
9,088,453
5,342,387
899,086
29,200,000
Cigarette sold (pack per capita)
2.58
1.65
0.25
9.82
Cigarette average price ($ per pack)
2.59
0.88
1.22
5.36
Cigarette average price (real $ per pack)
3.00
0.88
1.58
5.64
Smokeless tobacco
Smokeless tobacco price (real $)
3.35
0.99
1.52
7.72
Smokeless units sold (per capita)
0.15
0.19
0.00
1.17
Smokeless tobacco sales (real $ per capita)
0.40
0.38
0.00
1.97
Smokeless tobacco dollar sales
1,120,970
1,056,168
174.12
5,894,168
Smokeless tobacco unit sales
4,96,258.90
6,07,831.10
45
3766479
Smokeless tobacco price
2.86
0.93
1.15
6.72
Smokeless dollar sales (per capita)
0.34
0.32
0
1.76
Clean indoor air laws (CIAL) coverage
Workplace coverage (percentage)
7.7%
21.9%
0%
100%
Restaurant coverage (percentage)
14.1%
32.7%
0%
100%
Freestanding bar  coverage (percentage)
8.8%
26.7%
0%
100%
Any CIAL coverage (percentage)
14.9%
32.7%
0%
100%
All CIAL coverage (percentage)
2.9%
14%
0%
100%


### 2.1. Econometric Models

We used a two-step modeling approach to analyze the effects of cigarette and smokeless tobacco prices and CIALs on cigarette sales in supermarkets. In the first step, we estimated the long-run relationship between per capita cigarette sales and the covariates. In the second step, we used an error-correction methodology [15] to estimate the adjustment path of cigarette sales which is a long-run dynamic relationship between cigarette demand and the explanatory variables.The first-step model is(1)Cit=ai+b1CPriceit+b2SPriceit+b3Incomeit+b4CIALit+c1Quarteritc2Yearit+c3MarketitTrendit,
where the subscripts i and t denote market and time period, respectively; C is per capita cigarette sales; CPrice denotes average per pack cigarette price; SPrice is average price for one unit of smokeless tobacco (The types and qualities of raw tobacco used to manufacture cigarettes and smokeless tobacco are significantly different. There is high level of product differentiation. Smokeless tobacco products are taxed at a much lower rate than the cigarettes. Effect of price of raw tobacco on real prices of cigarettes is very small, and it is safe to assume that the CPrice and SPrice are independent to each other); Income indicates annual per capita personal income; CIAL denotes the percentage of the population covered by 100 percent smoke-free laws; Quarter represents vector of seasonal dummies; Year is a vector of year-specific dummies; Market × Trend represents market-specific time trends.The seasonal dummy variable (quarter) captures seasonal variations in sales, and the variable Year is also included in the model to control for potential cross-sectional dependence arising from national demand shocks. The market-specific trend terms are included to track the changes in the public sentiment toward smoking over time. We estimate (1) using five alternative specifications of CIAL, which were constructed as the percentage of the market population covered by CIALs in (1) workplaces or (2) restaurants or (3) bars or (4) any of the three places or (5) all of the three places.Hendry and Richard [15] proposed a technique known as the error correction model (ECM), which is used when the dependent variable and covariates may be nonstationary. The ECM model can be specified as
(2)ΔCit=αi+∑k=0K(β1ΔCPriceit-k+β2ΔSPriceit-k+β2ΔSPriceit-k+β3ΔIncomeit-k+β4ΔCIALit-k)+∑m=1MθmΔCit-m+λErrorCorrectiont-1+γ1Quarterit+γ2Yearit,
where, Δ is the first difference operator (difference in two consecutive terms), and the term error correction is the regression residual obtained from the first-step regression (1). β1, β2, β3, and β4 are K-dimensional vectors of parameters; θm is an M-dimensional vector of parameters, where K and M are the optimal lag lengths for the first-differenced covariates determined empirically using the F test. Because the dependent variable in (2) is in first-differenced form, the intercept αi measures market-specific deterministic time trends. The coefficient λ has to be negative for per capita cigarette sales to converge to its long-run equilibrium level. The magnitude of λ is the speed of the adjustment of the system toward its long-run equilibrium. If |λ| is close to 1, the adjustment will be fast; if |λ| is close to 0, the adjustment process will be slow.The parameters in (2) can be used to calculate the temporal adjustment path of per capita cigarette sales in response to a price shock or a change in the CIAL. The cumulative distributed lag adjustment process Scprice,T for per capita cigarette sales in response to a $1 permanent increase in cigarette price is given by the following:
(3)Scprice,0=β10,Scprice,0=Scprice,0+β11+λ(Scprice,0-b1)+θ1Scprice,0,Scprice,2=Scprice,1+β12+λ(Scprice,1-b1)+θ1(Scprice,1-Scprice,0)+θ2Scprice,0⋮Scprice,n=Scprice,n-1+β1n+λ(Scprice,n-1-b1)+∑m=1Mθm(Scprice,n-m-Scprice,n-m-1),
where β1j (j=1,…,n) is the jth element of the parameter vector β2, β2j=0 for all j>K, and n measures the number of periods past since the initial cigarette price shock.Similarly, the cumulative distributed lag adjustment processSsprice,T for per capita cigarette sales in response to a $1 permanent increase in smokeless tobacco price is given by the following:
(4)Ssprice,0=β20,Ssprice,1=Ssprice,0+β21+λ(Ssprice,0-b2)+θ1Ssprice,0,Ssprice,2=Ssprice,1+β22+λ(Ssprice,1-b2)+θ1(Ssprice,1-Ssprice,0)⋮Ssprice,n=Ssprice,n-1+β2n+λ(Ssprice,n-1-b2)+∑m=1Mθm(Ssprice,n-m-Ssprice,n-m-1),
where β2j (j=1,…,n) is the jth element of the parameter vector β2, and β2j=0 for all j>K.Finally, the cumulative distributed lag effect of a 1% permanent increase in the percentage of the population covered by CIALs is as follows:(5)Scial,0=0.01β40,Scial,1=0.01⌊Scial,0+β41+λ(Scial,0-b4)+θ1Scial,0⌋,Scial,2=0.01⌊Scial,1+β42+λ(Scial,1-b4)+θ1(Scial,1-Scial,0)+θ2Scial,0⌋,⋮Scial,n=0.01[∑m=1MθmScial,n-1+β4n+λ(Scial,n-1-b4)Scial=0.01+∑m=1Mθm(Scial,n-m-Scial,n-m-1)],
where β4j (j=1,…,n) is the jth element of the parameter vector β4, and β4j=0 for all j>K.


