Examining Individual Tax Morale in Europe with Machine-Learning Methods
DOI:
https://doi.org/10.17573/cepar.2025.1.05Keywords:
corruption, EVS, individual tax morale, machine learningAbstract
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.
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References
Abedin, M. Z. et al. (2020). Tax default prediction using feature transformation-based machine learning. IEEE Access, 9, pp. 19864–19881. https://doi.org/10.1109/ACCESS.2020.3048018
Allingham, M.G. and Sandmo, A. (1972). Income tax evasion: A theoretical analysis. Journal of public economics, 1(3-4), pp. 323–338. http://doi.org/10.1016/0047-2727(72)90010-2
Alm, J. (2014). Expanding the theory of tax compliance from individual to group motivations. In F. Forte et al., eds., A Handbook of Alternative Theories of Public Economics. Edward Elgar Publishing, pp. 260–277.
Alm, J. and Gomez, J. L. (2008). Social capital and tax morale in Spain. Economic Analysis and Policy, 38(1), pp. 73–87.
Alm, J. and Malézieux, A. (2021). 40 years of tax evasion games: a meta-analysis. Experimental Economics, 24(3), pp. 699–750. https://doi.org/10.1007/s10683-020-09679-3
Alm, J. and Torgler, B. (2006). Culture differences and tax morale in the United States and in Europe. Journal of economic psychology, 27(2), pp. 224–246. https://doi.org/10.1016/j.joep.2005.09.002
Alm, J., Martinez-Vazque, J. and Torgler, B. (2006). Russian attitudes toward paying taxes–before, during, and after the transition. International Journal of Social Economics 33(12), pp. 832–857. https://doi.org/10.1108/03068290610714670
Alm, J., Martinez-Vazquez, J. and McClellan, C. (2016). Corruption and firm tax evasion. Journal of Economic Behavior & Organization, 124, pp. 146–163. https://doi.org/10.1016/j.jebo.2015.10.006
Alm, J., Sanchez, I. and De Juan, A. (1995). Economic and noneconomic factors in tax compliance. Kyklos, 48(1), pp. 3–18. https://doi.org/10.1111/j.1467-6435.1995.tb02312.x
Andreoni, J., Erard, B. and Feinstein, J. (1998). Tax compliance. Journal of economic literature, 36(2), pp. 818–860.
Athey, S. (2018). The impact of machine learning on economics. In A. Agrawal, J. Gans and A. Goldfarb, eds., The economics of artificial intelligence: An agenda. University of Chicago Press, pp. 507–547.
Barone, G. and Mocetti, S. (2011). Tax morale and public spending inefficiency. International Tax and Public Finance, 18, pp. 724–749.
Becker, G.S. (1968). Crime and punishment: An economic approach. Journal of political economy, 76(2), pp. 169–217.
Castañeda, V. M. (2021). Tax Equity and its Association with Fiscal Morale. International Public Management Journal, 24(5), pp. 710–735.
Chen, S., Yang, M. and Lin, Y. (2022). Predicting happiness levels of European immigrants and natives: An application of Artificial Neural Network and Ordinal Logistic Regression. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.1012796
Cung, N. H. (2019). Impact of economic freedom index and corruption perceptions index on corporate income tax revenue in Vietnam. European Scientific Journal, 15(28), pp. 185–196. https://doi.org/10.19044/esj.2019.v15n28p1
Cyan, M.R., Koumpias, A.M. and Martinez-Vazquez, J. (2016). The determinants of tax morale in Pakistan. Journal of Asian Economics, 47, pp. 23–34.
Davidescu, A.A. et al. (2022). Could Religiosity and Religion Influence the Tax Morale of Individuals? An Empirical Analysis Based on Variable Selection Methods. Mathematics, 10(23), p. 44–97. https://doi.org/10.3390/math10234497
Dobrovič, J., Rajnoha, R. and Šuleř, P. (2021). Tax evasion in the EU countries following a predictive analysis and a forecast model for Slovakia. Oeconomia Copernicana, 12(3), pp. 701–728. https://doi.org/10.24136/oc.2021.023
Doerrenberg, P. et al. (2014). Nice guys finish last: Do honest taxpayers face higher tax rates? Kyklos, 67(1), pp. 29–53. https://doi.org/10.1111/kykl.12042
Dulleck, U. et al. (2016). Tax compliance and psychic costs: Behavioral experimental evidence using a physiological marker. Journal of Public Economics, 134, pp. 9–18.
EVS, 2020. European values study 2017: Integrated dataset (EVS 2017). GESIS Data Archive, Cologne. ZA7500 data file version 4.0.0. At <https://doi.org/10.4232/1.13560>, accessed May 2023.
