Global Transport Markets: Efficiency and Impact on World Trade

Economics/Mathematics/Computer Science

My research lies in the intersection of industrial organization and international trade. During my year at Radcliffe, I will be working on a project that explores the structure of the (maritime) transportation sector and its impact on world trade. Leveraging a combination of detailed datasets on transportation contracts, as well as satellite data on exact ship locations, we shed light on classic and new questions of economic interest. What is the role of geography (i.e., country locations, natural inheritance of goods) in determining trade costs and flows? How does ship behavior affect the behavior of exporters and the resulting trade flows? How do global shocks, such as a Chinese slow-down or an oil price shock, propagate through the network of countries? What is the impact of trade policies, such as tariffs, or trade wars on the world economy? Is the matching process between exporters and ships efficient; and if not, what mechanisms would be welfare improving? How would centralizing ship platforms (à la Uber) affect world trade? What is the impact of new infrastructure—such as the opening of the Northwest Passage or new ports, or the widening of the Panama Canal—on trade cost and flows? How do ports, as well as competition between ports, affect growth and trade?

To answer these and related questions, we employ detailed spatial data that provide commercial ships’ exact location every 5 minutes, as well as information on whether the ships are loaded. We have used big data like this to understand the role of transportation markets in international trade as well as efficiency in the transport sector; for our existing work see "Geography, Search Frictions and Endogenous Trade Costs". We are also in the process of collecting data on world ports to understand their impact on growth in developing countries.

I am looking for a research partner with good programming skills (Python/R/MATLAB) and a strong background ideally in both economics and math.