Examining Individual Tax Morale in Europe with Machine-Learning Methods

Aug 5, 2026

The research examines voluntary tax compliance by using machine-learning methods to predict individual tax morale across Europe. Data from the fifth wave (2017–2020) of the European Values Survey (EVS) supports a systematic, data-driven approach with six ML models. The study evaluates the role of formal, informal, and socio-demographic factors, and tests whether adding the Corruption Perception Index (CPI) improves predictive accuracy. Results show that ML can enhance prediction, with artificial neural networks performing best, and that accuracy rises when CPI is included.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages27

Reading Time1 h 8 min

AccessFree to download

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DOI: 10. 17573/cepar.2025. 1.05 1.01 Original scientific article Examining Individual Tax Moralein Europe with Machine-Learning Methods Lejla Lazović Pita University of Sarajevo, School of Economics and Business, Bosnia and Herzegovina [lejla.lazovic@efsa.unsa.ba](lejla.lazovic@efsa.unsa.ba) [https://orcid.org/0000-0001-9421-1842](https://orcid.org/0000-0001-9421-1842) Almasa Odžak University of Sarajevo, Faculty of Science, Bosnia and Herzegovina [almasa.odzak@pmf.unsa.ba](almasa.odzak@pmf.unsa.ba)[ ](almasa.odzak@pmf.unsa.ba)[https://orcid.org/0000-0001-6269-9759](https://orcid.org/0000-0001-6269-9759) Lamija Šćeta University of Sarajevo, School of Economics and Business, Bosnia and Herzegovina [lamija.sceta@efsa.unsa.ba](lamija.sceta@efsa.unsa.ba) [https://orcid.org/0000-0002-0410-0311](https://orcid.org/0000-0002-0410-0311) [Received: 3](Received: 3) . 12. 2024 Revised: 21. 2. 2025 Accepted: 24. 2. 2025 Published: 20. 5. 2025 ABSTRACT 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’ im- Lazović Pita, L., Odžak, A., Šćeta, L. (2025) . Examining Individual Tax Morale in Europe with Machine-Learning Methods. Central European Public Administration Review, 23(1), pp. 129–155 129 2591-2259 / This is an open access article under the CC-BY-SA license [https://creativecommons.org/licenses/by-sa/4.0/](https://creativecommons.org/licenses/by-sa/4.0/) Lejla Lazović Pita, Almasa Odžak, Lamija Šćeta pact 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. Keywords: corruption, EVS, individual tax morale, machine learning Preučevanje individualne davčne morale v Evropi z metodami strojnega učenja POVZETEK Namen: Raziskava proučuje in nadgrajuje vedenjsko literaturo o prostovoljnem izpolnjevanju davčnih obveznosti. Osredotoča se na uporabo in potencial metod ter modelov strojnega učenja za napo

FAQs

How does the study predict individual tax morale across Europe?

It uses data from the fifth wave (2017–2020) of the European Values Survey (EVS) and applies six machine-learning methods to predict individual tax morale.

What role does the Corruption Perception Index (CPI) play in the models?

Including the CPI increases predictive accuracy across all machine-learning methods, improving the understanding and prediction of tax morale.

Which machine-learning model achieved the highest accuracy?

Artificial neural networks (ANNs) delivered the highest accuracy among the deployed models.

Aug 5, 2026

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