Network-Based Policies Versus Tax Evasion

Abstract

This chapter explores the optimal income tax audit strategies from a social planner’s perspective, whose objective is to minimize the aggregated tax evasion of a given society. Agents live in a social-cohesive network with homophilic linkages, meaning individuals connect only with people who are akin to them. Further, each period individuals share their memories about past audits and consequently update their subjective probability of being audited. The Tax Agency finds that network-based audit policies are inefficient, in the sense that they are just as good as random. Thus, the social planner credibly announces that, from now on, audit rates will be linearly proportionate to the agent’s income, making richer people more prone of being audited. Audit rates are now endogenous and heterogeneous among agents, making it possible for the Tax Agency to find an optimal network-based policy following a local-average strategy where a “key sector” of society is predominantly audited every period. Following this strategy, under a dynamic framework, the Nash Equilibrium for the average subjective audit rate is swiftly raised after just a few fiscal years. The enhanced subjective audit rate, in turn, unfolds as a larger tax revenue collection and a significant deterrence of income tax evasion.

This project has received funding from the ITN ExSIDE European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 721846.