Work in Progress
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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 and its uncertainty drive a structural reallocation, causing workers to systematically avoid training paths facing high automation risk, while choosing paths requiring more manual or social skills. Second, direct AI impact and labor scarcity balance each other; as a result, expected wages rise across all training paths by approximately +14%. Third, automation uncertainty generates an endogenous risk premium of up to +2.3%.
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Overeducation Over the Lifecycle: Disentangling Frictions, Innate Ability, and Job‑Specific Experience
Abstract
I study overeducation persistence with a directed-search model where workers differ in education, field, innate ability, job-specific experience, and age. Calibrated to the NLSY79 and O*NET, a structural decomposition shows that nontransferable job-specific experience is the dominant source of long-run persistence, frictions matter mostly early on, and slow ability learning amplifies both channels. Age effects and apparent overeducation are minor. Education is treated as exogenous to focus on post-schooling dynamics; selection is captured through heterogeneous ability distributions across education groups. Policies that speed early learning and reduce frictions are most effective.
