I am a 4th Year PhD student in the Rome Economics Doctorate (RED), a new PhD program run by EIEF, LUISS, Sapienza University of Rome and Tor Vergata University of Rome.
My research lies at the intersection of Public Economics and Law & Economics, studying how firms and individuals respond to enforcement and legal institutions.
Before starting my PhD, I received an MSc in Economics from EIEF and LUISS (RoME) and a BSc in Statistics, Economics, and Finance from the University of Rome La Sapienza.
Find my CV here!
Email: escalcione@redphd.it
When resources are limited, information about the environment becomes critically important. This paper introduces a stylized dynamic model to illustrate how the challenge of audit agents—who may engage in activities such as tax evasion, crime, or other undesirable behaviors—is shaped by the need to gather information for future use. Our analysis reveals that monitors might choose to forgo immediate preventive actions in favor of collecting valuable information. This strategic decision allows them to better identify and target high-risk agents in the future. The extent to which monitors prioritize learning over deterrence depends on how valuable is learning. We discuss two key factors that affect the value of acquiring information: heterogeneity in agents’ characteristics and level of penalties. We also show that an information acquisition rationale arises even in a static model with delegated enforcement.
This paper studies the labor market effects of a no-poaching agreement among four major IT consulting multinationals in Portugal between 2014 and 2021. Using matched employer–employee administrative data linked to court evidence, we document in a difference-in-differences analysis how the agreement restricted worker mobility and suppressed wages by curtailing workers' outside options. A naive comparison of average wages at cartel and control firms detects no significant effect, masking a substantial compositional shift: the agreement simultaneously increased the retention of high-wage incumbents and raised the turnover of low-wage workers, mechanically inflating average wages and concealing within-worker losses. Holding worker composition fixed, we find that incumbent target workers suffered wage losses of approximately 12.5% by 2019, concentrated among mid- and high-skill professionals. We further show that the cartel generated negative wage spillovers of around 7.1% by 2019 on workers at non-member firms with strong pre-cartel labor market ties to the colluding parties, suggesting that a small cartel can suppress wages across an entire labor market segment. Finally, we find no evidence that the agreement stimulated investment in worker training, directly contradicting the efficiency defense advanced by one of the defendants.
Forecasting with Dynamic Factor Models: An empirical exercise, EIEF RoME Theses