Explaining and Predicting Double Tax Treaty Formation with Machine Learning Algorithms - Paper draft

Aug 2, 2026

The paper analyzes why some country pairs form double tax treaties while others do not, using machine learning informed by gravity model characteristics. It trains classification models to distinguish treaty vs. non-treaty pairs and selects random forest as the best-performing approach, achieving 94.3% accuracy. Based on predictions, it identifies 59 likely treaty pairs and evaluates outcomes by current treaty status, finding that public information is limited for only 19 countries, supporting the method’s validity. The results inform policymakers on which treaties to pursue and which may threaten international tax strategy.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages33

Reading Time1 h 23 min

AccessFree to download

Introduction

Goals of Double Tax Treaties

Drivers and Policy Context

Paper Approach and Prediction Setup

WU International Taxation Research Paper Series No. 2023-03 Explaining and Predicting Double Tax Treaty Formation with Machine Learning Algorithms Dmitry Erokhin Martin Zagler Editors: Eva Eberhartinger, Erich Kirchler, Michael Lang, Rupert Sausgruber, and Martin Zagler Electronic copy available at: [https://ssrn.com/abstract=4472477](https://ssrn.com/abstract=4472477) Explaining and Predicting Double Tax Treaty Formation with Machine Learning Algorithms Dmitry Erokhina and Martin Zaglera,b a WU Vienna University of Economics and Business, Austria bUPO University of Eastern Piedmont, Italy Paper draft 05 May 2023 Abstract The paper confronts country pairs that have tax treaties with country pairs that do not and investigates determinants for this distinction based on their gravity characteristics using machine learning techniques. It trains machine learning algorithms to distinguish such country pairs and selects random forest as the algorithm with the highest accuracy (94.3%) to use it for predictive purposes. The paper identifies 59 country pairs likely to have tax treaties based on their gravity characteristics. Countries/regions with the highest number of predicted new tax treaties are Germany (9), Saudi Arabia (8), Brazil (7), Myanmar (7), and Hong Kong (6) . The paper investigates the machine learning prediction from the point of the current tax treaty status of the identified country pairs. Out of these country pairs, 31 are known to lead tax treaty negotiations, to have initialed a tax treaty, or to have already signed a tax treaty, 6 country pairs have signed or are negotiating an exchange of information agreement or a transport tax treaty, 3 country pairs used to have tax treaties, which were terminated. There is no public information available about ongoing negotiations for only 19 countries, less than a third of all countries where the machine learning algorithm would predict a treaty. This supports the validity of the machine learning techniques for prediction purposes. These results present important insights for policymakers when deciding over which treaty to pursue and which treaties may present a threat to a country’s international tax policy. Keywords: Machine learning, treaty formation, double tax treaty JEL: F53, H20 Contact [dmitry.erokhin@wu.ac.at](dmitry.erokhin@wu.ac.at) [martin.zagler@gmail.com](martin.zagler@gmail.com) Electronic copy available at: [https://ssrn.com/abstract=4472477](https://ssrn.com/abstract=4472477) 1. Introduction This paper aims to predict the probability of a double tax treaty (DTT) between two countries using a machine learning approach. This is a highly relevant question for policymakers. First, if there is a high probability for a treaty, this means that countries in a similar situation have already concluded that such a treaty is advantageous. Given a limited capacity of treaty negotiators, this gives an indication to policymakers which treaties to pursue. Second, in the case of a capital importing income, if we find high probability that a neighbor will sign a DTT, there is a concrete risk that foreign direct investment (FDI) will be diverted away from our economy to a neighboring jurisdiction. Third, in the case of a capital exporting economy, ifa neighbor signs a DTT with a capital importing economy, our multinational firms will no longer find a level playing field in the foreign market. Understanding which countries are likely to sign a DTT in the future is crucial for economic policy. Broadly speaking, the main goal oftax 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 FDI promotion (Blonigen & Davies, 2004). A third goal is an exchange of information, which is becoming the

FAQs

What do the predictions contribute for policymakers?

The predicted treaty likelihoods help policymakers decide which treaties to pursue and anticipate where treaties could pose risks to a country’s international tax policy.

Aug 2, 2026

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