Innovation and Science Policy in the US and Around the World
Faculty Supervisor:
Project and Position Description:
Project and Position Description
Motivation:
Scientific research and technological innovation are critical drivers of long-term economic growth. A fundamental question for economists and policymakers globally is how to foster and sustain scientific discoveries and innovation. This project aims to study innovation and science policy from multiple perspectives, focusing on the efficient allocation of scarce R&D resources—such as funding, talent, and policy tools—across economic sectors and technological fields. Central to our exploration is the empirical characterization of technological evolution and the theoretical and quantitative analysis of how to optimally allocate these resources.
Policy Relevance:
The efficient allocation of R&D resources is of paramount importance to both policymakers and the scientific community. Every year, major scientific funding agencies (e.g., the U.S. National Science Foundation, the National Institutes of Health, Japan Science and Technology Agency) decide how to distribute funding across scientific disciplines. Governments similarly assess the distribution of resources to sectors that promote long-term economic growth, exemplified by the CHIPS for America Act, which aims to strengthen the semiconductor industry in the U.S. The outcomes of this project will directly inform such decisions by providing data-driven insights into the optimal allocation of R&D investments.
Research Plan:
Our research will employ cutting-edge tools in macroeconomics and network theory, complemented by extensive data analysis on industrial R&D activities, scientific grant allocations, and research output. The theoretical component will focus on developing a model for the optimal allocation of R&D resources across scientific disciplines and technological fields.
On the empirical side, we plan to construct comprehensive datasets encompassing millions of research papers (spanning over 200 years) and patent documents (covering 100 years). These datasets will allow us to map the interdependencies between scientific discoveries and technological breakthroughs. To do this, we will leverage archival data, network theory, and advanced AI/ML techniques. Additionally, we will collect and harmonize data on relevant policies, industrial sectors, and scientific grant funding, sourced from government documents, press releases, and other publications.
By combining these theoretical and empirical approaches, we aim to evaluate U.S. innovation and science policies and address critical questions related to the allocation of scientific funding, high-skill immigration policies, and the design of education curricula.
Research Output:
Our findings will provide actionable insights for governments, funding agencies, universities, and other institutions that manage R&D resources. The results will also influence broader policy areas, including education and immigration policies tailored to high-tech fields. Additionally, our research will produce an open-access dataset, enabling further research into R&D allocation and related topics.
Position Description:
The predoctoral fellow will work closely with faculty and the research team on a range of tasks, including data collection and processing, literature reviews, research design, and analysis. The role offers a comprehensive onboarding period that will equip the fellow with the necessary research skills. This is a unique opportunity for those interested in gaining hands-on research experience in economics, finance, and policy studies.
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 and applicants excited to learn new skills
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.