Professors Biasi and Ma are jointly recruiting one Fellow to work on the following projects:
Higher education plays a key role in the production of human capital by equipping individuals with up-to-date knowledge and skills. What is the distance between the content of higher education and the frontier of knowledge? How does it vary across schools serving students from different backgrounds? How does it relate to students’ economic outcomes, economic growth, and inequality and social mobility?
This project uses a big data approach to characterize the content of higher education courses and to investigate its consequences. To track the content of higher education, we collect and process the text of nearly four million syllabi of courses taught in US institutions over the last three decades. We also collect more than 20 million academic publications (including journal publications, dissertation, etc.), five million US patent filings, and the text of 200 million job postings. We use the documents as benchmarks to compare the content of higher education courses to frontier research, innovation, and the demanded skills on labor market.
We plan to work on two projects:
In the first project (in progress), we develop a new measure: the “education-innovation gap,” defined as the textual similarity between the materials taught in college and university courses and the content of frontier research, patents, and employment postings. Armed with this new measure, we plan to investigate the following facts. First, does the gap vary considerably within field, and how did it change over time within fields? Second, are there significant gap differences across schools serving different populations of students? Third, how does the “education-innovation gap” affect student outcomes such as graduation rates, income, student loan situation, and rates of inter-generational mobility, especially for students in lower-tiered universities? Our preliminary analysis shows that the gap is smallest for Ivy-League and elite schools and for schools enrolling children of affluent parents, and largest for schools with the largest proportion of minority students.
In the second project, we plan to study how the content of higher education affects two key drivers of growth of the economy: innovation and entrepreneurship. We plan to do so along two dimensions. First, we plan to explore the relationship between the content of education in college and the likelihood that students create innovation and startups down the road. For example, we would like to study whether specific concepts or skills or certain types of teaching and evaluation methods matter more than others for innovation and entrepreneurship. Second, we plan to deepen our knowledge on the specific set of curricula which focus on innovation and entrepreneurship. We will characterize the content of these courses and evaluate their actual impact.
Candidates should have quantitative and coding skills, especially experience in general purpose languages like Python and statistical languages like Matlab, R, or Stata.
- 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 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.