Who will sign a double tax treaty next? - A prediction based on economic determinants and machine learning algorithms

Aug 6, 2026

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Double tax treaties shape international economic relations, yet forecasting which country pairs will sign remains difficult. This study addresses the gap by applying machine learning classification to multinational economic data. The analysis identifies 59 country pairs likely to form tax treaties under their economic conditions and shows that foreign direct investment, trade, gross domestic product, and distance are key predictors. Results indicate random forest outperforms conventional econometric approaches. Predicted high-probability treaty candidates can guide policymakers’ focus and resources in upcoming negotiations.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages19

Reading Time48 min

AccessFree to download

Introduction

Goals and drivers of tax treaty formation

Methods and predictive modeling

Machine learning classification approach

Findings and implications

Key predictors and model performance

Policy relevance

Economic Modelling 139 (2024) 106819 Contents lists available at ScienceDirect Economic Modelling journal [homepage:](homepage: www.journals.elsevier.com/economic-modelling)[ www.journals.elsevier.com/economic-modelling](homepage: www.journals.elsevier.com/economic-modelling) | Who will sign a double tax treaty next? A prediction based on economic determinants and machine learning algorithms☆
Dmitry Erokhin a, b, *, 1 , Martin Zagler a, c, 1
a WU Vienna University of Economics and Business, Austria b International Institute for Applied Systems Analysis, Austria c UPO University of Eastern Piedmont, Italy | | | | --- | --- | --- | | A R T I C L E I N F O | | A B S T R A C T | | Handling editor: Sushanta Mallick
Original content: Tax treaty formation (Original data) | | Double tax treaties play a crucial role in shaping international economic relations, yet predicting which country pairs are likely to sign tax treaties remains a challenge. This study addresses this gap by employing a novel machine learning approach to predict tax treaty formations. Using data from a wide range of countries, we apply a series of classification algorithms and identify 59 country pairs likely to have tax treaties given their economic conditions. Our findings reveal that variables such as foreign direct investment, trade, Gross Domestic Product, and distance are significant predictors of tax treaty formations. Importantly, we demonstrate that the random forest classification algorithm outperforms conventional econometric methods in predicting tax treaty formations. By identifying which potential treaties exhibit a high probability of success, this paper gives policymakers an indication where to focus their attention and resources in upcoming treaty negotiations. | | JEL classification: JEL: F53
H20
Keywords:
Machine learning
Treaty formation
Double tax treaty | | | 1. Introduction Tax treaty formation is a very complex and multi-faceted decisionmaking process. Broadly speaking, the main goal of tax treaties is to boost trade and investment between countries by removing unnecessary tax barriers, which primarily means elimination of double taxation. Another important goal is to fight tax evasion and double non-taxation. In particular, new tax treaties focus more on anti-avoidance measures rather than on foreign direct investment (FDI) promotion (Blonigen and Davies, 2004). A third goal is an exchange of information, which is becoming the primary focus of new tax treaties and is also a subject of tax treaty negotiations. For illustration of different goals of a tax treaty, we can look at the explanation of a proposed treaty with Japan by the United States (Joint Committee on Taxation, 2004). That treaty defines goals of reduction or elimination of double taxation of income earned by residents of each country from sources within the other country, prevention of avoidance or evasion of the taxes of the two countries, promotion of closer economic cooperation between the two countries as well as elimination of possible barriers to trade and investment caused by overlapping taxing jurisdictions of the two countries. However, especially historically, tax treaty formation was also driven by “chess--games between superpowers”, decisions of “key persons”(Evers, 2013), and corporate lobbyism (Thrall, 2021). Policy diffusion,2 too, may have an effect on the policies adopted by the countries (Chen and Wang, 2021; Lopez-Cariboni and Cao, 2015) including in the area of taxation (Cao, 2010) and tax treaties (Barthel and Neumayer, 2012). The significance of tax treaty formation and its implications for international economic relations cannot be overstated. As globalization continues to drive cross-border economic activities, the negotiation and formation of tax treaties between countries play a crucial role in ☆ This research is supported by the Austrian Science Fund (FWF): Doc 92-G. The authors are grateful for helpful c

FAQs

Why is predicting double tax treaty signings challenging?

Tax treaty formation is a complex, multi-faceted decision process involving many political and economic factors, and identifying likely treaty pairs in advance remains difficult.

Which factors are identified as significant predictors of tax treaty formation?

Foreign direct investment, trade, gross domestic product, and distance are reported as significant predictors of double tax treaty formation.

How does the machine learning approach compare with conventional econometric methods?

The random forest classification algorithm outperforms conventional econometric methods in predicting which country pairs are likely to sign tax treaties.

Aug 6, 2026

Upload by Anda