Demand, Market and Elasticitys Regression Analysis
Statistically speaking, the fact that the equation caters to 91 percent of the variation in quantity demanded means that the independent variables that have been incorporated in this regression analysis are extremely significant. The T-test ratio indicates that cigarette prices, advertising and both dummy variables, C and D, are statistically significant in regards to the regression. The income and cigar price variable is shown as not having a significant impact upon the annual consumption of cigarettes. This is essentially in line with economics theory because the income elasticity of demand for cigarettes is -0.09. This basically tells us that cigarettes are inferior goods, signifying that consumption for them falls when income rises. However, the fact that the value is close to zero can also lead us to the result that cigarettes can act as normal goods as well. This dual nature is primarily due to the fact that an increase in demand can result in brand switching as well as a case in which people, owing to the harmful effects of cigarettes, can switch to alternatives like nicotine patches.
Resultantly, the cigar price variable is also not statistically significant because cigarettes and cigars are not readily substitutable. This fact is also proven by the price elasticity of demand for cigarettes which is -0.29. The negative value indicates that cigarettes have a relatively inelastic demand. Economic theory also provides evidence of this in the sense that cigarettes do not have any substitutes in terms of products, although internal brand switching does occur because of price differences. Nevertheless, the annual consumption is not significantly affected by price change because smoking is an addiction and the psychological utility that it provides is unprecedented and cannot be catered for in any other way.
The value for the advertising elasticity of demand is pretty low and that signifies the fact that the relationship between cigarette consumption and advertising is not significant in the sense that an increase in advertising will not result in a considerable increase in the demand for cigarettes. Intuitively, this can be explained theoretically in the sense that cigarette advertisements are heavily monitored and are not frequently aired as well. The other factor that we must consider is that the value is an indicator of total industry demand and not individual firm demand. Therefore, the value of one single firms product would be significantly higher.
The regression coefficients for both dummy variables, C and D, are slightly correct in comparison with basic economic theory. After the American Cancer Society published its report linking cancer with smoking, the annual consumption of cigarette smoking did essentially fall because of the reduction in consumption of people who were not in reality addicted or practiced social smoking. However, the fact of the matter is that the data has been taken from the period 1947-1982 and the report was published during 1953. Hence, the period before the publishing of the report has not been properly catered for.
The dummy variable D, on the other hand, caters to a period of two years while the entire regression has been run on the basis of 35 years. Therefore, theoretically speaking, a variable with a data count of 2 years should not have a significant impact upon the entire equation.
Conclusively, it can be seen that most of the regression results can be explained through the application of traditional economic theory. However, the incorporation of the two dummy variables in regards to the impact that they have upon the overall equation, does in fact deviate from basic theoretical assumptions primarily because of data collectionmethodology mistakes.