Using accounting information to predict aggressive tax location decisions by European groups

Matteo Borrotti, Michele Rabasco and Alessandro Santoro

Economic Systems, 2023, vol. 47, issue 3

Abstract: Although locating a company in a tax haven is not illegal per se, it is likely to be part of a scheme purported to erode the tax base or to shift profits to less-taxed jurisdictions. For this reason, this type of location decision is usually targeted by anti-avoidance laws, that can take the form either of specific rules or general standards that, ex-post, sanction or limit the location decision. However, rules entail higher drafting costs and are easy to circumvent whereas standards entail more uncertainty costs. The goal of this paper is to illustrate that the risk of aggressive location decisions can be predicted ex-ante using publicly available data and that this prediction can be used by tax authorities. In the paper, we do two things. First, we use publicly available accounting data for the period 2015–2019 on 4031 group ultimate owners (GUO) of active listed companies resident in one of the 27 European Union countries to predict the probability that these companies would have at least a subsidiary in a tax haven, by spring 2021, as well as the intensity in the use of tax havens. Second, we discuss how this prediction can be used by tax authorities in the context of a new administrative preventive approach that complements the traditional legal approach. This approach can increase welfare by reducing uncertainty, thus increasing investments and economic growth.

Keywords: Aggressive tax planning; Machine learning; Tax havens; Tax compliance by multinationals (search for similar items in EconPapers)
JEL-codes: C53 H25 H26 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosys:v:47:y:2023:i:3:s0939362523000195

DOI: 10.1016/j.ecosys.2023.101090

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