Matteo Maggiori, The Moghadam Family Professor and Professor of Finance, and Antonio Coppola, Assistant Professor of Finance, are recruiting several pre-doctoral research fellows to be part of the Global Capital Allocation Project (GCAP) Lab. The research fellow positions are based at the Stanford Graduate School of Business. You can find out more about the Lab's work on their website: www.globalcapitalallocation.com.
A substantial component of the work focuses on big-data applications in international macroeconomics and finance. The GCAP Lab mixes data, economic theory, and analytics to understand how capital moves around the world with the aim of improving international economic policy. Current projects include, for example: (i) mapping how global firms finance themselves through foreign subsidiaries, often shell companies in tax havens; (ii) understanding capital allocation and financial integration in the Eurozone; (iii) understanding China’s rising presence in global financial markets; and (iv) understanding how geopolitics and economic statecraft work.
The research fellows will be dedicated to Professors Maggiori and Coppola but will also work closely and directly with all members of the lab, including other lab co-directors such as Jesse Schreger at Columbia Business School, co-authors on academic papers, PhD students, and other research assistants. This is a vibrant community with plenty of interaction and the expectation of working collaboratively in smaller groups and then presenting research progress to the broader group.
Applications will be considered on a rolling basis, and short-listed applicants will be contacted by late October to complete a technical exercise and a remote interview.
Requirements
A bachelor’s degree or its equivalent, a strong quantitative background, excellent computer programming skills, and a serious interest in pursuing research in economics. A background in economics is helpful but not necessary. Previous research experience is a plus. For one of the positions, U.S. citizenship may be prioritized during the selection process due to the nature and requirements of some administrative datasets.
