AI and the Extended Workday: Productivity, Contracting Efficiency, and Distribution of Rents

This study investigates how occupational AI exposure impacts employment at the intensive margin, i.e., the length of workdays and the allocation of time between work and leisure. Drawing on individual-level time diary data from 2004–2023, we find that higher AI exposure—whether stemming from the ChatGPT shock or broader AI evolution—is associated with longer work hours and reduced leisure time, primarily due to AI complementing human labor rather than replacing it. This effect is particularly pronounced in contexts where AI significantly enhances marginal productivity and monitoring efficiency. It is further amplified in competitive labor and product markets, where workers have limited bargaining power to retain the benefits of productivity gains, which are often captured by consumers or firms instead. The findings question the expectation that technological advancements alleviate human labor burdens, revealing instead a paradox where such progresses compromise work-life balance. Joint work with Junyoung Park, Rachel (Jiqiu) Xiao, and Shen Zhang.