### 2.2. Tests for Nonstationarity

The unit root test evaluates whether a time series variable is nonstationary and is important in time-series data like smoking policies where public sentiment toward smoking is evolving over time. If the dependent variable and some or all of the covariates in (1) are nonstationary, then statistical inference of parameter estimates in (1) using ordinary least squares (OLS) might be problematic, but simple correction may be applied to the standard OLS if the dependent variable is cointegrated with the covariates [19]. We tested for unit roots in cigarette price (CPrice), smokeless tobacco price (SPrice), income, and clean indoor air laws (CIALs).The panel version of the augmented Dickey-Fuller (ADF) test proposed by Im et al. [20] estimates regression for each potentially nonstationary variable for each cross-section. The IPS panel unit root statistic is then a standardized t-bar test statistic based on the ADF test statistics averaged across the cross-sections. The null hypothesis of the IPS test is that each series contains a unit root. Another test proposed by Pesaran, 2003, showed that panel unit root tests assuming cross-sectional independence may be biased toward concluding unit roots when there is substantial cross-sectional dependence. He devised a cross-sectional augmented Dickey-Fuller (CADF) regression to remove the cross-sectional correlation. The IPS and CADF tests were applied to the per capita unit sales, prices, income, and clean indoor air variables. The test statistics for the first-differenced series indicated that the null of nonstationarity is rejected for most series and show the evidence that C, CPrice, SPrice, Income, and CIAL are integrated of degree one.


## 2.1. Econometric Models

We used a two-step modeling approach to analyze the effects of cigarette and smokeless tobacco prices and CIALs on cigarette sales in supermarkets. In the first step, we estimated the long-run relationship between per capita cigarette sales and the covariates. In the second step, we used an error-correction methodology [15] to estimate the adjustment path of cigarette sales which is a long-run dynamic relationship between cigarette demand and the explanatory variables.The first-step model is(1)Cit=ai+b1CPriceit+b2SPriceit+b3Incomeit+b4CIALit+c1Quarteritc2Yearit+c3MarketitTrendit,
where the subscripts i and t denote market and time period, respectively; C is per capita cigarette sales; CPrice denotes average per pack cigarette price; SPrice is average price for one unit of smokeless tobacco (The types and qualities of raw tobacco used to manufacture cigarettes and smokeless tobacco are significantly different. There is high level of product differentiation. Smokeless tobacco products are taxed at a much lower rate than the cigarettes. Effect of price of raw tobacco on real prices of cigarettes is very small, and it is safe to assume that the CPrice and SPrice are independent to each other); Income indicates annual per capita personal income; CIAL denotes the percentage of the population covered by 100 percent smoke-free laws; Quarter represents vector of seasonal dummies; Year is a vector of year-specific dummies; Market × Trend represents market-specific time trends.The seasonal dummy variable (quarter) captures seasonal variations in sales, and the variable Year is also included in the model to control for potential cross-sectional dependence arising from national demand shocks. The market-specific trend terms are included to track the changes in the public sentiment toward smoking over time. We estimate (1) using five alternative specifications of CIAL, which were constructed as the percentage of the market population covered by CIALs in (1) workplaces or (2) restaurants or (3) bars or (4) any of the three places or (5) all of the three places.Hendry and Richard [15] proposed a technique known as the error correction model (ECM), which is used when the dependent variable and covariates may be nonstationary. The ECM model can be specified as
(2)ΔCit=αi+∑k=0K(β1ΔCPriceit-k+β2ΔSPriceit-k+β2ΔSPriceit-k+β3ΔIncomeit-k+β4ΔCIALit-k)+∑m=1MθmΔCit-m+λErrorCorrectiont-1+γ1Quarterit+γ2Yearit,
where, Δ is the first difference operator (difference in two consecutive terms), and the term error correction is the regression residual obtained from the first-step regression (1). β1, β2, β3, and β4 are K-dimensional vectors of parameters; θm is an M-dimensional vector of parameters, where K and M are the optimal lag lengths for the first-differenced covariates determined empirically using the F test. Because the dependent variable in (2) is in first-differenced form, the intercept αi measures market-specific deterministic time trends. The coefficient λ has to be negative for per capita cigarette sales to converge to its long-run equilibrium level. The magnitude of λ is the speed of the adjustment of the system toward its long-run equilibrium. If |λ| is close to 1, the adjustment will be fast; if |λ| is close to 0, the adjustment process will be slow.The parameters in (2) can be used to calculate the temporal adjustment path of per capita cigarette sales in response to a price shock or a change in the CIAL. The cumulative distributed lag adjustment process Scprice,T for per capita cigarette sales in response to a $1 permanent increase in cigarette price is given by the following:
(3)Scprice,0=β10,Scprice,0=Scprice,0+β11+λ(Scprice,0-b1)+θ1Scprice,0,Scprice,2=Scprice,1+β12+λ(Scprice,1-b1)+θ1(Scprice,1-Scprice,0)+θ2Scprice,0⋮Scprice,n=Scprice,n-1+β1n+λ(Scprice,n-1-b1)+∑m=1Mθm(Scprice,n-m-Scprice,n-m-1),
where β1j (j=1,…,n) is the jth element of the parameter vector β2, β2j=0 for all j>K, and n measures the number of periods past since the initial cigarette price shock.Similarly, the cumulative distributed lag adjustment processSsprice,T for per capita cigarette sales in response to a $1 permanent increase in smokeless tobacco price is given by the following:
(4)Ssprice,0=β20,Ssprice,1=Ssprice,0+β21+λ(Ssprice,0-b2)+θ1Ssprice,0,Ssprice,2=Ssprice,1+β22+λ(Ssprice,1-b2)+θ1(Ssprice,1-Ssprice,0)⋮Ssprice,n=Ssprice,n-1+β2n+λ(Ssprice,n-1-b2)+∑m=1Mθm(Ssprice,n-m-Ssprice,n-m-1),
where β2j (j=1,…,n) is the jth element of the parameter vector β2, and β2j=0 for all j>K.Finally, the cumulative distributed lag effect of a 1% permanent increase in the percentage of the population covered by CIALs is as follows:(5)Scial,0=0.01β40,Scial,1=0.01⌊Scial,0+β41+λ(Scial,0-b4)+θ1Scial,0⌋,Scial,2=0.01⌊Scial,1+β42+λ(Scial,1-b4)+θ1(Scial,1-Scial,0)+θ2Scial,0⌋,⋮Scial,n=0.01[∑m=1MθmScial,n-1+β4n+λ(Scial,n-1-b4)Scial=0.01+∑m=1Mθm(Scial,n-m-Scial,n-m-1)],
where β4j (j=1,…,n) is the jth element of the parameter vector β4, and β4j=0 for all j>K.


