Modelling the Non-Linear Dependencies between Government Expenditures and Shadow Economy Using Data-Driven Approaches
Keywords:machine learning, shadow economy, government spending
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.
Alm, J., & Embaye, A. (2013). Using Dynamic Panel Methods to Estimate Shadow Economies Around the World, 1984–2006. Public Finance Review, 41(5), 510-543. http://dx.doi.org/10.1177/1091142113482353
Alm, J., Jackson, B., & McKee, M. (1992). Institutional uncertainty and taxpayer compliance. The American Economic Review, 82(4), 1018-1026.
ARS Progetti S.P.A., LATTANZIO Advisory, & AGRER. (2017). Extending coverage: social protection and the informal economy. Research, Network and Support Facility. Brussels.
Aruoba, S. B. (2010). Informal sector, government policy and institutions. Paper presented at the 2010 Meeting Papers from Society for Economic Dynamics.
Aziz, S., & Dowling, M. (2019). Machine learning and AI for risk management. In T. Lynn, Mooney, J., Rosati, P., Cummins, M. (Ed.), Disrupting finance (pp. 33-50): Palgrave Pivot, Cham. http://dx.doi.org/10.1007/978-3-030-02330-0_3
Berrittella, M. (2015). The effect of public education expenditure on shadow economy: A cross-country analysis. International Economic Journal, 29(4), 527-546. http://dx.doi.org/10.1080/10168737.2015.1081259
Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. Paper presented at the Proceedings of the fifth annual workshop on Computational learning theory.
Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140. http://dx.doi.org/10.1007/BF00058655
Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32. http://dx.doi.org/10.1023/A:1010933404324
Breiman, L., Friedman, J., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Brooks (Vol. 37). New York: Routledge. http://dx.doi.org/10.1201/9781315139470
Buehn, A., & Farzanegan, M. R. (2013). Impact of education on the shadow economy: Institutions matter. Economic Bulletin, 33, 2052-2063.
Cebula, R. J. (1997). An empirical analysis of the impact of government tax and auditing policies on the size of the underground economy: the case of the United States, 1973–1994. American Journal of Economics and Sociology, 56(2), 173-185. http://dx.doi.org/10.1111/j.1536-7150.1997.tb03459.x
Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree-boosting system. Paper presented at the Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
Dell’Anno, R. (2007). The Shadow Economy in Portugal: An Analysis with the MIMIC Approach. Approach. Journal of Applied Econometrics, 10(2), 253-277. http://dx.doi.org/10.1080/15140326.2007.12040490
Duncan, D., & Peter, S. K. (2014). Switching on the lights: do higher income taxes push economic activity into the shade? National Tax Journal, 67(2), 321-349. http://dx.doi.org/10.17310/ntj.2014.2.02
Gerxhani, K., & van de Werfhorst, H. (2013). The Effect of Education on Informal Sector Participation in a Post-Communist Country. European Sociological Review, 29, 446-476. http://dx.doi.org/10.1093/esr/jcr087
Guyon, I., Boser, B., & Vapnik, V. (1993). Automatic capacity tuning of very large VC-dimension classifiers. Advances in Neural Information Processing Systems, 147-155.
Hanousek, J., & Palda, F. (2004). Quality of government services and the civic duty to pay taxes in the Czech and Slovak Republics and other transition countries. Kyklos, 57(May), 237-252. http://dx.doi.org/10.1111/j.0023-5962.2004.00252.x
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: data mining, inference, and prediction: Springer Science & Business Media http://dx.doi.org/10.1007/978-0-387-84858-7
Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2(5), 359-366. http://dx.doi.org/10.1016/0893-6080(89)90020-8
Igor, F., & Schneider, F. (2017). Military expenditures and shadow economy in the Baltic States: Is there a link? Munich Personal RePEc Archive, (76194). Retrieved from https://mpra.ub.uni-muenchen.de/76194/
Ivașcu, C. F. (2021). Option pricing using Machine Learning. Expert Systems with Applications, 163(January), 113799. http://dx.doi.org/10.1016/j.eswa.2020.113799
Kelmanson, B., Kirabaeva, K., Medina, L., Mircheva, B., & Weiss, J. (2019). Explaining the shadow economy in Europe: size, causes and policy options: International Monetary Fund Working Paper.
Malaczewska, P. (2013). Useful government expenditure influence on the shadow economy. Quantitative Methods in Economics, XIV(2), 61-69.
