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.

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

My research combines rich micro data with quantitative structural models to study how the behavior of individual workers and firms shapes aggregate outcomes. I focus on how workers learn about their skills and sort across occupations, and on how firms’ internal labor markets shape career progression and wage growth.

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” I study how imperfect information about skills shapes workers' careers and who gains and loses from task-biased technological change.
Draft SSRN NBER Summer Institute Slides
I develop and estimate a dynamic structural model to study how uncertainty about multidimensional skills shapes occupational mobility and wages and affects who gains and who loses from task-biased technological change. Estimated on Portuguese administrative data, the model shows that learning about comparative advantage drives the reallocation of young and poorly matched workers. Removing information frictions raises aggregate output by 5.1%, while a feasible information treatment about comparative advantage can deliver about 19% of this output gain. The task-biased technological change induced by large language model adoption raises aggregate output but generates uneven wage gains and losses. Skill uncertainty becomes more costly, particularly for workers with a cognitive comparative advantage. If they knew their skills, these workers would lose little from the shock. The same information treatment is particularly effective in reducing the costs they bear, while delaying adoption smooths gains and losses for incumbents.
🏆 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

Working Papers

▶ “The Experimentation Value of Occupations” I use a dynamic structural model to quantify the experimentation value of occupations over the lifecycle.
Draft
Early in the career, workers are uncertain about their abilities and may experiment with different jobs to find where they are most productive. In this paper I estimate a dynamic structural model of occupational choice and learning on NLSY79 panel data to quantify the experimentation value of occupations over the lifecycle. This experimentation value arises when workers choose occupations in which they do not know their ability as this may result in a good match. I show this experimentation motive acts as a compensating differential particularly early in the career, drives the mobility of young workers and declines quickly over the lifecycle. Allowing for unobserved heterogeneity, I find that the willingness to pay for learning is heterogeneous across worker types and higher for high-ability workers.
“Firms’ Internal Labor Markets and Wage Dynamics” (with Sadhika Bagga) We study job mobility within firms and its consequences for wage dynamics.
Draft available upon request

Teaching

Bocconi University, Milan (Italy)

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

References

Basile Grassi
Department of Economics, Bocconi University
basile.grassi@unibocconi.it
Thomas Le Barbanchon
Department of Economics, Bocconi University
thomas.lebarbanchon@unibocconi.it
Jérôme Adda
Department of Economics, Bocconi University
jerome.adda@unibocconi.it
Luigi Bocola
Department of Economics, Stanford University
lbocola@stanford.edu

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.

▶ Replications (Link to GitHub Repository)
  • Bigio, S. and J. La’O (2020). “Distortions in Production Networks.” Quarterly Journal of Economics. (Code)
  • Buera, F. J. and Y. Shin (2013). “Financial Frictions and the Persistence of History: A Quantitative Exploration.” Journal of Political Economy. (Code)
  • Blanchard, O. J., J.-P. L’Huillier and G. Lorenzoni (2013). “News, Noise, and Fluctuations: An Empirical Exploration.” American Economic Review. (Code)
  • Bajari, P., C. L. Benkard and J. Levin (2007). “Estimating Dynamic Models of Imperfect Competition.” Econometrica. (Code)