## 2.2. Tests for Nonstationarity

The unit root test evaluates whether a time series variable is nonstationary and is important in time-series data like smoking policies where public sentiment toward smoking is evolving over time. If the dependent variable and some or all of the covariates in (1) are nonstationary, then statistical inference of parameter estimates in (1) using ordinary least squares (OLS) might be problematic, but simple correction may be applied to the standard OLS if the dependent variable is cointegrated with the covariates [19]. We tested for unit roots in cigarette price (CPrice), smokeless tobacco price (SPrice), income, and clean indoor air laws (CIALs).The panel version of the augmented Dickey-Fuller (ADF) test proposed by Im et al. [20] estimates regression for each potentially nonstationary variable for each cross-section. The IPS panel unit root statistic is then a standardized t-bar test statistic based on the ADF test statistics averaged across the cross-sections. The null hypothesis of the IPS test is that each series contains a unit root. Another test proposed by Pesaran, 2003, showed that panel unit root tests assuming cross-sectional independence may be biased toward concluding unit roots when there is substantial cross-sectional dependence. He devised a cross-sectional augmented Dickey-Fuller (CADF) regression to remove the cross-sectional correlation. The IPS and CADF tests were applied to the per capita unit sales, prices, income, and clean indoor air variables. The test statistics for the first-differenced series indicated that the null of nonstationarity is rejected for most series and show the evidence that C, CPrice, SPrice, Income, and CIAL are integrated of degree one.


## 3. Results and Discussion

### 3.1. Parameters Estimates for Cigarette Sales

We estimated equation (1) using the panel fully modified ordinary least squares (FMOLS) estimator for homogenous panels [19, 21, 22]. When unit roots are present in the stochastic variables, the OLS estimator is asymptotically biased [21]. The FMOLS estimator is a valid estimator for nonstationary panels. The estimator makes two nonparametric adjustments to the OLS estimator. It corrects for serial correlation in the regression residual of (1) and then it also corrects for the endogeneity of the stochastic covariates with the regression residual from (1). Endogeneity arises as a result of the dynamic feedback effects between the nonstationary-dependent variable and the nonstationary covariates over time.The FMOLS and OLS estimates for (1) using the five different measures of population covered by CIALs are presented in Table 2. Most of the estimated coefficients have the expected signs and are statistically significant (few exceptions: indicated by the symbol Ψ). The coefficients on cigarette price and smokeless tobacco price (price elasticity and cross-price elasticity) indicate that cigarettes and smokeless tobacco prices have negative effects on each others sales. Similarly, coefficients on income suggest that growth in income has negative effect in volume of cigarette sales.Table 2
Panel fully modified ordinary least squares (FMLOS) estimates and the long-run elasticities.
(a)
Covariates
Model 1
Model 2
Model 3
FMOLS coef.
t-stat
OLS coef.
t-stat
FMOLS coef.
t-stat
OLS coef.
t-stat
FMOLS coef.
t-stat
OLS  coef.
t-stat
Cigarette price (real)
−0.333
13.913
−0.309
10.498
−0.325
13.440
−0.299
10.152
−0.323
13.469
−0.296
10.009
Smokeless tobacco price (real)
0.076
2.881
0.063
2.384
0.070
2.658
0.057
2.168
0.060
2.283
0.046
1.745 Ψ
Per capita income (real)
−0.00004
4.285
−0.00004
4.590
−0.00004
4.634
−0.00005
5.044
−0.00005
5.288
−0.00005
5.861
Workplace CIAL: percentage of population
−0.239
4.702
−0.292
6.220
—
—
—
—
Restaurant CIAL: Percentage of population
—
—
−0.159
3.667
–0.171
4.426
—
—
Bar CIAL: percentage of population
—
—
—
—
0.080
1.560 (NS)
0.097
2.277
Spring
0.083
3.505
0.073
5.387
0.083
3.466
0.073
5.347
0.083
3.471
0.074
5.392
Summer
0.167
6.906
0.152
10.909
0.168
6.919
0.150
10.789
0.168
6.893
0.150
10.740
Fall
0.073
3.019
0.054
3.893
0.072
2.978
0.053
3.820
0.073
3.002
0.054
3.847
Elasticity
Mean
Mean
Mean
Own-price elasticity
−0.750
−0.731
−0.727
Cross-price elasticity
0.185
0.171
0.148
Income elasticity
−0.895
−0.969
−1.116
CIAL elasticity
−0.023
−0.029
0.012
Stationarity tests of FMOLS residuals
t-bar
P value
t-bar
P value
t-bar
P value
IPS
−2.26
0
−2.226
0
−2.226
0
CADF
−2.211
0
−2.171
0.001
−2.231
0
(b)
Covariates
Model 4
Model 5
FMOLS coef.
t-stat
OLS coef.
t-stat
FMOLS coef.
t-stat
OLS coef.
t-stat
Cigarette price (real)
−0.324
13.389
−0.298
10.140
−0.324
13.493
−0.292
9.895
Smokeless tobacco price (real)
0.068
2.594
0.056
2.120
0.067
2.524
0.052
1.95 Ψ
Per capita income (real)
−0.00004
4.704
−0.00005
5.095
−0.00005
5.010
−0.00005
5.596
