Research

This paper argues that product quality improvement is a key driver of robot adoption in U.S. manufacturing. Using microdata, I find that firms’ robot use increases with a stronger strategic focus on enhancing product quality and the introduction of higher-quality goods. Establishments with greater robot intensity produce goods with higher unit prices (a proxy for quality) relative to peers within the same product category. Following robot adoption, establishments experience increases in both unit prices and revenues, consistent with an outward shift in demand toward higher quality. Motivated by this evidence, I develop a multi-sector model in which rising automation is driven by non-homothetic forces: producing higher-quality goods is more robot-intensive, and as income rises, demand shifts toward higher quality. I calibrate key industry-level parameters governing non-homotheticity using indirect inference and quantify the extent to which these forces account for the rise in automation as part of a broader structural change in a growing economy.

This paper examines the effects of artificial intelligence (AI) adoption on firm growth. Using panel data on Korean firms, I find that AI adopters experience faster sales growth following adoption, along with increases in product innovation and marketing expenditures. Consistent with their reported primary use of AI in product development, AI adopters experience significantly greater sales growth on the introduction of new products, suggesting higher returns to product innovation. To address potential endogeneity in AI adoption, I use U.S. industry-level AI penetration as an instrument for Korean firms’ adoption. The results show that firms in industries with greater applicability of AI grow systematically faster during the period of AI diffusion, supporting AI’s impact on firm-level growth.

Under the production approach to markup estimation, where markup is estimated as the ratio of a variable factor's output elasticity to its cost share of revenue, differences in production technology across firms and over time may be mistakenly attributed to higher markups if these technological differences are not accounted for in estimating output elasticity. I analyze how allowing firms that adopt advanced automation technologies to operate under different production functions affects markup estimation. Using a firm panel that tracks the adoption status of advanced technologies, I investigate how estimated markups change when accounting for technology adoption, whether firms with automated technology, on average, charge higher markups, and whether markups increase following technology adoption.