Feld, L.P. and Frey, B.S. (2002). Trust breeds trust: How taxpayers are treated. Economics of governance, 3, pp. 87–99.
Frey, B.S. and Torgler, B. (2007). Tax morale and conditional cooperation. Journal of comparative economics, 35(1), pp. 136–159. https://doi.org/10.1016/j.jce.2006.10.006
García, G.A., Azorín, J.D.B. and de la Vega, M.D.M.S. (2018). Tax evasion in Europe: An analysis based on spatial dependence. Social Science Quarterly, 99(1), pp. 7–23. https://doi.org/10.1111/ssqu.12382
Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. O’Reilly Media, Inc.
Gerstenblüth, M. et al. (2009.) Threats in Latin American and Caribbean countries: how do inequality and the asymmetries of rules affect tax morale?. Documento de Trabajo/FCS-DE; 14/08. https://doi.org/10.2139/ssrn.1489393
Gerstenblüth, M et al. (2012). How do inequality affect tax morale in Latin America and Caribbean?. Revista de Economía del Rosario, 15(2), pp. 123–135.
GESIS Leibniz Institute for the Social Sciences (2023). EVS-Bibliographs. GESIS Data Archive, Cologne. At <https://www.gesis.org/en/european-values-study>, accessed 4 July 2024.
Hofmann, E. et al. (2017). Tax compliance across sociodemographic categories: Meta-analyses of survey studies in 111 countries. Journal of Economic Psychology, 62, pp. 63–71. https://doi.org/10.1016/j.joep.2017.06.005
Horodnic, I. A. (2018). Tax morale and institutional theory: a systematic review. International journal of sociology and social policy, 38(9/10), pp. 868–886.
Hsu, H. Y. (2023). Fiscal transparency and tax morale: is the relationship shaped by perceptions of government performance and corruption? International Review of Administrative Sciences, pp. 1–18. https://doi.org/10.1177/00208 523231220599
Jahnke, B. and Weisser, R. A. (2019). How does petty corruption affect tax morale in Sub-Saharan Africa? European Journal of Political Economy, 60, p. 101751. https://doi.org/10.1016/j.ejpoleco.2018.09.003
James, S. et al. (2019). How seriously do taxpayers regard tax evasion? A survey of opinion in England. Journal of Money Laundering Control 22(3), pp. 563– 575.
Kharitonova A.E. (2023). Forecasting the tax burden of agricultural enterprises by machine learning methods. Taxes and Taxation, 4, pp. 28–38. http://doi.org/10.7256/2454-065X.2023.4.43917
Kountouris, Y. and Remoundou, K. (2013). Is there a cultural component in tax morale? Evidence from immigrants in Europe. Journal of Economic Behavior and Organization, 96, pp. 104–119.
Lago-Peñas, I. and Lago-Peñas, S. (2010). The determinants of tax morale in comparative perspective: Evidence from European countries. European Journal of Political Economy, 26(4), pp. 441–453. https://doi.org/10.1016/j.ejpoleco.2010.06.003
Lee, J.A. (2020). Artificial Neural Networks in Public Policy: Towards an Analytical Framework. Doctoral dissertation. George Mason University
Lewis, A. (1982). The psychology of taxation. Oxford: Martin Robertson.
Listhaug, O. and Miller, A.H. (1985). Public Support for Tax Evasion: Self-interest or Symbolic Politics? European Journal of Political Research, 13(3), pp. 265– 282. http://doi.org/10.1111/j.1475-6765.1985.tb00123.x
Lubian, D. and Zarri, L. (2011). Happiness and tax morale: An empirical analysis. Journal of Economic Behavior & Organization, 80(1), pp. 223–243.
Luttmer, E.F. and Singhal, M. (2014). Tax morale. Journal of economic perspectives, 28(4), pp. 149-168.
Molero, J.C. and Pujol, F. (2012). Walking inside the potential tax evader’s mind: tax morale does matter. Journal of Business Ethics, 105, pp. 151–162.
Murorunkwere, B. F. et al. (2023). Predicting tax fraud using supervised machine learning approach. African Journal of Science, Technology, Innovation and Development, 15(6), pp. 731–742. https://doi.org/10.1080/20421338.2023.2187930
OECD (2013). Bribery and Corruption Awareness Handbook for Tax Examiners and Tax Auditors. OECD Publishing, Paris. https://doi.org/10.1787/9789264205376-en.
OECD (2019). Tax Morale: What Drives People and Businesses to Pay Tax? OECD Publishing, Paris. https://doi.org/10.1787/f3d8ea10-en
OECD/CAF/ECLAC (2018). Latin American Economic Outlook 2018: Rethinking Institutions for Development. OECD Publishing, Paris. https://doi.org/10.1787/leo-2018-en.