Any CIAL: percentage of population
−0.160
3.559
−0.183
4.496
—
—
All CIAL: percentage of population
—
—
−0.092
1.33 Ψ
−0.113
1.89 (NS)
Spring
0.082
3.458
0.073
5.345
0.083
3.480
0.074
5.374
Summer
0.168
6.922
0.150
10.793
0.166
6.790
0.151
10.757
Fall
0.072
2.973
0.053
3.827
0.073
2.979
0.054
3.856
Price, income, and CIAL elasticities
Mean
Mean
Own-price elasticity
−0.728
−0.729
Cross-price elasticity with smokeless tobacco
0.167
0.163
Income elasticity
−0.984
−1.052
CIAL elasticity
−0.030
−0.004
Stationarity tests of the FMOLS residuals
t-bar
P value
t-bar
P-value
IPS
−2.255
0
−2.221
0
CADF
−2.185
0.001
−2.161
0.002
Note: all models include market-specific intercepts and time trends and year-specific intercepts.IPS denotes the Im et al.[20] test for nonstationarity; CADF denotes Pesaran’s [40] cross-sectionally augmented Dickey-Fuller (CADF) test.
The symbol “Ψ” next to thet-statistics indicates that the coefficient is not statistically significant at 5% sig.level.IPS and CADF test results suggest that the residuals are stationary in all five models (Table3). This finding may be interpreted as evidence that there is a cointegration relationship between per capita cigarette sales and the stochastic covariates including cigarette and smokeless tobacco prices, per capita personal income, and the percentage of the population covered by CIALs. Because of this cointegration, the parameter estimates in Table 2 measure the long-run relationship between per capita sales and its covariates.Table 3
Error-correction model estimates.
Covariates
Model 1
Model 2
Model 3
Model 4
Model 5
Coef.
P
>
|
t
|
Coef.
P
>
|
t
|
Coef.
P
>
|
t
|
Coef.
P
>
|
t
|
Coef.
P
>
|
t
|
Cigarette price (real)
−0.379
0.000
−0.376
0.000
−0.374
0.000
–0.376
0.000
−0.375
0.000
Smokeless tobacco price (real)
0.014
0.545
0.014
0.540
0.013
0.570
0.014
0.547
0.013
0.564
Per capita income (real)
−0.0000536
0.001
−0.000055
0.000
−0.0000578
0.000
−0.0000551
0.000
−0.0000566
0.000
Workplace CIAL: percentage of population
−0.062
0.209
—
—
—
—
Restaurant CIAL: percentage of population
—
−0.02189
0.603
—
—
—
Bar CIAL: percentage of population
—
—
0.010
0.848
—
—
Any CIAL: percentage of population
—
—
—
−0.031
0.481
—
All CIAL: percentage of population
—
—
—
—
0.011
0.874
Error correction (1st lag)
−0.213
0.000
−0.21341
0.000
−0.213
0.000
−0.213
0.000
−0.212
0.000
Per capita cigarette units (1st lag)
0.095
0.000
0.095129
0.000
0.095
0.000
0.095
0.000
0.095
0.000
Per capita cigarette units (2nd lag)
−0.157
0.000
−0.15729
0.000
−0.158
0.000
−0.157
0.000
−0.158
0.000
Spring
0.074
0.000
0.0741
0.000
0.074
0.000
0.074
0.000
0.074
0.000
Summer
0.134
0.000
0.134347
0.000
0.134
0.000
0.134
0.000
0.134
0.000
Fall
0.039
0.000
0.038818
0.000
0.039
0.000
0.039
0.000
0.039
0.000
Note: The fully modified ordinary least squares (FMOLS) regression residual of (1) is used as the error correction term in (2).
CIAL: clean indoor air law.The estimated own-price elasticity of cigarette sales ranged from −0.75 to −0.73 (Table2). The estimated cross-price elasticity of cigarette sales with respect to smokeless tobacco price is between 0.185 and 0.148. It suggests that a 10% increase in smokeless tobacco price would increase cigarette sales by between 1.85% and 1.48%. The effect of CIALs in bars is not statistically significant (t-value = 1.560). Of the four CIAL variables that have the expected negative effect on cigarette sales, three are highly statistically significant: percentage of population covered by workplace CIALs (t-value = 4.702), by restaurant CIALs (t-value = 3.667), and by any CIAL (t-value = 3.559). Although the estimated effect of the percentage of the population covered by all CIALs is negative, it is not statistically significant (t-value = 1.33). Workplace smoking bans are estimated to have the largest effect on cigarette sales. The coefficient estimate of −0.239 for workplace CIALs indicates that a 10 percent increase in the percentage of the population covered by workplace CIALs would reduce per capita supermarket cigarette sales by 2.39 packs in the long run. The mean estimated elasticity for workplace CIALs is −0.023, meaning that a 10% increase in the workplace CIAL variable would reduce per capita cigarette sales in supermarkets by 0.23 percent. The CIAL variables, bounded between 0 and 1, measure the percentage of the population covered by CIALs. Therefore, a one percent increase in a CIAL variable is usually less than a one percent increase in the percentage of the population covered by a CIAL. The OLS results indicate similar findings as those of the FMOLS, although the FMOLS estimated coefficient on the bar CIAL is positive but statistically not significant; the OLS counterpart estimate is positive and statistically significant (Table 2). This finding suggests that the FMOLS is preferred to the OLS estimator.


