Scrap tire management : tire demand estimation

Abstract

The proper management of scrap tires is relatively resource-intensive. Two features of waste tires produce internal and external costs. Their donut-like shape occupies vast space on transportation vehicles and in general landfills. It also allows the breeding of disease-carrying mosquitoes in populated areas. Their rubber and steel composition makes them durable and at the same time costly to reduce in size. Tire rubber can also generate air and ground pollutants during fires. Mosquito and fire outbreaks are associated with scrap tire stocks of any size. Faced with increasing flows of scrap tires (and other solid and hazardous waste), local officials consider a policy to decrease the waste tire generation rate, i.e., source-reduction. To implement such a policy, a critical first step is to determine, theoretically and empirically, the factors that influence tire demand. Consequently, this thesis applies basic consumer theory to specify important economic determinants of tire demand. Their empirical counterparts form the basis of the explanatory variables in the econometric demand model. Tire sales quantities are derived from state revenue collections of a per unit tire tax on new tire purchases. Measurement errors in the dependent variable and lack of explanatory data motivate the use of the generalized least-squares fixed effects estimator in a pooled tire demand model comprising 28 states. The qualitative results of the econometric estimation are in conformity with economic theory. Quantitatively, the model produces an income elasticity of 0.4; a ten percent increase in real per capita income increases tire sales per vehicle by four percent, ceteris paribus. This effect is very likely to measure increased gasoline consumption, i.e., vehicle utilization. The calculated own price elasticity of tire demand is about -15, which is too large to reflect a pure price effect. Unless statistical problems generate this estimate, it probably captures a movement of and along the tire demand curve.