Attacking Networks of Tax Evasion: Theory and Evidence from Paraguay

(with Florian Grosset, Gastón Pierri, Evan Sadler, and Panos Toulis)

The creation of a tax system that raises revenues efficiently and equitably is one of the central challenges in economic development. Our project combines the new theory and a series of three Randomized Controlled Trials (RCTs) in Paraguay to provide new insights into tax evasion by firms, enforcement spillovers through production networks, and the optimal targeting of tax enforcement activities. We develop a simple, scalable tool to detect potential misreporting in the Value Added Tax (VAT). The first experiment (8/22–12/23) estimates the direct effects of notifying taxpayers of detected misreporting on their tax compliance. The second experiment (12/24) estimates the indirect effects on notified taxpayers' suppliers and clients. The third experiment (4/25) tests the optimal targeting rule, contrasting status quo targeting based on direct effects; theory-driven optimal targeting based on our network evasion model; data-driven optimal targeting based on the results of the first two experiments; and random targeting.