Factors Fostering Shadow Economy Performance in Poland and Lithuania during 2000–2019

Abstract

This study relies on a calculable and essential analysis of a statistically oriented regression model. Ninety-five variables taken into consideration in this research were grouped into four categories. The first category covers the general macroeconomic situation, the second is devoted to crime, the third is formed by characteristics of income and living conditions, and the fourth one applies to the taxation system. The Multiple Indicators Multiple Causes (MIMIC) model was employed to measure the level of shadow economy in Poland and in Lithuania during 2000–2019. The MIMIC model depends on Structural Equation Models. The MIMIC approach allows one to assess shadow economy as a latent variable. The observed factors are government employment/labor force, tax burden, subsides/GDP, social benefits paid by government/GDP, self-employment/GDP, and unemployment rate. The Pearson correlation index was used to size up the correlation between independent variables, and Kolmogorov–Smirnov (KS) test for normality of residuals was applied. In both countries, factors affecting the shadow economy performance show great similarity. The shadow economy development in Poland and in Lithuania is fostered by many different factors, related to, but not limited to, the general macroeconomic situation. In fact, the economic situation is associated with the standard of living, income as well as the crime rate. Important factors are associated with the taxation system. The results demonstrate that the regression model can be used to predict the shadow economy development and performance in Poland and in Lithuania. Such information facilitates taking adequate steps in order to minimize the shadow economy level in both countries. Such implications are very useful for decision makers in shaping the legal and economic progress in both countries.