Weights and Importance in Composite Indicators: Mind the Gap

Abstract

Multidimensional measures (often termed composite indicators) are popular tools in the public discourse for assessing the performance of countries on human development, perceived corruption, innovation, competitiveness, or other complex phenomena. These measures combine a set of variables using an aggregation formula, which is often a weighted arithmetic average. The values of the weights are usually meant to reflect the variables importance in the index. This paper uses measures drawn from global sensitivity analysis, specifically the Pearson correlation ratio, to discuss to what extent the importance of each variable coincides with the intentions of the developers. Two nonparametric regression approaches are used to provide alternative estimates of the correlation ratios, which are compared with linear measures. The relative advantages of different estimation procedures are discussed. Three case studies are investigated: the Resource Governance Index, the Good Country Index, and the Financial Secrecy Index.