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.

This study investigates a unique incentive-based affirmative action program in South Korea’s academic labor market. Specifically, I analyze the effects of a 2018 policy requiring all national and public universities in South Korea to ensure that neither gender constitutes less than 25% of tenure-track or tenured faculty at the university level, with noncompliance resulting in reductions in government administrative and financial support. Using department-level faculty composition data for the entire universe of Korean universities and a difference-in-differences methodology, I find that the implementation of gender quotas is associated with an increased share of female new hires in national and public universities. Furthermore, using web-scraped individual-level data on tenure-track and tenured professors, I explore the differential impact of gender quotas on entry barriers for newly hired professors by gender, focusing on research performance. Analysis of entry barrier-related outcomes is still in progress. 

This paper estimates the joint intention-to-treat (ITT) effects of the Earned Income Tax Credit (EITC) and the Child Tax Credit (CTC) on the monthly labor supply and schooling decisions of children aged 16–24 in potentially eligible families. The results indicate that greater EITC generosity increases the likelihood of school enrollment but also raises the probability that a specific subgroup—16–18-year-old males—are neither in the labor force nor in school during the academic year by 0.8 percentage points (21 percent). While CTC generosity shows little overall effect, the evidence suggests that it may reduce school enrollment among another subgroup—Asian and Pacific Islander children aged 19–24—during the academic year by as much as 24 percentage points (37 percent). These findings highlight potential unintended consequences of these tax credits for particular subgroups, which should be carefully considered in policy design.