Aggregating Composite Indicators through the Geometric Mean: A Penalization Approach
| Autor: | Francesca Mariani, Mariateresa Ciommi |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Computation, Vol 10, Iss 4, p 64 (2022) |
| Druh dokumentu: | article |
| ISSN: | 10040064 2079-3197 |
| DOI: | 10.3390/computation10040064 |
| Popis: | In this paper, we introduce a penalized version of the geometric mean. In analogy with the Mazziotta Pareto Index, this composite indicator is derived as a product between the geometric mean and a penalization term to account for the unbalance among indicators. The unbalance is measured in terms of the (horizontal) variability of the normalized indicators opportunely scaled and transformed via the Box–Cox function of order zero. The penalized geometric mean is used to compute the penalized Human Development Index (HDI), and a comparison with the geometric mean approach is presented. Data come from the Human Development Data Center for 2019 and refer to the classical three dimensions of HDI. The results show that the new method does not upset the original ranking produced by the HDI but it impacts more on countries with poor performances. The paper has the merit of proposing a new reading of the Mazziotta Pareto Index in terms of the reliability of the arithmetic mean as well as of generalizing this reading to the geometric mean approach. |
| Databáze: | Directory of Open Access Journals |
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