The aim of this paper is to study whether politicians manipulate fiscal policy to extract private rents. We focus on the local personal income tax (PIT), in the setting of Italian cities, which is a progressive instrument that allows mayors to set different rates to distinct wage groups. We...

Can machine learning support better governance? In the context of Brazilian municipalities, 2001-2012, we have access to detailed accounts of local budgets and audit data on the associated fiscal corruption. Using the budget variables as predictors, we train a tree-based gradient-boosted...

Can machine learning support better governance? In the context of Brazilian municipalities, 2001-2012, we have access to detailed accounts of local budgets and audit data on the associated fiscal corruption. Using the budget variables as predictors, we train a tree-based gradient-boosted...

Can machine learning support better governance? In the context of Brazilian municipalities, 2001-2012, we have access to detailed accounts of local budgets and audit data on the associated fiscal corruption. Using the budget variables as predictors, we train a tree-based gradient-boosted...

The aim of this paper is to study the effect of local corruption on political participation which is mediated by the press. Focusing on Italy, we generate a daily measure of exposition to local corruption screening articles of main Italian press agency. Applying an event-study methodology on...

The standard household model predicts that couples cooperate to maximize family income. This paper shows that gender identity norms prevent family income maximization. For identification, we focus on an Italian policy that grants a large tax credit to the main earner when the second earner...

The goal of this paper is to study the impact of the lockdown policy on voting behaviour, during the COVID-19 pandemic. We focus on France, where a differential lockdown was implemented across departments, based on the local diffusion of the disease. In particular, the country has been divided...