Rihyeon Kang

Biography

Rihyeon is a PhD candidate in Economics at the University of California, Santa Barbara (UCSB), specializing in labor and public economics. Her primary research interest is in understanding how major institutional and technological changes reshape the interaction between education and labor markets. She examines the dynamic consequences of technological change, quota-based affirmative action, and tax policies, focusing on how they influence individuals’ educational investments, labor market opportunities, and the distribution of economic outcomes. A central theme of her work is analyzing the intended and unintended consequences of these shocks for human capital investment, performance, and skill mismatch.

Prior to joining UCSB, she earned a B.S. in Chemistry from Gwangju Institute of Science and Technology (GIST) and an M.A. in Economics from Seoul National University (SNU).

Rihyeon will be available on the 2025-26 academic job market.

Job Market Paper Title

"Technology Maturity and Human Capital Investment: Dynamic Labor Market Effects
of Robot Adoption"

Abstract

Technological shocks can have markedly different effects on education and labor markets depending on the phase of adoption, yet their dynamic consequences remain insufficiently explored. This paper studies how industrial robot diffusion shaped educational investments and labor outcomes across U.S. cohorts between 2000 and 2017. Using a cohort–state pseudo-panel and shift–share exposure measures with foreign diffusion instruments, I estimate dynamic effects through event study and state-dependent local projection models. Results indicate that early diffusion raised college enrollment and reinforced skilled wage premia, while mature-stage adoption weakened these gains and generated overeducation and declining returns to higher education. Timing along the life cycle matters: adolescent exposure encouraged schooling, but exposure during career entry increased unemployment risk and extended schooling beyond the prime college-going years. Adjustment also varied with local higher-education capacity. These findings highlight the importance of considering both adoption phases and life-cycle timing when assessing the long-run consequences of automation.

JEL Codes: J24, I21, O33, I26, J23