Rahman, R. A., Masrom, S. and Omar, N. (2019). Tax avoidance detection based on machine learning of Malaysian government-linked companies. International Journal of Recent Technology and Engineering, 8(2), pp. 535– 541. http://doi.org/10.35940/ijrte.B1083.0982S1119
Rahman, R. A. et al. (2020). An application of machine learning on corporate tax avoidance detection model. IAES International Journal of Artificial Intelligence, 9(4), pp. 721–725. http://doi.org/10.11591/ijai.v9.i4.
Riahi-Belkaoui, A. (2004). Relationship between tax compliance internationally and selected determinants of tax morale. Journal of international accounting, auditing and taxation, 13(2), pp. 135–143.
Richardson, G. (2006). Determinants of tax evasion: A cross-country investigation. Journal of international Accounting, Auditing and taxation, 15(2), pp. 150–169.
Ryšavá, T. and Zídková, H. (2021). What are the factors of tax evasion? New findings in the EVS Study. Review of Economic Perspectives, 21(4), pp. 385– 409. https://doi.org/10.2478/revecp-2021-0017
Sá, C., Martins, A. and Gomes, C. (2013). Tax morale, occupation and income level: An analysis of portuguese taxpayers. Comunicações.
Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, pp. 85–117. https://doi.org/10.1016/j.neunet.2014.09.003
Schmölders, G. (1970). Survey research in public finance: A behavioral approach to fiscal theory. Public finance, 25(2), pp. 300–306.
Schneider, F., Raczkowski, K. and Mróz, B. (2015). Shadow economy and tax evasion in the EU. Journal of Money Laundering Control, 18(1), pp. 34–51.
Strümpel, B. (1969). The contribution of survey research to public finance. In A. T. Peacock, ed., Quantitative Analysis in Public Finance. New York: Praeger Publishers, pp. 14–32.
Torgler, B. (2003). To evade taxes or not to evade: that is the question. The Journal of Socio Economics, 32(3), pp. 283–302. https://doi.org/10.1016/S1053-5357(03)00040-4
Torgler, B. (2005). Tax morale in Latin America. Public choice 122(1-2), pp. 133–157.
Torgler, B. (2006). The importance of faith: Tax morale and religiosity. Journal of economic Behavior & organization, 61(1), pp. 81–109. https://doi.org/10.1016/j.jebo.2004.10.007
Torgler, B. (2012). Tax morale, eastern Europe and European enlargement. Communist and Post-Communist Studies, 45(1-2), pp. 11–25. https://www. jstor.org/stable/48609657
Torgler, B. and Schneider, F. (2005). Attitudes towards paying taxes in Austria: An empirical analysis. Empirica, 32(2), pp. 231–250. https://doi.org/10.1007/s10663-004-8328-y
Torgler, B. and Schneider, F. (2007). What shapes attitudes toward paying taxes? Evidence from multicultural European countries. Social Science Quarterly, 88(2), pp. 443–470. https://www.jstor.org/stable/42956304
Torgler, B. and Valev, N.T. (2010). Gender and public attitudes toward corruption and tax evasion. Contemporary Economic Policy, 28(4), pp. 554–568. https://doi.org/10.1111/j.1465-7287.2009.00188.x
Transparency International. (2022). Corruption Perception Index.
Vargas-Hernández, J.G. (2009). The multiple faces of corruption: Typology, forms and levels. In A. Stachowicz-Stanusch, ed., Organizational immunity to corruption: Building Theoretical and Research Foundations. Information Age Publishing Inc.: Charlotte, pp. 111–117.
Weber, P. et al. (2018). Prospect for knowledge in survey data: an artificial neural network sensitivity analysis. Social Science Computer Review, 36(5), pp. 575– 590. https://doi.org/10.1177/0894439317725836
Yamen, A. et al. (2018). Impact of institutional environment quality on tax evasion: A comparative investigation of old versus new EU members. Journal of International Accounting, Auditing and Taxation, 32, pp. 17–29. https:// doi.org/10.1016/j.intaccaudtax.2018.07.001
Yitzhaki, S. (1974). A note on income tax evasion: A theoretical analysis. Journal of public economics, 3, pp. 201–202. http://doi.org/10.1016/0047- 2727(74)90037-1
Yu, H.F., Huang, F.L. and Lin, C.J. (2011). Dual coordinate descent methods for logistic regression and maximum entropy models. Machine Learning, 85, pp. 41–75. https://doi.org/10.1007/s10994-010-5221-8
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