【报告摘要】We develop a framework of partial automation to study the incidence of technological change within and across occupations. The key object is the within-occupation gradient of workers' comparative advantage across tasks. Core tasks are those where productivity rises most steeply with worker ability; peripheral tasks are those where workers of different ability are closer substitutes. Automating these two ends has opposite effects. Core automation weakens the return to specialization and compresses wage differences across worker ranks, generating forces toward commoditization. Peripheral automation strengthens that return and widens those differences, generating winner-takes-all forces. Across occupations, the joint distribution of comparative advantage governs worker reallocation and equilibrium incidence. We measure task coreness and technology exposure for 18,796 tasks across 923 occupations. Past automation waves show the predicted opposite effects on wage growth by worker rank and within-occupation dispersion. We assess whether current and prospective AI exposure tilts toward core or peripheral tasks.

【报告人简介】Tianyu Fan received his Ph.D. in Economics from Yale University in 2026. He will join the Chinese University of Hong Kong as an Assistant Professor of Economics in 2027. His research spans macroeconomics, political economy, international economics, and economic growth.