A Study on the OECD Better Life Index Using Multivariate Statistical Analysis

Abstract

One of the more interesting indexes used for the measurement of the quality of life was launched in 2011, and having catered for more than 100,000 people from 180 countries, covers 11 topics dealing with living conditions and quality of life such as: housing, jobs, work-life balance, income, health, safety, education, community, civic engagement, environment and life satisfaction. The OECD Better Life Index, next to the most used GDP index, reveals inequalities between people from different socioeconomic spheres (groups), disparities in well-being and other very important economic phenomena. This paper presents statistical analysis based on Better Life Index data (2017). The author applied multivariate statistical analysis, namely correspondence analysis and classification methods for grouping countries into subgroups of countries with a similar level of well-being and life satisfaction. As a result, the study presents classification and visualization (dendrogram and perceptual map) of countries with a similar level of quality of life in terms of the importance of the 11 characteristics consisting of 24 variables that define well-being. The study shows that multivariate statistical methods can be used as an alternative for the classical measurement proposed by the OECD. All the calculations are conducted using the R software (www.r-project.org).(original abstract)