Welcome!

I am a sixth-year Ph.D. candidate in Economics at Bocconi University in Milan, where my advisors are Basile Grassi, Thomas Le Barbanchon and Jérôme Adda.

I will be on the 2026/2027 academic job market.

My research focuses on how workers sort across jobs and the consequences for human capital and wages. I am particularly interested in how uncertainty shapes the behaviour of firms and workers and how those responses affect the aggregate economy.

Research interests: Macroeconomics, Labor Economics, and Structural Econometrics.

In Spring 2026, I visited the Stanford Economics Department as a Visiting Student Researcher, hosted by Luigi Bocola. During Spring 2025, I was a Visiting Student Research Collaborator at Princeton University, hosted by Gianluca Violante.

Curriculum Vitae

Research

Job Market Paper

▶ "The Micro and Macro Implications of Multidimensional Skill Uncertainty"
What are the consequences of multidimensional skill uncertainty for workers’ wages and aggregate output? I develop and estimate a general equilibrium dynamic Roy model in which workers have imperfect information about their multidimensional skills and accumulate task-specific human capital. Estimated on Portuguese administrative data, the model rationalizes key patterns of occupational mobility, with learning about comparative advantage playing a central role in occupational reallocation among young and poorly matched workers. Removing information frictions raises aggregate output by 5.2%, primarily through better skill allocation across occupations, while generating the largest wage gains early in workers’ careers and among the most mismatched. A feasible information treatment about comparative advantage across tasks recovers about 18% of this output loss. Finally, job transformation induced by Large Language Model adoption raises output while widening the output gap between the imperfect- and full-information economies along the transition path and in the new long run.
Draft SSRN NBER Summer Institute Slides
🏆 Best Young Scholar Paper Award (First Prize) — CEF 2026
Presented at the NBER Summer Institute – The Micro and Macro Perspectives of the Aggregate Labor Market

Work in Progress

  • “The Experimentation Value of Occupations” (Draft available soon)

  • “Promoting Inequality: Internal Job Ladders and Wage Dynamics”

Teaching

Bocconi University, Milan (Italy)

  • Monetary Theory and Policy (30159, Bachelor) — Fall 2022 – Present
  • Economia – Modulo 2 (Macroeconomia) (30066, Bachelor) — Fall 2022 – Present
  • Financial Macroeconomics (30172, Bachelor) — Fall 2022 – Fall 2024
  • Econometrics (30462, Bachelor) — Fall 2022 – Fall 2024

Resources

▶ Projection Methods with Neoclassical Growth Model Application (Link to GitHub Repository)

Python implementations of projection methods (Chebyshev polynomials) for solving dynamic models, applied to the Neoclassical Growth Model — first the approximation machinery itself, then a global solution of the stochastic NGM with endogenous labor supply.

Why Chebyshev polynomials

Chebyshev nodes deliver near-uniform accuracy on smooth functions, where a Taylor expansion is only accurate near the point it expands around.

Chebyshev vs Taylor

Stochastic NGM with endogenous labor

Policy functions for consumption and labor, $c(k,z)$ and $l(k,z)$, solved globally. Euler residuals vanish as the polynomial degree rises, confirming high global accuracy.

Policy functions

Calibrated to standard quarterly values ($\beta = 0.99$, $\alpha = 0.33$, $\delta = 0.025$, $\rho = 0.95$). Full derivations, the remaining figures, the Euler-error diagnostics and the code are in the repository.