Olena Stryzhak

An assessment of a relationship between aggregated and partial indicators of the tourist sector development on one hand and other sectors of economy and society on other hand are investigated in the paper. Indicators are expressed in both direct monetary terms and rating scales. The study covers 149 countries in 2016. The results of the study indicate a positive relationship between the development of the tourist sector and the level of economic welfare of the country. This is explained by fact that tourist goods and services are subject of a second necessity and demand for they increases significantly with growth of income level. Found that there is a positive correlation between indicators of tourism sector development and indicators of quality of socio-economic environment. This may be due to fact that, on one hand, high quality socioeconomic environment stimulates visits to country; on other hand, travels stimulate growth of economy and welfare of citizens who live in territory that visited. Further analysis of aggregate of countries, divided into 4 groups by income, showed mixed results. Thus, a stronger correlation between all analyzed indicators is observed in low and high-income countries, weakening in transition to averages by a sample of values. However, such a weak relationship for some countries may be a consequence of a small share of the tourism sector in the economy. This paper differs from previous researches by focusing on a large sample of countries and reveals the relationship between tourism sector development indicators and socioeconomic environment indicators. One can speak of objectivity of results obtained, since they are confirmed both using Spearman’s correlation coefficient and Kendall’s Tau correlation.

The digital transformation of society affects socio-economic relations in all spheres of life; however, the degree of this influence differs depending on countries and regions. Various industries and sectors of the economy are also affected by digitalization to one degree or another.
In this context, the purpose of the paper is to determine the dependence of human capital in the tourism industry on the digitalization level of the economy.
The following methods were used: data standardization, cluster analysis, analysis of variance, K means, and SWOT analysis.
The panel sample includes indicators from 61 countries for 2018. The analysis revealed distinctive features that allowed allocating the countries into clusters. Cluster 1 (14 cases): countries with average Human Capital Index (HCI) and World Digital Competitiveness (WDC) values, depending on tourism. Cluster 2 (13 cases): countries with slightly above average HCI and WDC values that are less dependent on tourism. Cluster 3 (15 cases): countries with HCI and WDC values below average, not particularly dependent on tourism. Cluster 4 (1 case): outliers. Cluster 5 (18 cases): countries with above average HCI and WDC that are tourism dependent. The calculation results made it possible to identify the cluster principles. The use of the identified distinctive features in the SWOT analysis allows formulating the key elements of human capital strategies in the tourism sector for each group of countries.