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 quality improvement 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 the interaction of production- and demand-side 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 these forces using indirect inference and quantify that they account for approximately one-third of the secular rise in robot adoption as part of 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.

This study examines variation in AI adoption intensity across industries, analyzing how these patterns reflect firms’ strategic priorities and adoption motives. Using microdata covering all sectors, we provide a cross-sectoral accounting of AI intensity and its relation to channels through which AI may affect consumer welfare, including cost reduction, quality improvement, and product variety expansion. We illustrate how these patterns inform potential welfare gains from AI. We further assess how industry-level price index changes relate to AI intensity and its primary usage across sectors, with implications for whether these welfare effects are captured in official price indices.