Machine Learning (ML) can be a powerful tool to inform policy decisions. Those who are treated under a programme might have different propensities to put into practice the behaviour that the policymaker wants to incentivize. ML algorithms can be used to predict the policy-compliers; that is,...

We use Machine Learning (ML) predictive tools to propose a policy-assignment rule designed to increase the effectiveness of public guarantee programs. This rule can be used as a benchmark to improve targeting in order to reach the stated policy goals. Public guarantee schemes should target firms...

This paper employs a unique Italian data source to take a comprehensive approach to labor market pooling. It jointly considers many different aspects of the agglomeration – labor market relationship, including turnover, learning, matching, and hold up. It also considers labor market pooling...

The paper evaluates the effectiveness of a major Italian place-based policy (Contratti di Programma), by means of which the state approves and finances industrial projects proposed by private firms. Using the areas to be exposed to the same policy at a later date as counterfactuals, the study...