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TRENTINI FRANCESCO

Ruolo:
Assegnista di ricerca
Settore scientifico disciplinare:
Economia politica (SECS-P/01)
E-mail:
[email protected]

Biografia

I am a Labour Economist, Postdoctoral Researcher at the University of Milano-Bicocca, Department of Statistics and Quantitative Methods and  CRISP, affiliated with LABORatorio "R. Revelli", and research coordinator at Randstad Research Italia. 

After graduating in Economics and Social Sciences from Bocconi University of Milan, I obtained a Master in Economics and Complexity from the Collegio Carlo Alberto, Turin and a PhD in Economics from the University of Turin, where I was adjunct professor of Labour economics. 

My research interests range in the field of labour economics, with particular attention to the evolution of occupational profiles, labour market performance and impact assessment of labour market policies. I mostly conduct quantitative research with a variety of sources, including surveys, administrative and web data.

Pubblicazioni

  • Abbonato, D., Bottai, C., Trentini, F., Velyka, A. (2024). Validazione e arricchimento del RUNTS con dati web. Intervento presentato a: Quindicesima Conferenza Nazionale di Statistica, Roma, Italia. Dettaglio

  • Bottai, C., Trentini, F., Velyka, A. (2024). Augmenting the Italian Third Sector registry using non-profit organisations’ websites. In Proceedings CARMA 2024 - 6th International Conference on Advanced Research Methods and Analytics (pp.140-147). Editorial Universitat Politècnica de València [10.4995/carma2024.2024.17830]. Dettaglio

  • Pacelli, L., Trentini, F., Filandri, M. (2023). An analysis of NEET youths in EU in a longitudinal perspective. SINAPPSI, 13(1), 68-87 [10.53223/Sinappsi_2023-01-5]. Dettaglio

  • Berton, F., Pacelli, L., Quaranta, R., Trentini, F. (2023). Patterns of labor market reforms: a regional approach to the Italian Jobs Act. SINAPPSI, 13(1), 50-67 [10.53223/Sinappsi_2023-01-4]. Dettaglio

  • Mercorio, F., Trentini, F., Mezzanzanica, M., Pallucchini, F., Guo, Y., Langer, C. (2023). PIAAC2ESCO - An AI-driven classification of the PIAAC Background questionnaire onto the ESCO Skills Pillar (2021-2022) [Banca dati] [10.20366/unimib/unidata/si399-1.0]. Dettaglio

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