Using machine learning methods in a difference-in-differences setting, we quantify the population of informal workers generated by an irregular migration shock. We exploit the exogenous variation from the Arab Spring wave on southern Italian coasts, and a rich data on Italian vineyards. We...

This paper develops a novel asymptotic theory for panel models with common shocks. We assume that contemporaneous correlation can be generated by both the presence of common regressors among units and weak spatial dependence among the error terms. Several characteristics of the panel are...

This paper considers models with latent/discrete endogenous regressors and presents a simulation-based two-step (STS) estimator. The endogeneity is corrected by adopting a simulation-based control function approach. The first step consists of simulating of the residuals of the reduced-form...

We propose a new approach to detect and quantify informal employment resulting from irregular migration shocks. Focusing on a largely informal sector, agriculture, and on the exogenous variation from the Arab Spring wave on southern Italian coasts, we use machine-learning techniques to document...