Piero De Dominicis
Field: Macroeconomics, Labor Economics, Structural Econometrics
Research Interests:
- Job Mobility Within and Across Firms
- Human Capital and Wage Determination
- Technological Change and Labor Reallocation
- Uncertainty and Firm Dynamics
(Expected) Graduation: June 2027
References
- Basile Grassi basile.grassi@unibocconi.it
- Thomas Le Barbanchon thomas.lebarbanchon@unibocconi.it
- Jerome Adda jerome.adda@unibocconi.it
- Luigi Bocola lbocola@stanford.edu
Contact
Bocconi University,
Department of Economics, Via G. Roentgen 1, 20136, Milan (Italy)
piero.dedominicis@unibocconi.it
My research combines rich micro data with quantitative structural models to study how the behavior of individual workers and firms shapes aggregate outcomes. 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.
In Summer 2026 I presented my Job Market Paper at the NBER Summer Instiute - The Micro and Macro Perspective of the Aggregate Labor Market. My JMP was also awarded the Graduate Student Paper Award by the Society for Computational Economics at the CEF Conference 2026.
I will be on the job market in AY. 2026-2027.
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.
WORKING PAPERS
- The Micro and Macro Implications of Multidimensional Skill Uncertainty
- The Experimentation Value of Occupations
WORK IN PROGRESS
-
Promoting Inequality: Internal Labor Markets and Wage Dynamics, with Sadhika Bagga