Working Papers
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Investments in Human Capital in the Age of Artificial Intelligence
Abstract
How does Artificial Intelligence reshape human capital investment decisions? I develop a task-based model where risk-averse agents make irreversible training choices based on AI's expected impact and uncertainty. Calibrated to the US labor market, the framework decomposes AI shocks into expected automation, expected augmentation, and their associated uncertainties. The model yields three key results. First, expected automation drives a structural reallocation, causing young students to systematically avoid training paths facing high automation risk, while choosing paths requiring more manual or social skills. Second, average expected wages rise, but their distribution depends on the responsiveness of training choices made by the new generation: when it is high, labor scarcity offsets the direct impact of AI and wage gains are nearly uniform across training paths; when it is low, labor reallocation is weaker, and wage gains are more concentrated in the safest paths. Third, automation uncertainty generates an endogenous risk premium.
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Overeducation Persistence Over the Lifecycle: Disentangling Frictions, Specialization and Aging
Abstract
Between 20% and 35% of workers are overeducated: they hold more education than the typical requirement of their job. The monthly rate at which overeducated U.S. college graduates move to a matched job that requires their degree falls by almost two thirds between their thirties and their fifties. Moreover, among graduates who are overeducated at age 30, one in four has never moved to a matched job by age 55. I study these facts with a lifecycle directed-search model in which graduates differ by field of study and accumulate field-specific experience that is only partly transferable between non-college and college jobs, firms and workers learn about match quality over time, and the work horizon shortens with age. Calibrated to the NLSY79, the model reproduces the profile of the hazard rate of exits by age, and the exits in the first year after age 30, although it understates the share of graduates who never leave overeducation. Search and learning frictions account for most of the time that graduates spend overeducated, especially early in the career. As they age, the partial loss of experience and the shrinking work horizon account for almost half of overeducation persistence.
Work in Progress
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AI and Labor Market Rigidity: Implications for Human Capital
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Is AI Unsorting the Labor Market?