### 3.2. Error Correction Model Estimation

Parameter estimates for the error correction model (2) are presented in Table 3. The estimated coefficient on cigarette price (own-price) is statistically significant, but the coefficients on smokeless tobacco price and CIAL variables are not statistically significant. The results suggest that changes in smokeless tobacco price and percentage of the population covered by CIALs do not have an immediate effect on supermarket sales.TheF test results indicated that including concurrent cigarette and smokeless tobacco prices, per capita personal income, CIAL variables, and lagged per capita cigarette sales up to two quarters in, (2) were sufficient in terms of model fit. Table 4 and Figure 1 (graph of model 1 only is presented for illustration purpose) present the quarterly cumulative distributed lag adjustment path for per capita cigarette sales, calculated using the FMOLS estimates in response to a permanent unit increase in cigarette price, smokeless tobacco price, and a one percent increase in the percentage of the population covered by CIALs. The adjustment path under each price or CIAL shock scenario is similar across the five models. For model 1, the short-run cumulative distributed lag response of cigarette sales to a one-time, $1 increase in cigarette price oscillates between 97% and 122% of its long-run response. The full effect of a cigarette price change is realized about a year (four quarters) after the initial price shock. Cigarette sales adjust more slowly to changes in smokeless tobacco price and adoption of CIALs (Table 4 and Figure 1). It takes up to 15 quarters after the initial shock for sales to reach 97 percent of the long-run equilibrium level.Table 4
Distributed lag adjustment path of per capita cigarette sales.
(a)
Quarters after the shock
Model 1
Model 2
Own-price
Cross-price
CIAL
Own-price
Cross-price
CIAL
Change
%
Change
%
Change
%
Change
%
Change
%
Change
%
0
−0.379
114
0.014
18
−0.00062
26
−0.376
116
0.014
20
−0.00022
14
1
−0.405
122
0.028
37
−0.00106
44
−0.401
123
0.027
39
−0.00053
34
2
−0.333
100
0.037
50
−0.00129
54
−0.328
101
0.035
50
−0.00075
47
3
−0.322
97
0.044
59
−0.00148
62
−0.316
97
0.041
59
−0.00090
57
4
−0.335
100
0.050
66
−0.00165
69
−0.328
101
0.047
67
−0.00103
65
5
0.055
73
−0.00180
75
0.051
73
−0.00114
72
6
0.059
78
−0.00191
80
0.055
78
−0.00122
77
7
0.062
82
−0.00200
84
0.058
82
−0.00129
81
8
0.065
86
−0.00208
87
0.060
86
−0.00135
85
9
0.067
88
−0.00214
89
0.062
89
−0.00139
88
10
0.068
91
−0.00219
91
0.064
91
−0.00143
90
11
0.070
92
−0.00222
93
0.065
92
−0.00146
92
12
0.071
94
−0.00226
94
0.066
94
−0.00148
94
13
0.072
95
−0.00228
95
0.067
95
−0.00150
95
14
0.073
96
−0.00230
96
0.067
96
−0.00152
96
15
0.073
97
−0.00232
97
0.068
97
−0.00153
97
(b)
Quarters after the initial shock
Model 3
Model 4
Own-price
Cross-price
CIAL±
Own-price
Cross-price
CIAL
Change
%
Change
%
Change
%
Change
%
Change
%
Change
%
0
−0.374
116
0.013
21
—
—
−0.376
116
0.014
20
−0.00031
19
1
−0.398
123
0.024
40
—
—
−0.400
124
0.026
39
−0.00061
38
2
−0.326
101
0.031
51
—
—
−0.327
101
0.035
51
−0.00081
50
3
−0.314
97
0.036
60
—
—
−0.316
98
0.040
59
−0.00095
59
4
−0.327
101
0.041
67
—
—
−0.328
101
0.046
67
−0.00107
67
5
0.044
74
—
—
0.050
73
−0.00117
73
6
0.048
79
—
—
0.054
78
−0.00126
78
7
0.050
83
—
—
0.056
82
−0.00132
82
8
0.052
86
—
—
0.059
86
−0.00138
86
9
0.054
89
—
—
0.060
88
−0.00142
88
10
0.055
91
—
—
0.062
91
−0.00145
91
11
0.056
92
—
—
0.063
92
−0.00148
92
12
0.057
94
—
—
0.064
94
−0.00151
94
13
0.057
95
—
—
0.065
95
−0.00152
95
14
0.058
96
—
—
0.066
96
−0.00154
96
15
0.059
97
—
—
0.066
97
−0.00155
97
±Long-run effect of smoke-free laws on cigarette sales is not statistically significant in this model.Therefore, the adjustment path is not calculated.Percent change is the size of the response in a quarter as a percentage of the long-run response.Figure 1
Distributed lag adjustment path of per capita cigarette sales Note: chart shows the adjustment path on per capita cigarette sales after the initial shock (model one in Table4). Models two and four also exhibit similar paths of adjustment after the shock.The estimated own-price elasticity (−0.75 to −0.73) of cigarette sales is higher than the range of overall cigarette demand elasticity between −0.3 and −0.5 normally found in the literature. Supermarket cigarette sales account for only a small share of total cigarettes sold in the market. It may be possible that smokers who purchase cigarettes at supermarkets are more price sensitive than the smokers who buy cigarettes from other outlets where buyers may utilize discount coupons. Average supermarket cigarette sales have a lower price per pack than single-pack sales, and supermarket cigarette shoppers are more sensitive to price change. Stockpiling of cigarettes is not a money-saving strategy for smokers under the changes on smokeless tobacco price and CIAL, but it could be money-saving strategy in the case of cigarette price changes.These results suggest that increasing cigarette prices by raising taxes will lower cigarette sales in supermarkets. A similar relationship exists between smokeless tobacco prices and smokeless tobacco sales volume. However, because cigarettes and smokeless tobacco are imperfect substitutes, price increase in either one will also have the effect of increasing sales in the other. The adoption of 100 percent smoke-free laws in workplace and restaurants should be promoted as another tool to lower cigarette sales.Prior studies have examined the individual effects of tobacco control interventions including taxes and smoke-free air laws on cigarette sales. This study used scanner data and an error-correction method to estimate how quickly cigarettes sales adjust to own-price changes, smokeless tobacco product prices, and the adoption of smoke-free air laws. We found that increasing cigarette prices (own-price) lowers cigarette demand (sales) much faster than the adjustment of cigarette sales due to the changes in the price of smokeless tobacco products (cross-price) or the adoption of smoke-free laws (CIALs).The effects of tobacco control interventions such as increasing excise taxes on cigarettes and other tobacco products and implementation of smoke-free air laws are well documented. Studies show that increasing the price of cigarettes through excise taxes is the most effective intervention to reduce cigarette consumption [14, 23–30]. Hopkins et al. in 2001 reviewed the evidence on tobacco use interventions among youth and adults and concluded that increases in the price of tobacco products reduce the overall prevalence and the amount consumed [31]. Keller et al. [32] conducted an empirical analysis of local cigarette pricing and taxation and found that when state taxes are passed, the price of tobacco products increases. The price elasticity of demand, which measures the magnitude of change in consumption as a result of a change in the price of cigarettes, has been estimated at −0.4, where a 10 percent price increase lowers consumption of cigarettes by 4 percent [33]. There is also sufficient evidence to show that implementing smoke-free air laws can reduce the onset of established smoking, encourage adult smokers to quit or reduce daily cigarette consumption, and reduce exposure to secondhand smoke among nonsmokers [34–38].


#### 3.2.1. Limitations

This study has a few limitations. First, the sample is not nationally representative, as the sample was obtained from supermarkets and food stores with a full-service pharmacy, but convenient stores, gas stations, and mass merchandisers such as Wal-Mart were excluded. Second, data on cigarette prices and cigarette sales for US markets were obtained from metropolitan areas only. Data are more likely to represent the urban smokers since the proportion of current smokers is generally higher in urban communities compared to rural areas [39]. Third, cigarette sales data were not observed but were projected using the sales data from the stores selected to estimate sales for each stratum. Fourth, CIALs data were limited to jurisdictions that have passed 100 percent smoke-free laws and we did not include the jurisdictions where partial smoking bans were in place. Also, although we had information on jurisdictions with smoke-free air laws, information on enforcement of such laws was not available. Despite these limitations, the strength of this analysis is in the use of retail scanner data and error-correction method, which has not been used before.


