Examining Individual Tax Morale in Europe with Machine-Learning Methods

Purpose: This research examines and contributes to the behavioural lit­erature on voluntary tax compliance. It focuses on the use and potential of machine-learning (ML) methods and models to predict individual tax morale across Europe, and it identifies the factors that influence predic­tive accuracy.Design/Methodology/Approach: Using data from the fifth wave (2017– 2020) of the European Values Survey (EVS), a data-driven, systematic approach employing six ML methods is applied to predict individual tax morale across Europe. The importance of formal, informal and socio-de­mographic factors is assessed, and the study tests whether incorporating the Corruption Perception Index (CPI) improves predictive accuracy.Findings: The results indicate that ML methods and models can enhance understanding and prediction of individual tax morale in Europe. Among the deployed models, artificial neural networks (ANNs) achieved the highest accuracy. Accuracy increased across all ML methods when the CPI was included. Attitudes towards bribery, perceptions of immigrants’ im­pact on the national welfare system, and gender emerged as significant formal, informal and socio-demographic factors.Academic contribution to the field: The study offers a novel application of data-driven ML methods to the prediction of individual tax morale. Given the scarcity of empirical ML research in the social sciences, the find­ings provide valuable insights in a European context and may serve as a basis for further global research.Practical Implications: The conclusions are particularly relevant for gov­ernments and tax administrations seeking to improve tax compliance and revenue collection. In the European context, the results confirm the virtu­ous circle linking effective government performance, high tax morale and voluntary tax compliance—insights that are crucial for decision-makers, regulators, European institutions and tax-policy makers.Originality/Value: The findings confirm that, when ML methods are ap­plied, individual tax morale can be viewed as an outcome of interactions between formal and informal institutions. They also show that predictive accuracy is higher in countries with lower corruption, as indicated by a higher CPI.