In the last years, with the data revolution and the use of new technologies, phenomena are frequently described by a huge quantity of information useful for making strategic decisions. A priority for policymakers is having simple statistical tools useful to synthesize data. Such tools are represented by composite indicators (CIs). According to the glossary of statistical terms of OECD (The OECD-JRC handbook on practices for developing composite indicators. Paper presented at the OECD Committee on Statistics, 2004), OECD-JRC (Handbook on constructing composite indicators. Methodology and user guide, OECD, Paris, 2008), a CI is formed when manifest (observed) indicators are compiled into a single index, on the basis of an underlying model of the multi-dimensional concept that is being measured, and weights commonly represent the relative importance of each indicator. CIs are increasingly used for bench-marking countries’ performances and the methodological challenges raise a series of technical issues that, if not adequately addressed, can lead to CIs being misinterpreted or manipulated. Yet doubts are often raised about the robustness of the resulting countries’ rankings and about the significance of the associated policy message. In this paper, we propose a model-based approach for the construction of CIs with a hierarchical structure where the CIs (first and second order) are estimated using the Hierarchical Disjoint Non-Negative Factor Analysis (Cavicchia and Vichi in Hierarchical disjoint non-negative factor analysis. Manuscript submitted for publication, 2020) in a LS framework. In order to assess the methodology of construction of a CI, a set of properties is proposed and applied. Some well-known CIs, such as the Human Development Index and the Multidimensional Poverty index, are taken into consideration to show the importance of those properties. Therefore, we include into our proposal the most frequently used approaches in the literature of CIs, and we evaluate the model to assess their performances.
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