We examine the role of financial aid in shaping the formation of human capital in economics. Specifically, we study the impact of a large merit-based scholarship for graduate studies in affecting individuals’ occupational choices, career trajectories, and labor market outcomes of a generation of Italian economists with special focus on gender gaps and the role of social mobility. We construct a unique dataset that combines archival sources and includes microdata for the universe of applicants to the scholarship program and follow these individuals over their professional life. Our unique sample that focuses on the high end of the talent and ability distribution also allows us to analyze the characteristics of top graduates, a group which tends to be under-sampled in most surveys. We discuss five main results. First, women are less likely to be shortlisted for a scholarship as they tend to receive lower scores in the most subjective criteria used in the initial screening of candidates. Second, scholarship winners are much more likely to choose a research career and this effect is larger for women. Third, women who work in Italian universities tend to have less citations than men who work in Italy. However, the citation gender gap is smaller for candidates who received a scholarship. Fourth, women take longer to be promoted to the rank of full professor, even after controlling for academic productivity. Fifth, it is easier to become a high achiever for individuals from households with a lower socio-economic status if they reside in high social mobility provinces. However, high-achievers from lower socio-economic status households face an up-hill battle even in high social mobility provinces.
Estimating the trustworthiness of a set of actors when all the available information is provided by the actors themselves is a hard problem. When two actors have conflicting reports about each other, how do we establish which of the two (if any) deserves our trust? In this paper, we model this scenario as a network problem: actors are nodes in a network and their reports about each other are the edges of the network. To estimate their trustworthiness levels, we develop an iterative framework which looks at all the reports about each connected actor pair to define its trustworthiness balance. We apply this framework to a customer/supplier business network. We show that our trustworthiness score is a significant predictor of the likelihood a business will pay a fine if audited. We show that the market network is characterized by homophily: businesses tend to connect to partners with similar trustworthiness degrees. This suggests that the topology of the network influences the behavior of the actors composing it, indicating that market regulatory efforts should take into account network theory to prevent further degeneration and failures.