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

Bishwa B Adhikari,Joshua Goetz,Brett Loomis,Jennifer W Kahende,Chen Zhen

doi:10.1155/2012/148702

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.

Highlights

Cigarette smoking is associated with premature death, productivity losses, and a substantial burden to the health-care system in the United States [1, 2]
We estimated the impact of changes in cigarette price, smokeless tobacco price, and the adoption of clean indoor air laws (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 econometric methodologies such as FMLOS that account for the cointegration of time series variables are necessary for unbiased estimates

Summary

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 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]. 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). 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

Data and Methods

Results and Discussion

Conclusions

Disclosure