• Overeducation Over the Lifecycle: Disentangling Frictions, Innate Ability, and Job‑Specific Experience

    Aleandro Palazzo

    [Draft]
    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.

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  • Rethinking Investments in Human Capital in the Age of Artificial Intelligence

    Aleandro Palazzo

    [Preliminary Draft]
    Abstract

    How does Artificial Intelligence reshape human capital investment decisions? I develop an Overlapping Generations model where risk-averse agents choose among different training paths to maximize their lifetime utility. Agents form expectations about the future impact of AI in each training path, but some paths are more uncertain than others. Using US data from BLS employment projections and recent AI exposure metrics, the model reveals three key results. First, uncertainty is an important driver of reallocation: risk-averse individuals systematically avoid highly uncertain training paths (reducing employment by up to 7% in the most uncertain one), which endogenously increases expected wages in those training paths due to labor scarcity. Second, workers reallocate toward paths with higher expected demand and lower uncertainty; however, the resulting crowding effect dampens the positive demand shock, ultimately leaving expected wages substantially unchanged. Third, due to these general equilibrium forces, the net impact of AI on expected wages remains modest across all training paths, contained between -6\% and +6\% compared to pre-AI (2022) median wages.