Innovation and Science Policy in the US and Around the World
POSITION FILLED
FACULTY SUPERVISOR:
PROJECT DESCRIPTION:
This research project is joint with Professor Ernest Liu from Princeton University. The position is sponsored by the Alfred P. Sloan Foundation.
Scientific research and technological innovation are the source of long-run growth. How to foster scientific discoveries and innovation has long been a central question for economists and policymakers around the world. In this project, we plan to study innovation and science policy around the world from several different angles.
At the center of our exploration is to study the question of how to efficiently allocate scarce R&D resources (for example funding, talent, and other policy tools) across economic sectors or technological fields. This question is important, policy-relevant, and at the center of the functioning of the scientific community. For example, every year, scientific funding agencies around the world (e.g., the National Science Foundation, the National Institutes of Health, Japan Science and Technology Agency) determine their allocation of funding across different scientific fields. Governments also constantly evaluate their allocation of funding across different sectors to support those that have long-term impact on economic growth (e.g., the CHIPS for America Act supporting the semiconductor industry in the US).
The research will answer these questions by combining cutting-edge economic theory in macroeconomics and network theory with detailed data analysis on industrial R&D activities and scientific grant allocations and output. Theoretically, we plan to study an economy’s optimal allocation of R&D resources to different scientific disciplines and technological fields using cutting-edge tools in macroeconomics and network economics.
Empirically, we plan to build comprehensive datasets on scientific research and technological breakthroughs, and their inter-dependence, with the help of a wide set of archival data and ML and AI-related techniques. We plan to assess the allocative efficiencies in industrial sectors and scientific grant funding allocations in the U.S. and around the world by collecting and harmonizing data on scientific publications, patent filings, R&D allocations, and industrial productions, among others.
Our answers could have real-world impact on how governments, scientific funding agencies, universities, or other related entities consider the R&D allocation problem and broader issues such as education policy and immigration policy related to specific technological fields.
REQUISITE SKILLS AND QUALIFICATIONS:
Candidates should have quantitative and coding skills, especially experience in general-purpose languages like Python and Julia and statistical languages like Stata or R.
- Candidates are expected to perform large scale textual analysis, so exposure to related methodologies is a big plus.
- Exposure/knowledge of Machine Learning is a plus, but not required. The candidate should be open to learn such skills over the research period.
- The work will primarily use LINUX, so exposure of Linux and Cluster server computing is strongly recommended, but training can be provided if needed
- Preference will be given to detail-oriented applicants
Candidates need not be economics majors, though they should have experience with economics. We welcome applicants from other fields such as, but not limited to, computer science, engineering, mathematics, political science, psychology, and statistics. A love of working with data—cleaning it, understanding it, and presenting it in enlightening ways—is essential for this position.