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Scrappage programs have a dual aim of stimulating the private car industry and removing inefficient vehicles from the road. More specifically the performance of the Irish scrappage scheme will be assessed in this paper by whether the scheme promoted the purchase of cars which produce carbon emissions of 140g/km or less…
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Recall that the questions we are concerned with are (i) whether the scrappage scheme has had a positive impact on the aggregate sales of cars in the domestic market and (ii) whether the introduction of the scrappage scheme has created a substitution effect from G and C band vehicles to A and B brand vehicles.
The hypothesis and the basic setup
In order to empirically examine these questions, the easiest and simplest methodology adoptable is that of using two different OLS regressions for cases (i) and (ii) to test whether the introduction of the scheme led to any significant departures in the time series observations of our dependent variables. As mentioned earlier, our dependent variable for case (i) are the quarterly car sales over the sample period of 2003 to 2010. Incorporating an indicator variable for the time periods which were under the coverage of the scrappage scheme as an independent variable, one can conceive an OLS specification to see if the there was a significant impact of the dummy on the dependent variable. However, in order to obtain precise, valid estimates it is crucial to control for other factors that may have influenced car sales during the sample period. To better understand the reasoning, consider the following regression: (1) represents the dependent variable, denotes the constant, i.e., the intercept, denotes the dummy for the scrappage scheme, represents a vector of controls and is the stochastic disturbance or the error term . can be defined as : And Thus, the coefficient signifies the impact of the scrappage scheme on the dependent variable . If we find is statistically significant from zero, the implication will be that the scrappage scheme had an impact. The sign on the coefficient will indicate the direction of the impact. Therefore, if the coefficient is found to be significant and positive, that will imply that the scrappage scheme led to an increase in the dependent variable . If on the other hand we find the coefficient to be negative, that will imply that the scrappage scheme led to a decline on the dependent variable. The coefficient vector includes the coefficients on the individual variables included as controls. The signs and significances of these coefficients will reveal the direction and importance of the control variables for the determination of the dependent variable. Therefore, for case (i), we can take the time series of car sales as our dependent variable
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Fig. 2.2.1 Foundation Plan
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