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

Abstract

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.

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