## 3.1. Parameters Estimates for Cigarette Sales

We estimated equation (1) using the panel fully modified ordinary least squares (FMOLS) estimator for homogenous panels [19, 21, 22]. When unit roots are present in the stochastic variables, the OLS estimator is asymptotically biased [21]. The FMOLS estimator is a valid estimator for nonstationary panels. The estimator makes two nonparametric adjustments to the OLS estimator. It corrects for serial correlation in the regression residual of (1) and then it also corrects for the endogeneity of the stochastic covariates with the regression residual from (1). Endogeneity arises as a result of the dynamic feedback effects between the nonstationary-dependent variable and the nonstationary covariates over time.The FMOLS and OLS estimates for (1) using the five different measures of population covered by CIALs are presented in Table 2. Most of the estimated coefficients have the expected signs and are statistically significant (few exceptions: indicated by the symbol Ψ). The coefficients on cigarette price and smokeless tobacco price (price elasticity and cross-price elasticity) indicate that cigarettes and smokeless tobacco prices have negative effects on each others sales. Similarly, coefficients on income suggest that growth in income has negative effect in volume of cigarette sales.Table 2
Panel fully modified ordinary least squares (FMLOS) estimates and the long-run elasticities.
(a)
Covariates
Model 1
Model 2
Model 3
FMOLS coef.
t-stat
OLS coef.
t-stat
FMOLS coef.
t-stat
OLS coef.
t-stat
FMOLS coef.
t-stat
OLS  coef.
t-stat
Cigarette price (real)
−0.333
13.913
−0.309
10.498
−0.325
13.440
−0.299
10.152
−0.323
13.469
−0.296
10.009
Smokeless tobacco price (real)
0.076
2.881
0.063
2.384
0.070
2.658
0.057
2.168
0.060
2.283
0.046
1.745 Ψ
Per capita income (real)
−0.00004
4.285
−0.00004
4.590
−0.00004
4.634
−0.00005
5.044
−0.00005
5.288
−0.00005
5.861
Workplace CIAL: percentage of population
−0.239
4.702
−0.292
6.220
—
—
—
—
Restaurant CIAL: Percentage of population
—
—
−0.159
3.667
–0.171
4.426
—
—
Bar CIAL: percentage of population
—
—
—
—
0.080
1.560 (NS)
0.097
2.277
Spring
0.083
3.505
0.073
5.387
0.083
3.466
0.073
5.347
0.083
3.471
0.074
5.392
Summer
0.167
6.906
0.152
10.909
0.168
6.919
0.150
10.789
0.168
6.893
0.150
10.740
Fall
0.073
3.019
0.054
3.893
0.072
2.978
0.053
3.820
0.073
3.002
0.054
3.847
Elasticity
Mean
Mean
Mean
Own-price elasticity
−0.750
−0.731
−0.727
Cross-price elasticity
0.185
0.171
0.148
Income elasticity
−0.895
−0.969
−1.116
CIAL elasticity
−0.023
−0.029
0.012
Stationarity tests of FMOLS residuals
t-bar
P value
t-bar
P value
t-bar
P value
IPS
−2.26
0
−2.226
0
−2.226
0
CADF
−2.211
0
−2.171
0.001
−2.231
0
(b)
Covariates
Model 4
Model 5
FMOLS coef.
t-stat
OLS coef.
t-stat
FMOLS coef.
t-stat
OLS coef.
t-stat
Cigarette price (real)
−0.324
13.389
−0.298
10.140
−0.324
13.493
−0.292
9.895
Smokeless tobacco price (real)
0.068
2.594
0.056
2.120
0.067
2.524
0.052
1.95 Ψ
Per capita income (real)
−0.00004
4.704
−0.00005
5.095
−0.00005
5.010
−0.00005
5.596
Any CIAL: percentage of population
−0.160
3.559
−0.183
4.496
—
—
All CIAL: percentage of population
—
—
−0.092
1.33 Ψ
−0.113
1.89 (NS)
Spring
0.082
3.458
0.073
5.345
0.083
3.480
0.074
5.374
Summer
0.168
6.922
0.150
10.793
0.166
6.790
0.151
10.757
Fall
0.072
2.973
0.053
3.827
0.073
2.979
0.054
3.856
Price, income, and CIAL elasticities
Mean
Mean
Own-price elasticity
−0.728
−0.729
Cross-price elasticity with smokeless tobacco
0.167
0.163
Income elasticity
−0.984
−1.052
CIAL elasticity
−0.030
−0.004
Stationarity tests of the FMOLS residuals
t-bar
P value
t-bar
P-value
IPS
−2.255
0
−2.221
0
CADF
−2.185
0.001
−2.161
0.002
Note: all models include market-specific intercepts and time trends and year-specific intercepts.IPS denotes the Im et al.[20] test for nonstationarity; CADF denotes Pesaran’s [40] cross-sectionally augmented Dickey-Fuller (CADF) test.
The symbol “Ψ” next to thet-statistics indicates that the coefficient is not statistically significant at 5% sig.level.IPS and CADF test results suggest that the residuals are stationary in all five models (Table3). This finding may be interpreted as evidence that there is a cointegration relationship between per capita cigarette sales and the stochastic covariates including cigarette and smokeless tobacco prices, per capita personal income, and the percentage of the population covered by CIALs. Because of this cointegration, the parameter estimates in Table 2 measure the long-run relationship between per capita sales and its covariates.Table 3
Error-correction model estimates.
Covariates
Model 1
Model 2
Model 3
Model 4
Model 5
Coef.
P
>
|
t
|
Coef.
P
>
|
t
|
Coef.
P
>
|
t
|
Coef.
P
>
|
t
|
Coef.
P
>
|
t
|
Cigarette price (real)
−0.379
0.000
−0.376
0.000
−0.374
0.000
–0.376
0.000
−0.375
0.000
Smokeless tobacco price (real)
0.014
0.545
0.014
0.540
0.013
0.570
0.014
0.547
0.013
0.564
Per capita income (real)
−0.0000536
0.001
−0.000055
0.000
−0.0000578
0.000
−0.0000551
0.000
−0.0000566
0.000
Workplace CIAL: percentage of population
−0.062
0.209
—
—
—
—
Restaurant CIAL: percentage of population
—
−0.02189
0.603
—
—
—
Bar CIAL: percentage of population
—
—
0.010
0.848
—
—
Any CIAL: percentage of population
—
—
—
−0.031
0.481
—
All CIAL: percentage of population
—
—
—
—
0.011
0.874
Error correction (1st lag)
−0.213
0.000
−0.21341
0.000
−0.213
0.000
−0.213
0.000
−0.212
0.000
Per capita cigarette units (1st lag)
0.095
0.000
0.095129
0.000
0.095
0.000
0.095
0.000
0.095
0.000
Per capita cigarette units (2nd lag)
−0.157
0.000
−0.15729
0.000
−0.158
0.000
−0.157
0.000
−0.158
0.000
Spring
0.074
0.000
0.0741
0.000
0.074
0.000
0.074
0.000
0.074
0.000
Summer
0.134
0.000
0.134347
0.000
0.134
0.000
0.134
0.000
0.134
0.000
Fall
0.039
0.000
0.038818
0.000
0.039
0.000
0.039
0.000
0.039
0.000
Note: The fully modified ordinary least squares (FMOLS) regression residual of (1) is used as the error correction term in (2).
CIAL: clean indoor air law.The estimated own-price elasticity of cigarette sales ranged from −0.75 to −0.73 (Table2). The estimated cross-price elasticity of cigarette sales with respect to smokeless tobacco price is between 0.185 and 0.148. It suggests that a 10% increase in smokeless tobacco price would increase cigarette sales by between 1.85% and 1.48%. The effect of CIALs in bars is not statistically significant (t-value = 1.560). Of the four CIAL variables that have the expected negative effect on cigarette sales, three are highly statistically significant: percentage of population covered by workplace CIALs (t-value = 4.702), by restaurant CIALs (t-value = 3.667), and by any CIAL (t-value = 3.559). Although the estimated effect of the percentage of the population covered by all CIALs is negative, it is not statistically significant (t-value = 1.33). Workplace smoking bans are estimated to have the largest effect on cigarette sales. The coefficient estimate of −0.239 for workplace CIALs indicates that a 10 percent increase in the percentage of the population covered by workplace CIALs would reduce per capita supermarket cigarette sales by 2.39 packs in the long run. The mean estimated elasticity for workplace CIALs is −0.023, meaning that a 10% increase in the workplace CIAL variable would reduce per capita cigarette sales in supermarkets by 0.23 percent. The CIAL variables, bounded between 0 and 1, measure the percentage of the population covered by CIALs. Therefore, a one percent increase in a CIAL variable is usually less than a one percent increase in the percentage of the population covered by a CIAL. The OLS results indicate similar findings as those of the FMOLS, although the FMOLS estimated coefficient on the bar CIAL is positive but statistically not significant; the OLS counterpart estimate is positive and statistically significant (Table 2). This finding suggests that the FMOLS is preferred to the OLS estimator.


