Social capital and the shadow economy: a Bayesian analysis of the BRICS

Purpose - The paper aims to examine the impact of social capital on the size of the shadow economy in the BIRCS countries over the period 1995–2014. Design/methodology/approach - The authors employ the Bayesian linear regression method to uncover the relationship between social capital and the shadow economy. The method applies a normal distribution for the prior probability distribution while the posterior distribution is determined using the Markov chain Monte Carlo technique. Findings - The results indicate that the unemployment rate and tax burden positively affect the size of the shadow economy. By contrast, corruption control and trade openness are negatively associated with the development of this informal sector. Moreover, the paper’s primary finding is that social capital represented by social trust and tax morale can hinder the size of the shadow economy. Research limitations/implications - This study is limited to the case of the BRICS countries for the period 1995-2014. The determinants of the shadow economy in different groups of countries can be heterogeneous. Moreover, social capital is a multidimensional concept that may consist of various components. This difficulty of measuring the social capital calls for further research on the relationship between other dimensions of social capital and the shadow economy. Originality/value - Many studies investigate the effect of economic factors on the size of the shadow economy. This paper applies a new approach to discover the issue. Notably, the authors use the Bayesian linear regression method to analyze the relationship between social capital and the shadow economy in the BRICS countries.