A Semiparametric Analysis of Gasoline Demand in the US: Reexamining The Impact of Price

The evaluation of the impact of an increase in gasoline tax on demand relies crucially on the estimate of the price elasticity. This paper presents an extended application of the Partially Linear Additive Model (PLAM) to the analysis of gasoline demand using a panel of US households, focusing mainly on the estimation of the price elasticity. Methodologically, we propose a root-n consistent estimator for the vector of parameters of the linear part of the PLAM that is semi-parametrically efficient. The estimator is also computationally more convenient compared to recently proposed alternative kernel smoothers. Unlike previous semi-parametric studies that use household-level data, we work with vehicle-level data within households that can potentially add richer details to the price variable. Both households and vehicles data are obtained from the Residential Transportation Energy Consumption Survey (RTECS) of 1991 and 1994, conducted by the US Energy Information Administration. As expected, the derived vehicle-based gasoline price has significant dispersion across the country and across grades of gasoline. By using a PLAM specification for gasoline demand, we obtain a measure of gasoline price elasticity that circumvents the implausible price effects reported in earlier studies. In particular, our results show the price elasticity ranges between −0.3, at low prices, to −0.45, at high prices, suggesting that households might respond differently to price changes depending on the level of price. In addition, users of regular gasoline seem to be more sensitive to price changes compared to users of nonregular (premium and mid-range) gasoline.