## 3.2. Error Correction Model Estimation

Parameter estimates for the error correction model (2) are presented in Table 3. The estimated coefficient on cigarette price (own-price) is statistically significant, but the coefficients on smokeless tobacco price and CIAL variables are not statistically significant. The results suggest that changes in smokeless tobacco price and percentage of the population covered by CIALs do not have an immediate effect on supermarket sales.TheF test results indicated that including concurrent cigarette and smokeless tobacco prices, per capita personal income, CIAL variables, and lagged per capita cigarette sales up to two quarters in, (2) were sufficient in terms of model fit. Table 4 and Figure 1 (graph of model 1 only is presented for illustration purpose) present the quarterly cumulative distributed lag adjustment path for per capita cigarette sales, calculated using the FMOLS estimates in response to a permanent unit increase in cigarette price, smokeless tobacco price, and a one percent increase in the percentage of the population covered by CIALs. The adjustment path under each price or CIAL shock scenario is similar across the five models. For model 1, the short-run cumulative distributed lag response of cigarette sales to a one-time, $1 increase in cigarette price oscillates between 97% and 122% of its long-run response. The full effect of a cigarette price change is realized about a year (four quarters) after the initial price shock. Cigarette sales adjust more slowly to changes in smokeless tobacco price and adoption of CIALs (Table 4 and Figure 1). It takes up to 15 quarters after the initial shock for sales to reach 97 percent of the long-run equilibrium level.Table 4
Distributed lag adjustment path of per capita cigarette sales.
(a)
Quarters after the shock
Model 1
Model 2
Own-price
Cross-price
CIAL
Own-price
Cross-price
CIAL
Change
%
Change
%
Change
%
Change
%
Change
%
Change
%
0
−0.379
114
0.014
18
−0.00062
26
−0.376
116
0.014
20
−0.00022
14
1
−0.405
122
0.028
37
−0.00106
44
−0.401
123
0.027
39
−0.00053
34
2
−0.333
100
0.037
50
−0.00129
54
−0.328
101
0.035
50
−0.00075
47
3
−0.322
97
0.044
59
−0.00148
62
−0.316
97
0.041
59
−0.00090
57
4
−0.335
100
0.050
66
−0.00165
69
−0.328
101
0.047
67
−0.00103
65
5
0.055
73
−0.00180
75
0.051
73
−0.00114
72
6
0.059
78
−0.00191
80
0.055
78
−0.00122
77
7
0.062
82
−0.00200
84
0.058
82
−0.00129
81
8
0.065
86
−0.00208
87
0.060
86
−0.00135
85
9
0.067
88
−0.00214
89
0.062
89
−0.00139
88
10
0.068
91
−0.00219
91
0.064
91
−0.00143
90
11
0.070
92
−0.00222
93
0.065
92
−0.00146
92
12
0.071
94
−0.00226
94
0.066
94
−0.00148
94
13
0.072
95
−0.00228
95
0.067
95
−0.00150
95
14
0.073
96
−0.00230
96
0.067
96
−0.00152
96
15
0.073
97
−0.00232
97
0.068
97
−0.00153
97
(b)
Quarters after the initial shock
Model 3
Model 4
Own-price
Cross-price
CIAL±
Own-price
Cross-price
CIAL
Change
%
Change
%
Change
%
Change
%
Change
%
Change
%
0
−0.374
116
0.013
21
—
—
−0.376
116
0.014
20
−0.00031
19
1
−0.398
123
0.024
40
—
—
−0.400
124
0.026
39
−0.00061
38
2
−0.326
101
0.031
51
—
—
−0.327
101
0.035
51
−0.00081
50
3
−0.314
97
0.036
60
—
—
−0.316
98
0.040
59
−0.00095
59
4
−0.327
101
0.041
67
—
—
−0.328
101
0.046
67
−0.00107
67
5
0.044
74
—
—
0.050
73
−0.00117
73
6
0.048
79
—
—
0.054
78
−0.00126
78
7
0.050
83
—
—
0.056
82
−0.00132
82
8
0.052
86
—
—
0.059
86
−0.00138
86
9
0.054
89
—
—
0.060
88
−0.00142
88
10
0.055
91
—
—
0.062
91
−0.00145
91
11
0.056
92
—
—
0.063
92
−0.00148
92
12
0.057
94
—
—
0.064
94
−0.00151
94
13
0.057
95
—
—
0.065
95
−0.00152
95
14
0.058
96
—
—
0.066
96
−0.00154
96
15
0.059
97
—
—
0.066
97
−0.00155
97
±Long-run effect of smoke-free laws on cigarette sales is not statistically significant in this model.Therefore, the adjustment path is not calculated.Percent change is the size of the response in a quarter as a percentage of the long-run response.Figure 1
Distributed lag adjustment path of per capita cigarette sales Note: chart shows the adjustment path on per capita cigarette sales after the initial shock (model one in Table4). Models two and four also exhibit similar paths of adjustment after the shock.The estimated own-price elasticity (−0.75 to −0.73) of cigarette sales is higher than the range of overall cigarette demand elasticity between −0.3 and −0.5 normally found in the literature. Supermarket cigarette sales account for only a small share of total cigarettes sold in the market. It may be possible that smokers who purchase cigarettes at supermarkets are more price sensitive than the smokers who buy cigarettes from other outlets where buyers may utilize discount coupons. Average supermarket cigarette sales have a lower price per pack than single-pack sales, and supermarket cigarette shoppers are more sensitive to price change. Stockpiling of cigarettes is not a money-saving strategy for smokers under the changes on smokeless tobacco price and CIAL, but it could be money-saving strategy in the case of cigarette price changes.These results suggest that increasing cigarette prices by raising taxes will lower cigarette sales in supermarkets. A similar relationship exists between smokeless tobacco prices and smokeless tobacco sales volume. However, because cigarettes and smokeless tobacco are imperfect substitutes, price increase in either one will also have the effect of increasing sales in the other. The adoption of 100 percent smoke-free laws in workplace and restaurants should be promoted as another tool to lower cigarette sales.Prior studies have examined the individual effects of tobacco control interventions including taxes and smoke-free air laws on cigarette sales. This study used scanner data and an error-correction method to estimate how quickly cigarettes sales adjust to own-price changes, smokeless tobacco product prices, and the adoption of smoke-free air laws. We found that increasing cigarette prices (own-price) lowers cigarette demand (sales) much faster than the adjustment of cigarette sales due to the changes in the price of smokeless tobacco products (cross-price) or the adoption of smoke-free laws (CIALs).The effects of tobacco control interventions such as increasing excise taxes on cigarettes and other tobacco products and implementation of smoke-free air laws are well documented. Studies show that increasing the price of cigarettes through excise taxes is the most effective intervention to reduce cigarette consumption [14, 23–30]. Hopkins et al. in 2001 reviewed the evidence on tobacco use interventions among youth and adults and concluded that increases in the price of tobacco products reduce the overall prevalence and the amount consumed [31]. Keller et al. [32] conducted an empirical analysis of local cigarette pricing and taxation and found that when state taxes are passed, the price of tobacco products increases. The price elasticity of demand, which measures the magnitude of change in consumption as a result of a change in the price of cigarettes, has been estimated at −0.4, where a 10 percent price increase lowers consumption of cigarettes by 4 percent [33]. There is also sufficient evidence to show that implementing smoke-free air laws can reduce the onset of established smoking, encourage adult smokers to quit or reduce daily cigarette consumption, and reduce exposure to secondhand smoke among nonsmokers [34–38].


