La mia ricerca si concentra sull'inferenza causale e sulla valutazione di politiche, con particolare attenzione alle applicazioni nel campo dell'economia dell'istruzione e dell'economia del lavoro. I miei progetti in corso sono descritti di seguito
"At the Roots of Child Development: On the Role of Parental Beliefs, Preferences and Engagement" finanziato da MIUR (PRIN 2017) con M. Bigoni, C. Monfardini, D. Iorio, T. Arduini (tutti presso Università di Bologna). Questo progetto è sviluppato con la collaborazione scientifica di A. Guarini ed A. Sansavini del Dipartimento di Psicologia (Università di Bologna)
We investigate the determinants of children’s non-cognitive skills and social preferences, focusing on the role of parenting practices. The project comprises three pillars: i) a theoretical model of parental investment; ii) a novel longitudinal dataset on children’s preferences and non-cognitive skills, and parents’ preferences, beliefs, and time-use in activities that involve their child; iii) the exogenous variation in parental beliefs and time-investments induced by a psycho-educational intervention that targets parents through an RCT. We aim at contributing to the creation of a sound empirical and theoretical basis for the design of policies promoting children’s well-being and equality of opportunities.
Gender Differences in STEM: Can Teaching Girls to Code Close the Gaps? finanziato da the Spencer Foundation (Small Grant for Education Research); EIEF grant – in entrambi i casi sono P.I.) con M. Carlana (Harvard Kennedy School).
The project aims at understanding the reasons behind gender gaps in the choice of STEM education and whether these gaps can be reduced through early exposure of girls to coding. First, we will build a new comprehensive dataset combining survey and administrative information that allows to analyze the barriers that may deter girls from pursuing a STEM career. Second, we will study whether a program that exposes girls in junior high schools to coding is effective at reducing the gender gap in STEM education, relying on the random allocation of participants to the program due to rationing of available slots. The data collected will allow to explore spillovers on other female and male students, to indicate possible mediators of the program impact and to document heterogeneity with respect to participant pre-treatment characteristics. This research can also inform about who are the students who can mostly benefit from taking part to the program, so that effective targeting could be implemented in future, as slots rationing is likely to persist in the future.