Essays in Spatial and Urban Economics

This dissertation examines the measurement, economic effects, and optimal design of land use regulations within a city.

In the first part of the dissertation, I examine how to quantify land use regulations. Land use regulations are difficult to measure because they consist of bundles of statutory rules whose effects on housing supply depend on local market conditions. To address this challenge, I construct a wedge-based measure of regulation that is recovered using data on construction costs and property values for Chicago and the surrounding Cook County.

In the second part, I model the economic effects of neighborhood-level regulations using a general equilibrium quantitative spatial model of a city with productivity and amenity externalities. This framework allows me to account for how urban structure and population distribution change with land use regulations. I calibrate the model to Cook County using commuting, income, and transit data.

In the third part, I study the welfare-maximizing configuration of land use regulations within this general-equilibrium framework and compare it to existing regulations. I numerically solve for the optimal allocation of regulations across neighborhoods in Cook County, using automatic differentiation to make this high-dimensional policy problem computationally tractable. I find that the optimal policy delivers substantial welfare gains relative to both current regulations in Chicago and wholesale deregulation. Optimal regulations depend less on local amenities and productivity in isolation than on spatial linkages across locations, measured by residents' access to jobs and firms' access to workers.

Taken together, this dissertation provides an integrated framework for land use policy that quantifies regulations, models their economic effects, and characterizes welfare maximizing policies. The automatic-differentiation methods explored herein also offer a promising approach to a broader class of high-dimensional optimal policy problems.