In the first project, I develop a new framework to analyse optimal infrastructure investment in an urban setting. The model features people commuting between residential areas and business districts as well as a choice over the mode of transportation. We plan to evaluate the framework to historical data about specific cities.

In the second project, I propose and implement a new algorithm to compute optimal transport networks in the presence of increasing returns to transport, a likely prominent feature of real-world networks. The algorithm applies a branch-and-bound method in a series of geometric programming relaxations of the problem.

In the third project, I study the dynamic evolution of actual transport networks using satellite data from the US, India and Mexico. In the spirit of Hsieh and Klenow (2007), I use the model to measure distortions in the placement of roads between rich and poor countries.

In the fourth project, I study the inefficiencies and welfare losses associated with political economy frictions among governments and planning agencies. I use the model to identify inefficiencies and relate them to measures of institutions and political outcomes.

In the final project, I propose a new explanation behind the Zipf’s law distribution of city sizes. I show that Zipf’s law may result from particular topological properties of optimal transport networks that allocate resources efficiently in space.