Working papers

"Female representation and talent allocation in entrepreneurship: the role of early exposure to entrepreneurs", with Mikkel Baggesgaard Mertz and Maddalena Ronchi [new draft!]
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)

This paper shows that exposure to entrepreneurs during adolescence increases women’s entry and performance in entrepreneurship and improves the allocation of talent in the economy. Using population-wide registry data from Denmark, we track nearly one million individuals from adolescents to adulthood and exploit idiosyncratic within-school, cross-cohort variation in exposure to entrepreneurs, as measured by the share of an adolescent’s peers whose parents are entrepreneurs at the end of compulsory school. Early exposure, and in particular exposure to the entrepreneur parents of female peers, encourages girls’ entry and tenure into this profession, while it has no effect on boys. The increase in female entrepreneurship is associated with the creation of successful and female-friendly firms. Furthermore, early exposure reduces women’s probability to discontinue education at the end of compulsory school and to hold low wage jobs through their lives. Together these results challenge the view that the most successful female entrepreneurs would enter this profession regardless of early exposure. 

 "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 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 a sample of first- and second-instance criminal courts. Leveraging the quasi-random allocation of both judges and cases to judicial panels, in which ruling takes place collegially, we find that gender composition affects decision-making across two margins. First, it influences the type of decisions taken by the team, with same-gender panels convicting more often for comparable offenses. Second, gender composition affects the duration of decision-making, with all-men teams reaching a decision faster than any other team composition. We then turn to decision-quality as measured by the probability that the decisions taken by the judicial panels are appealed against and overturned. We find that all-men teams' decisions are more likely to be wrong, especially when ruling guilty - while we find no significant quality difference between mixed-gender and all-women panels. Finally, we show that the results are not driven by specific types of crimes or by other dimensions of diversity correlated with gender. Rather, we find that gender composition matters only in complex decision-making, suggesting that gender-based disparities emerge primarily when cases necessitate deeper analysis and nuanced consideration, and that experience may influence the extent to which these gender-based differences manifest.

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

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.