RESUMO
COVID-19 disrupted international tourism worldwide, subsequently presenting forecasters with a challenging conundrum. In this competition, we predict international arrivals for 20 destinations in two phases: (i) Ex post forecasts pre-COVID; (ii) Ex ante forecasts during and after the pandemic up to end 2021. Our results show that univariate combined with cross-sectional hierarchical forecasting techniques (THieF-ETS) outperform multivariate models pre-COVID. Scenarios were developed based on judgemental adjustment of the THieF-ETS baseline forecasts. Analysts provided a regional view on the most likely path to normal, based on country-specific regulations, macroeconomic conditions, seasonal factors and vaccine development. Results show an average recovery of 58% compared to 2019 tourist arrivals in the 20 destinations under the medium scenario; severe, it is 34% and mild, 80%.
RESUMO
The aim of this article is to investigate the claim that tourism development can be the engine for poverty reduction in Kenya using a dynamic, microsimulation computable general equilibrium model. The article improves on the common practice in the literature by using the more comprehensive Foster-Greer-Thorbecke (FGT) index to measure poverty instead of headcount ratios only. Simulations results from previous studies confirm that expansion of the tourism industry will benefit different sectors unevenly and will only marginally improve poverty headcount. This is mainly due to the contraction of the agricultural sector caused the appreciation of the real exchange rates. This article demonstrates that the effect on poverty gap and poverty severity is, nevertheless, significant for both rural and urban areas with higher impact in the urban areas. Tourism expansion enables poorer households to move closer to the poverty line. It is concluded that the tourism industry is pro-poor.
