Publications:
Caggiano E., and Rocco L. “Bipartisan Firms.” Economics & Politics (2026).
Caggiano E., and Maurici F. “Gone with the Cycle: The Asymmetric Impact of Business Cycle on Growth.” The Journal of Economic Asymmetries (2026)
Caggiano, E., d’Addona, S., Maurici, F. & Vittori, C. “Cluster Atlas: Stability, Inequality, and Growth.” Economic Systems (2026)
Job Market Paper
Caggiano Emanuele, “Lobbying in the US: companies’ reaction to an unexpected political change”
Abstract: This study examines the relationship between campaign donations and lobbying, utilizing a comprehensive dataset to track firms’ economic and political engagements. We test the hypothesis that firms use electoral campaign financing as an insurance strategy using an event study framework. Our results show that firms that did not finance the electoral campaign increased their lobbying expenditures by 47% following the unexpected election of Donald Trump in 2016, in contrast to the non-reactive behavior observed after Obama’s 2012 election. Additional analyses confirm the central hypothesis, indicating that firms adjust their lobbying based on electoral outcomes. The study also incorporates a heterogeneity analysis using public procurement data to understand how firm revenue dependencies with the public sector affect the decision to increase lobbying expenditure. The study also incorporates a heterogeneity analysis using public procurement data to understand firm sensitivities further.
Work in progress
- Caggiano Emanuele, Enrico Rettore, Lorenzo Rocco. “Synthetic Data In Economics”
Abstract: This study investigates the use of synthetic data in economic research as a solution to challenges such as privacy restrictions and data scarcity. To assess the validity of synthetic data, we replicate two influential studies—Moretti 2013 and Arenberg et al. 2024—using data generated through Variational Autoencoders. The findings reveal that synthetic data effectively retains the analytical value of real data while ensuring privacy compliance. By benchmarking synthetic datasets against their real counterparts, we demonstrate their reliability in reproducing critical insights, emphasizing their potential to enhance data accessibility and ethical data use.
Policy Work: