Congestion, agglomeration, and the structure of cities

Congestion pricing has long been held up by economists as a panacea for the problems associated with ever increasing traffic congestion in urban areas. In addition, the concept has gained traction as a viable solution among planners, policymakers, and the general public. While congestion costs in urban areas are significant and clearly represent a negative externality, economists also recognize the advantages of density in the form of positive agglomeration externalities. The long-run equilibrium outcomes in economies with multiple correlated, but offsetting, externalities have yet to be fully explored in the literature. To this end, I develop a spatial equilibrium model of urban structure that includes both congestion costs and agglomeration externalities. I then estimate the structural parameters of the model by using a computational solution algorithm and match the spatial distribution of employment, population, land use, land rents, and commute times in the data. Policy simulations based on the estimates suggest that naive optimal congestion pricing can lead to net negative economic outcomes.