Please join us for the upcoming presentation of the Maryland Longitudinal Data System (MLDS) Center Research Series Virtual Brown Bag. This brown bag is a forum to bring together researchers, policymakers, and practitioners to discuss in-progress research on academic and workforce outcomes.
Title: "Computer Science for All? The Impact of High School Computer Science Courses on College Majors and Earnings"
Date and Time: Friday, Feb. 16, 2024 - Noon-1 p.m. Eastern
Video call link: https://meet.google.com/
More phone numbers: https://tel.meet/xec-
RSVP: Jamese Dixon-Bobbitt via email: jamese.dixon-bobbitt@m
Presentation Abstract:
In response to the growing importance of computing skills in the economy, U.S. states are quickly expanding computer science (CS) course offerings in K-12 schools. Using data from the MLDS, this study provides the first causal evidence on the effects of this expansion by estimating impacts on students' college major choice, degree attainment, and early career earnings. The staggered rollout of CS course offerings across high schools is leveraged to reduce selection effects. Results indicate that taking a CS course increases students' likelihood of declaring a CS major by 10 percentage points (pp) and receiving a CS BA degree by 5 pp. Evidence also suggests that CS course exposure raises students' likelihood of being employed and early career earnings. Importantly, female, Black, and low socioeconomics status students experience larger benefits of taking a CS course for CS BA receipt and earnings outcomes. Given their take-up of CS coursework is much lower compared with their peers, improving the representation of historically underrepresented groups in the CS field requires greater effort to reach these students as policymakers continue to expand CS curricula across K-12 schools.
Presenter: Jing Liu, PhD, MLDS Research Branch and University of Maryland, College Park
About the Presenter
Jing Liu, PhD, is an MLDS Center research branch member and an assistant professor in education policy at the University of Maryland, College Park, and a research affiliate of the IZA Institute of Labor Economics. Named as a National Academy of Education Sciences/Spencer Dissertation Fellow, he earned his PhD in Economics of Education from Stanford University in 2018. His recent research focuses on using language technologies to measure and improve teaching practices across diverse educational settings and informing policymaking on how to prepare students for an AI-driven future. Liu’s research has appeared in leading peer-reviewed outlets across disciplines, including economics, education, public policy, and computer science.