Abstract

This paper draws on McFadden’s location choice theory and incorporates households’ residential choice decisions as a hierarchical process in a structural travel demand model. The paper argues that such an approach can effectively tackle the problems of self-selection and multicollinearity. Contrary to previous findings, empirical results based on OLS and 3SLS reveal that travel demand is highly elastic to certain smart-growth features, if they are measured at different spatial scales. The results are robust against alternative sequencing of the hierarchical choice process. An analysis of the quantitative impact of a change in the smart-growth and fuel-tax policies reveals significant returns under both policies. Finally, a simulation based on California suggests that smart growth policies substantially reduce household travel demand.