Are the variables used in building composite indicators of well-being relevant? Validating composite indexes of well-being

• Published in: Ecological indicators 2014-11, Vol.46, p.575-585
• Main Authors: Otoiu, Adrian, Titan, Emilia, Dumitrescu, Remus
• Format: Article
• Language: English
• Subjects: Animal and plant ecology; Animal, plant and microbial ecology; Applied ecology; Assessments; Biological and medical sciences; Cluster analysis; Composite indicators; Conservation, protection and management of environment and wildlife; Construction; Data mining; Economic indicators; Fundamental and applied biological sciences. Psychology; GDP; General aspects; Happy Planet Index; HDI; Human; Indicators; Legatum Prosperity Index; Mathematical models; Optimization; Parks, reserves, wildlife conservation. Endangered species: population survey and restocking; Performance indices; Synecology; Well-being
• ISSN: 1470-160X; 1872-7034
• DOI: 10.1016/j.ecolind.2014.07.019

•Using variables that define well-being, we classify countries with cluster analysis.•We compare our classification with the ones from three major well-being indicators.•We validate classifications from UNDP's HDI and LPI, and invalidate the third (HPI).•We conclude the validity of cluster analysis for validating composite indicators.•Our approach can highlight country rank discrepancies from the two valid indicators. This paper explores the relevance of the variables that define well-being and human progress and makes a quantitative inquiry into the validity of three of the well-known and well-documented composite indicators of well-being: the Human Development Index (HDI), the Legatum Prosperity Index (LPI) and the Happy Planet Index (HPI). After choosing the key variables that describe most of the objective and subjective dimensions of well-being, we perform cluster analysis to come up with an optimal grouping of countries based on their multidimensional performance on well-being. A comparison of the classifications obtained with the three indexes invalidates the HPI, confirms results obtained for the HDI, and validates for the first time the LPI as a reliable measure of well-being. The optimal cluster structure yields robust results, which correct the rank discrepancies between the HDI and LPI for a large number of countries. It also proves that a robust ranking of countries based on multidimensional well-being can be achieved with a relatively small number of variables, which mitigates the risk of including variables that are not reliable and/or not available for a significant number of countries. The fact that cluster analysis generates results based on similarities between observations and not on computed values based on the aggregation of variables helps overcome problems that may occur due to the distribution of variables and increases its value as a validation method. Therefore, validation results achieved through cluster analysis are more robust and help to achieve a good check of the validity and relevance of the composite indexes, provide an objective perspective that can guide policy-makers and the public in making a fair assessment of actual levels of well-being, and avoid unfounded claims that may overstate it and delay or postpone measures to increase it.