Working papers
"Female representation and talent allocation in entrepreneurship: the role of early exposure to entrepreneurs", with Mikkel Baggesgaard Mertz and Maddalena Ronchi
[draft available upon request] [poster]
Winner of the Best Paper Award at the CSEF-RCFS Conference on Finance, Labor and Inequality 2022
Winner of the Unicredit Foundation Best Paper Award on Gender Economics (12th edition, 2022)
[draft available upon request] [poster]
Winner of the Best Paper Award at the CSEF-RCFS Conference on Finance, Labor and Inequality 2022
Winner of the Unicredit Foundation Best Paper Award on Gender Economics (12th edition, 2022)
Using registry data from Denmark, this paper shows that higher exposure to entrepreneurship during adolescence affects the representation of women and the allocation of talent in this profession. We track the educational and professional choices of one million individuals from adolescence to adulthood and exploit within-school cross-cohort variation in exposure to entrepreneurship, as measured by the share of an adolescent's peers whose parents are entrepreneurs during the last years of compulsory schooling. We find that early exposure to entrepreneurs encourages girls’ entry and tenure into this profession, while it does not affect the professional choices of boys. The effect is entirely driven by exposure to the entrepreneur parents of female peers and works via a decrease in girls’ likelihood to discontinue education at the end of compulsory schooling and to hold low-paying jobs as adults. Moreover, the increase in female entrepreneurship is associated with the creation of businesses that are larger and survive longer than the average firm. Taken together, these findings suggest that gender-specific entry barriers, which appear to be both cultural and informational in nature, may prevent some innately talented female entrepreneurs from pursuing their comparative advantage.
"Gender diversity and decision-making in teams", with Maddalena Ronchi [draft available upon request]
This project has been awarded the 2021 EIEF Grant and the IHS Research Grant
This project has been awarded the 2021 EIEF Grant and the IHS Research Grant
This paper investigates the effect of gender diversity in teams on their decision-making process and the quality of decisions. We focus on the Italian judicial system and assemble a novel database containing the universe of collegial ruling sentences from first, second, and last instance criminal courts in the district of Naples and Florence. Exploiting the quasi-random allocation of both judges and cases to judicial panels, in which ruling takes place collegially, we find that mixed-gender teams rule more leniently on similar offenses. The effect is driven by all-women panels ruling more severely, and it is non-linear in the number of women in the panel. Moreover, all-men teams take less to reach a decision, but they are also more likely to be perceived as wrong when ruling guilty - while we find no such effect for mixed-gender or all-women panels. Finally, we investigate whether gender diversity affects the quality of the final ruling as measured by the probability that the decision taken by the judicial panel is confirmed or overturned in subsequent courts, and find that mixed-gender teams are 5 percentage points more likely to take better decisions than gender-homogeneous teams.
Work in progress
"The effects of wartime sexual violence on women's labor market outcomes - with Barbara Petrongolo and Anna Raute
Peers’ academic quality and performance of teachers - with Adriano De Falco, Sofia Sierra Vasquez and Yannick Riechlin
Publications
"Targeting with machine learning: An application to a tax rebate program in Italy", with Monica Andini, Emanuele Ciani, Guido De Blasio, Alessio D'Ignazio
Journal of Economic Behavior & Organization, Vol 156, 2018
Journal of Economic Behavior & Organization, Vol 156, 2018
This paper shows how machine learning (ML) methods can be used to improve the effectiveness of public schemes and inform policy decisions. Focusing on a massive tax rebate scheme introduced in Italy in 2014, it shows that the effectiveness of the program would have significantly increased if the beneficiaries had been selected according to a transparent and easily interpretable ML algorithm. Then, some issues in estimating and using ML for the actual implementation of public policies, such as transparency and accountability, are critically discussed.