Kyeongmin Park

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 how AI adoption affects firm growth using panel data on Korean firms. Motivated by the pattern that larger firms adopt AI more, together with the fact that AI applicability varies across industries, I test whether larger firms grow systematically faster in more AI-applicable industries following AI diffusion, as expected if AI raises firm productivity. I formalize this prediction with a simple model of firms with costly AI adoption. Using an applicability measure from U.S. survey data, I find evidence consistent with this prediction during, but not before, the AI diffusion period. A complementary event-study analysis shows AI adopters experience faster sales growth and increases in profitability, alongside smaller increases in payroll. Adopters also innovate more and spend more on marketing, consistent with their reported primary uses of AI. AI adopters show larger sales and profitability returns to a given innovation event, and a larger sales return to marketing, but no corresponding profitability gain, a distinction that clarifies what lies behind the sales and profitability gains of AI adopters.

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. 

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.