A Bayesian Analysis of Tourism on Shadow Economy in ASEAN Countries

Abstract

AbstractTourism contributes to boost a country’s economic growth, improve foreign currency supply, and engender employment and business opportunities in many ASEAN countries. Previous studies suggest that tourism can be associated with the shadow economy. Hence, the current paper examines the impact of tourism on the informal economy in the ASEAN countries from 1999 to 2017. To accomplish the research objective, we utilize the Bayesian linear regression technique in order to analyze collected data. Specifically, we apply a normal prior distribution, while the posterior distribution is generated using the Markov Chain Monte Carlo (MCMC) method via the Gibbs sampler algorithm. The main finding of the paper is that tourism represented by tourism expenditures, tourism receipts, and tourist arrivals positively influence the shadow economy size. Moreover, the posterior probability of tourism receipts and tourist arrivals is higher than that of tourism expenditures. Other factors that also promote the shadow economy are the government size and tax burden. Indeed, we find extreme evidence for the impact of these factors on informality in all models. Conversely, GDP per capita, trade openness, rule of law, and corruption control adversely affect the shadow economy in the ASEAN countries between 1999–2017.KeywordsASEANTourismShadow economyBayesian approach