An Alternative Method of Component Aggregation for Computing Multidimensional Well-Being Indicators

This paper questions the validity of the statistical methods currently used in computing the composite indicators of well-being from their main subcomponents. The facts that most of the weights of the principal sub-components of the composite indicators are equal, that the determinants of well-being are correlated, and that the results are interpreted primarily in terms of country ranks, point out to the appropriateness of using a rank-based method for computing the composite indicators form their sub-indexes. A comparison of the actual ranks with ranks computed as averages of the ranks of sub-indexes for three well-known indicators of well-being, Human Development Index, Legatum Prosperity Index, and Social Progress Index, shows that results are almost the same. This calls into question the use of weighted averages of actual values of sub-components, as very high values for a sub-component increases a country’s relative rank, despite much lower performance on other sub-components, as in the case of USA and New Zealand. Our proposed approach helps achieve more robust/reliable rankings of countries and tackle the issues posed by extreme values or non-normal distributions of the sub-components variables used.