This article aims to model the relationship between the size of the shadow economy and the most important government expenditures respectively social protection, health, and education, using nonlinear approaches. We applied four different Machine Learning models, namely Support Vector Regression, Neural Networks, Random Forest, and XGBoost on a cross-sectional dataset of 28 EU states between 1995 and 2020. Our goal is to calibrate an algorithm that can explain the variance of shadow economy size better than a linear model. Moreover, the most performant model has been used to predict the shadow economy size for over 30,000 simulated combinations of expenses in order to outline some possible inflection points after which government expenditures become counterproductive. Our findings suggest that ML algorithms outperform linear regression in terms of R-squared and root mean squared error and that social protection spending is the most important determinant of shadow economy size. Further to our analysis for the 28 EU states, between 1995 and 2020, the results suggest that the lowest size of shadow economy occurs when social protection expenses are greater than 20% of GDP, health expenses are greater than 6% of GDP, and education expenses range between 6% and 8% of GDP. To the best of the authors' knowledge, this is the first paper that used ML to model shadow economy and its determinants (i.e., government expenditures). We propose an easy-to-replicate methodology that can be developed in future research.

In an era of ongoing global development and increasing focus on sustainability, the underground economy persistently identifies novel areas for expansion. This dynamic growth compels states to engage in an ongoing search for effective strategies to regulate and mitigate its effects, while simultaneously addressing the broader implications for economic development. Poverty, financial development, income inequality, and legal framework are analyzed in this paper in relation to the underground economy for the European Union member states between 2004 and 2022 by way of a panel model. Our findings reveal a positive relationship between the levels of poverty among individuals and the underground economy. Conversely, the interplay between the underground economy and both financial development and the legal framework exhibits a detrimental effect. In conclusion, our analysis reveals that the three primary variables examined—poverty, financial development, and legal framework—substantially influence the scale of the underground economy, with important implications for overall economic development and sustainable growth.