Corruption, Shadow Economy, FDI, and Tax Revenue in BRICS: A Bayesian approach

Abstract

The paper aims to test the hypothesis that corruption, shadow economy, and foreign direct investment (FDI) affect the BRICS economies' tax revenue collection. To accomplish this aim, we collected data from the Transparency International, World Development Indicators, and Worldwide Governance Indicators over the period 2001-2017. The Bayesian linear regression method simulated by the Monte Carlo Markov Chain (MCMC) technique through the Gibbs sampling algorithm is applied to uncover empirical re-sults. The study indicates that the control of corruption has a strong positive impact on tax collection. Meanwhile, the shadow economy's size has a nonlinear relationship with the BRICS countries' tax revenue. Notably, this is an inverted U-shaped relationship. As long as the informality does not ex-ceed the turning point, the shadow economy positively affects tax revenue collection. When the shadow economy's size is larger than this turning point, any further increase in the shadow economy can decrease the BRICS countries' tax revenue collection. Moreover, the results show that the size of FDI has a strong negative effect on these countries' tax revenue. We also find that GDP per capita and agriculture adversely affect tax collection, although their negative impact probabilities are different. In contrast, the Governance Index has a relatively strong positive effect on the BRICS econ-omies' tax revenue collection.