Short-run and long-run performance of international tourism: Evidence from Bayesian dynamic models

Measuring the technical efficiency of the tourism industry is essential for evaluating tourism sustainability and reshaping tourism activities. This paper introduces for the first time a new dynamic stochastic frontier model to 1-measure and compare the short-run and long-run technical efficiencies of leading tourism destinations, and 2-provide impulse response functions and persistence measures to trace out the dynamic effect of shocks in technical inefficiency. We develop our model in a Bayesian framework using carefully constructed Markov Chain Monte Carlo (MCMC) techniques. We report efficiency results and persistence scores for individual destinations and discuss how different destinations recover from shocks in tourism performance.