Welcome to seminar by Dario Tortarolo on May 10th

By Annette Alstadsæter

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On Wednesday, Ma10th at 14.00, Dario Tortarolo will present the paper “Revealing 21% of GDP in Hidden Assets: Evidence from Argentina's Tax Amnesties” at NMBU.

The presentation is on May 10th, in room T451 (OBS: NEW LOCATION)  at Tårnbygningen, NMBU, from 14.00-15.00.

Dario Tortarolo is an Assistant Professor of Economics at the University of Nottingham and a Research Associate at the Institute for Fiscal Studies. His research focuses on Public Economics and seeks to understand (i) how public policies affect workers, businesses, and consumers, (ii) the distributional impacts and the unintended effects due to design issues or a limited understanding of policymakers, and (iii) how governments can address tax avoidance and evasion problems, especially at the top of the income distribution. He holds a Ph.D. in Economics from UC Berkeley and a B.Sc. and M.Sc. in Economics from Universidad Nacional de La Plata. He is the 2020 recipient of the National Tax Association’s Outstanding Doctoral Dissertation Award.

He will present his most recent paper joint  with Juliana Londono-Velez: "Revealing 21% of GDP in Hidden Assets: Evidence from Argentina's Tax Amnesties"

Abstract
We study the effectiveness of tax amnesties and their impacts on capital taxation and public spending. We leverage rich policy variation from Argentina, which implemented the world’s most successful program, reportedly revealing assets worth 21% of GDP. First, despite substantial offshore tax evasion, declared foreign assets quadrupled. Second, tax progressivity improved because disclosures were extensive among the wealthiest 0.1%. Third, improving tax compliance has sizable fiscal externalities on capital taxes and social transfers: the wealth and capital income tax bases more than doubled, and the earmarked revenue boosted pension benefits by 15%. We end by discussing the lessons from Argentina.

Please contact malgorzata.bernhardsen@nmbu.no should you have any practical questions.

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