Artificial intelligence is changing which tasks workers do and how they do them. Predicting its labor market consequences requires understanding how technical change affects workers' productivity across tasks, how workers adapt by changing occupations and acquiring new skills, and how wages adjust in general equilibrium. We introduce a dynamic task-based model in which workers accumulate multidimensional skills that shape their comparative advantage and, in turn, their occupational choices. We then develop an estimation strategy that recovers (i) the mapping from skills to task-specific productivity, (ii) the law of motion for skill accumulation, and (iii) the determinants of occupational choice. We use the quantified model to study generative AI's impact via augmentation, automation, and a third and new channel—simplification—which captures how technologies change the skills needed to perform tasks. Our key finding is that AI substantially reduces wage inequality while raising average wages by 21 percent. AI's equalizing effect is fully driven by simplification, enabling workers across skill levels to compete for the same jobs. We show that the model's predictions line up with recent labor market data.

Media: Fox Business; Marginal Revolution.

This paper studies the distributional effects of two general purpose technologies: steam and electric power. Using newly collected historical data from the US and the Netherlands and plausibly exogenous variation in adoption, I show that steam power increased firm sizes and top income and wealth inequality, while electric power had opposite effects. These effects reveal a key link between technology and inequality: scale bias—the extent to which technical change increases returns to scale. Steam entailed high fixed costs and was mainly adopted by large firms, whereas electricity was small-scale-biased. A model of occupational and technological choice illustrates how large-scale-biased technical change raises top income inequality by concentrating entrepreneurial income.

Awards: UniCredit Foundation Best Paper Award.

How did the US become a land of opportunity? Previous historical research on intergenerational mobility has focused on father-son income correlations, masking the role of mothers. We introduce a new mobility measure that incorporates both parents' human capital, develop a latent variable method leveraging literacy as a proxy, and construct a representative linked panel that includes women. We find that intergenerational mobility—in both human capital and income—rose sharply from the 19th to the 20th century. Initially, maternal human capital was most predictive of children's outcomes. However, as schooling expanded, this reliance declined and intergenerational mobility rose. Investments in mass education have therefore been central to America's rise as a mobile society.

Media: Marginal Revolution; SIEPR Policy Brief; Macro Roundup; National Affairs; BSE Focus.

This paper studies the long-run effects of slavery and restrictive Jim Crow institutions on Black Americans' economic outcomes. We track individual-level census records of each Black family from 1850 to 1940, and extend our analysis to neighborhood-level outcomes in 2000 and surname-based outcomes in 2023. We show that Black families whose ancestors were enslaved until the Civil War have considerably lower education, income, and wealth than Black families whose ancestors were free before the Civil War. The disparities between the two groups have persisted substantially because most families enslaved until the Civil War lived in states with strict Jim Crow regimes after slavery ended. In a regression discontinuity design based on ancestors' enslavement locations, we show that Jim Crow institutions sharply reduced Black families' economic progress in the long run.

Media: The Guardian; Marginal Revolution; 2025 Nobel Lecture; VoxEU; Hoover Institution; Frankfurter Allgemeine; National Affairs; Helsingin Sanomat; VoxDev.
Awards: Erik Olin Wright Prize; Urban Economics Association Prize; IPUMS USA Research Award.