Mara, E. R. (2021). Drivers of the shadow economy in European Union welfare states: A panel data analysis. Economic Analysis and Policy, 72(December), 309-325. http://dx.doi.org/10.1016/j.eap.2021.09.004
McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5(4), 115-133. http://dx.doi.org/10.1007/BF02478259
Medeiros, M. C., Vasconcelos, G. F., Veiga, Á., & Zilberman, E. (2021). Forecasting inflation in a data-rich environment: the benefits of machine learning methods. Journal of Business & Economic Statistics, 39(1), 98-119. http://dx.doi.org/10.1080/07350015.2019.1637745
Medina, L., & Schneider, F. (2018). Shadow economies around the world: what did we learn over the last 20 years? : International Monetary Fund. http://dx.doi.org/10.5089/9781484338636.001
Medina, L., & Schneider, F. (2019). Shedding light on the shadow economy: A global database and the interaction with the official one. Munich Society for the Promotion of Economic Research. http://dx.doi.org/10.2139/ssrn.3502028
Nachane, D. M. (2006). Econometrics: Theoretical foundations and empirical perspectives. New Delhi: Oxford University Press.
Pang, J., Li, N., Mu, H., & Zhang, M. (2021). Empirical analysis of the interplay between shadow economy and pollution: With panel data across the provinces of China. Journal of Cleaner Production, 285, 124864. http://dx.doi.org/10.1016/j.jclepro.2020.124864
Psychoyios, D., Missiou, O., & Dergiades, T. (2021). Energy based estimation of the shadow economy: The role of governance quality. The Quarterly Review of Economics and Finance, 80(May), 797-808. http://dx.doi.org/10.1016/j.qref.2019.07.001
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagation errors. nature, 323(6088), 533-536. http://dx.doi.org/10.1038/323533a0
Schneider, F. (2006). Shadow Economies and Corruption All over the World: What Do We Really Know? IZA Discussion Papers 2315. Institute of Labor Economics. Retrieved from IZA Discussion Papers website: https://www.iza.org/publications/dp/2315/
Schneider, F., Buchn, A., & Montenegro, C. E. (2010). New estimates for the shadow economies all over the world. International Economic Journal, 24(4), 443-461. http://dx.doi.org/10.1080/10168737.2010.525974
Schneider, F., & Enste, D. H. (2000). Shadow Economies: Size, Causes, and Consequences. Journal of Economic Literature, 38(1), 77-114. http://dx.doi.org/10.1257/jel.38.1.77
Schneider, F., & Enste, D. H. (2002). The shadow economy: Theoretical approaches, studies, and political implications. Cambridge: Cambridge University Press.
Schneider, F., & Williams, C. (2013). The shadow economy. Retrieved from http://dx.doi.org/10.13140/2.1.1324.1286
Slemrod, J. (2007). Cheating Ourselves: The Economics of Tax Evasion. The Journal of Economic Perspectives, 21(1), 25-48. http://dx.doi.org/10.1257/jep.21.1.25
Slemrod, J., & Weber, C. (2012). Evidence of the Invisible: Toward a Credibility Revolution in the Empirical Analysis of Tax Evasion and the Informal Economy. International Tax and Public Finance, 19(1), 25-53. http://dx.doi.org/10.1007/s10797-011-9181-0
Smola, A. J., & Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and computing, 14(3), 199-222. http://dx.doi.org/10.1023/B:STCO.0000035301.49549.88
Smuga, T., Burzyński, W., Karpińska-Mizielińska, W., Marzec, A., Niemczyk, J., & Ważniewski, P. (2005). Metodologia badań szarej strefy na rynku usług turystycznych. Warszawa: Instytut Koniunktur i Cen Handlu Zagranicznego.
Stulhofer, A. (1997). Sociocultural aspects of the informal economy - between opportunism and mistrust. Financial Practice, 21, 125-140.
Torgler, B., Schneider, F., & Schaltegger, C. (2010). Local autonomy, tax morale, and the shadow economy. Public Choice, 144, 293–321. http://dx.doi.org/10.1007/s11127-009-9520-1
Vapnik, V. (1995). The nature of statistical learning theory. New York: Springer. http://dx.doi.org/10.1007/978-1-4757-2440-0
Vapnik, V., Golowich, S. E., & Smola, A. J. (1997). Support vector method for function approximation, regression estimation, and signal processing. Cambridge: MIT Press, Cambridge.
Wu, D. F., & Schneider, F. (2019). Nonlinearity between the shadow economy and level of development: Institute of Labor Economics (IZA).
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