(734) 615-6990

Applications:
Economics, Education, Learning Analytics, Policy Research
Methodologies:
Causal Inference, Classification, Econometrics, Longitudinal Data Analysis, Machine Learning, Pattern Analysis and Classification
Relevant Projects:

Skills, Majors, and Jobs: Does Higher Education Respond? (National Science and Russell Sage Foundations); College and Beyond II (Mellon Foundation)


Kevin Stange

Associate Professor

Ford School of Public Policy

Prof. Stange’s research uses population administrative education and labor market data to understand, evaluate and improve education, employment, and economic policy. Much of the work involves analyzing millions of course-taking and transcript records for college students, whether they be at a single institution, a handful of institutions, or all institutions in several states. This data is used to richly characterize the experiences of college students and relate these experiences to outcomes such as educational attainment, employment, earnings, and career trajectories. Several projects also involve working with the text contained in the universe of all job ads posted online in the US for the past decade. This data is used to characterize the demand for different skills and education credentials in the US labor market. Classification is a task that is arising frequently in this work: How to classify courses into groups based on their title and content? How to identify students with similar educational experiences based on their course-taking patterns? How to classify job ads as being more appropriate for one type of college major or another? This data science work is often paired with traditional causal inference tools of economics, including quasi-experimental methods.