Our aim is to evaluate the efficiency of tourist destinations at a global scale, considering 140 countries and drawing on World Economic Forum 2019 data. The approach follows three stages. First, we try to solve the problem of sample heterogeneity through cluster analysis to obtain homogeneous groups of countries. Second, we apply data envelopment analysis to evaluate countries’ efficiency as tourist destinations, considering a territorially based virtual production function which optimizes the flow of revenue from international tourism grounded on a set of inputs such as accommodation capacity, employment of tourist sector and volume of tourist arrivals. Finally, we identify which external factors might determine tourism efficiency by using bootstrap truncated regression analysis. We obtain two groups of countries which evidence differential levels of competitiveness. Rather than natural resources, cultural heritage in a broad sense seems to act as factor that enhances tourism efficiency.

我们的目标是，在考虑140个国家/地区并借鉴世界经济论坛2019年数据的情况下，在全球范围内评估旅游目的地的效率。该方法分为三个阶段。首先，我们尝试通过聚类分析来解决样本异质性问题，以获得同质的国家组。其次，我们使用数据包络分析来评估国家作为旅游目的地的效率，同时考虑基于区域的虚拟生产函数，该函数基于一系列输入（例如住宿能力，旅游部门的就业和旅游量）来优化国际旅游的收入流。游客的到来。最后，我们使用引导截断回归分析来确定哪些外部因素可能会决定旅游效率。我们获得了两组国家，这些国家的竞争水平存在差异。从广义上讲，文化遗产不是自然资源，而是可以提高旅游效率的因素。