An agent based model for studying optimal tax collection policy using experimental data: The cases of Chile and Italy

This paper investigates optimal audit programs in an economy populated by artificial agents. The behavior of the artificial agents is calibrated using data obtained from experiments on fiscal evasion made in northern Chile (Antofagasta) and northern Italy (Trento). We identify a tax collection policy that is optimal in the sense that its outperforms the tax payments made by the calibrated agents, using any other standard collection plans used by governments. We find that the design of an optimal audit scheme depends on three components: income distribution, the identification of patterns of behaviors and the number of times individuals are audited.