### 3.2.1. Limitations

This study has a few limitations. First, the sample is not nationally representative, as the sample was obtained from supermarkets and food stores with a full-service pharmacy, but convenient stores, gas stations, and mass merchandisers such as Wal-Mart were excluded. Second, data on cigarette prices and cigarette sales for US markets were obtained from metropolitan areas only. Data are more likely to represent the urban smokers since the proportion of current smokers is generally higher in urban communities compared to rural areas [39]. Third, cigarette sales data were not observed but were projected using the sales data from the stores selected to estimate sales for each stratum. Fourth, CIALs data were limited to jurisdictions that have passed 100 percent smoke-free laws and we did not include the jurisdictions where partial smoking bans were in place. Also, although we had information on jurisdictions with smoke-free air laws, information on enforcement of such laws was not available. Despite these limitations, the strength of this analysis is in the use of retail scanner data and error-correction method, which has not been used before.


## 3.2.1. Limitations

This study has a few limitations. First, the sample is not nationally representative, as the sample was obtained from supermarkets and food stores with a full-service pharmacy, but convenient stores, gas stations, and mass merchandisers such as Wal-Mart were excluded. Second, data on cigarette prices and cigarette sales for US markets were obtained from metropolitan areas only. Data are more likely to represent the urban smokers since the proportion of current smokers is generally higher in urban communities compared to rural areas [39]. Third, cigarette sales data were not observed but were projected using the sales data from the stores selected to estimate sales for each stratum. Fourth, CIALs data were limited to jurisdictions that have passed 100 percent smoke-free laws and we did not include the jurisdictions where partial smoking bans were in place. Also, although we had information on jurisdictions with smoke-free air laws, information on enforcement of such laws was not available. Despite these limitations, the strength of this analysis is in the use of retail scanner data and error-correction method, which has not been used before.


## 4. Conclusions

We estimated the impact of changes in cigarette price, smokeless tobacco price, and the adoption of CIALs on cigarette demand by using retail scanner data and applying an error-correction econometric technique. Our study found that per capita cigarette sales, cigarette prices, smokeless tobacco prices in supermarkets, and population coverage of 100 percent smoke-free laws are all nonstationary, and therefore, econometric methodologies such as FMLOS that account for the cointegration of time series variables are necessary for unbiased estimates. Higher per capita income and 100% smoke-free laws, are negatively associated with cigarette demand. The error-correction model suggests that effect of cigarette prices on cigarette sales is much faster than the adoption of 100% smoke-free laws or the change in the smokeless tobacco prices. The effects (the adjustment path) of smokeless tobacco price increases and coverage of smoke-free laws on cigarette sales are similar but opposite.The United States has a track record of effective tobacco control policies [20]. Historically, smoking prevalence in the United States has decreased substantially, but smoking still remains the leading preventable cause of premature death, resulting in an estimated 443,000 deaths annually [2]. The findings of this study indicate that increasing cigarettes prices is an effective policy tool for lowering cigarette demand. The findings also suggest that the price of smokeless tobacco products and the adoption of CIALs also affect the demand for cigarettes, although more slowly than a change in cigarette prices. This indicates that policies designed to increase cigarette prices, such as the imposition of excise taxes, may be more effective when combined with other policies such as changes in smokeless tobacco prices and the adoption of CIALs.



---

*Source: 148702-2012-09-02.xml*