I am a job market candidate in Economics from Princeton University interested in humanitarian work, international development issues, and social change. My thesis focuses on international trade and urban/spatial economics.
In my projects, I combine causal inference methods, structural modeling, and computational analysis to answer policy questions.
I will be available for interviews at the 2020 ASSA Annual Meeting in San Diego.
This paper develops a solution method for computing optimal decisions to combinatorial discrete choice problems (CDCPs) in heterogeneous agent settings. With an arbitrary type distribution over any number of differentiated characteristics, it quickly computes the policy function mapping the entire type space to corresponding optimal actions. The binary decisions can display either supermodular or submodular interactions. Problems of this structure arise naturally in economic settings, especially in international trade and industrial organization. The proposed algorithm is particularly well suited for estimating or computing general equilibrium models incorporating heterogeneous agents solving CDCPs, including choices on plant locations, input sourcing partners, or export market entry. As an illustration of the algorithm in practice, the paper then turns to evaluating the effects of a counterfactual policy equalizing corporate tax rates across the European Union using a quantitative general equilibrium model where heterogeneous firms optimally select a set of countries in which to operate affiliates.
Brazil's tariff cuts and jobs in its nontraded sector