|dc.description.abstract||This dissertation identifies the average treatment effect of state level incentives for hybrid vehicles, identifies individual-level predictors of early adopters, and attempts to understand why states adopt these incentives. These questions are estimated using traditional parametric techniques, logistic regression, difference-in-difference regression, and fixed effects. In particular, this dissertation looks at changes in aggregate demand on two comparison groups: (1) the natural control group, states that did not adopt subsidies, and (2) a constructed control group, states that proposed subsidies during this same time period but did not adopt them. In addition to these parametric models, propensity score matching was used to construct a third comparison group using the models that identified determinants of the policy adoption. These findings were supplemented by exploratory analyses using the individual-level National Household Travel Survey. This multitude of evaluative analyses shows that HOV lane exemptions, if implemented in places with high traffic congestion, were found to impact aggregate demand and an individual's propensity to adopt a hybrid, while traditional incentives had limited impact.
These analyses provide insight into why states adopt certain policies and the circumstances in which these incentives are effective. Since people may be motivated by factors other than economic factors, creating effective incentives for energy efficiency technologies may be more challenging than just offsetting the price differential. Instead, customization to the local community's characteristics could help increase the efficacy of such policies.||en_US