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Il libro descrive e affronta alcune questioni tutt’ora aperte nel campo della costruzione di indicatori compositi. Si propone come complemento a riferimenti consolidati quali OECD handbook of composite indicators, e approfondisce temi che saranno certo oggetto di ampio sviluppo nel prossimo futuro, soprattutto nell’ambito degli indicatori di benessere e progresso umano. La prima parte del libro illustra lo stato dell’arte delle metodologie per la costruzione di indicatori compositi mentre la seconda parte affronta specifici nodi critici piu’ recenti. Il libro fornisce strumenti utili sia a ricercatori con una conoscenza limitata degli indicatori compositi, che a studiosi interessati ad un aggiornamento su temi più avanzati.

Most of the socio-economic phenomena such as development, well-being, and quality of life have a multidimensional nature and require the definition of a set of individual indicators to be properly assessed. Often, individual indicators are summarized and a composite index is created. One of the main problems in constructing composite indices is the choice of a method which allows time comparisons. In this paper, we consider the Adjusted Mazziotta–Pareto Index, a non-compensatory composite index used by the Italian National Institute of Statistics for measuring “Equitable and Sustainable Well-being” in Italy. An empirical comparison with some traditional non-compensatory indices is presented and an Influence Analysis is, for the first time, performed in order to assess the robustness of the index.

The Italian wine industry is going through, during a general economic crisis, a period of strong growth and expansion in the world markets. The quality of products, characterized by tradition and innovation, has defeated the involution of the Italian entrepreneurial system. The variety of wines produced represents the peculiarities of the Italian territory. The aim of this paper is to measure a multidimensional phenomenon, the “Enological vocation” of the Italian provinces, through the use of a composite index. The results highlight both areas of the territory for which it is known the strong vocation and less known areas where you need to do further investigation.

The measurement of well-being of people is very difficult because it is characterized by a multiplicity of aspects or dimensions. Principal Components Analysis (PCA) is probably the most popular multivariate statistical technique for reducing data with many dimensions and, often, well-being indicators are reduced to a single index of well-being by using PCA. However, PCA is implicitly based on a reflective measurement model that is not suitable for all types of indicators. In this paper, we discuss the use and misuse of PCA for measuring well-being, and we show some applications to real data.

Composite indices are being more and more used to measure multidimensional phenomena in social sciences. Considerable attention has been devoted in recent years to the methodological issues associated with index construction, such as non-compensability and comparability of the data over time. The aim of this paper is to compare two non-additive approaches: the Mazziotta–Pareto Index (MPI) and the Weighted Product (WP) method. The MPI is a nonlinear composite index that rewards the units with ‘balanced’ values of the individual indicators. The WP method implicitly penalizes the ‘unbalance’ and allows building, for each unit, two closely interrelated composite indices: a ‘static’ index for space comparisons and a ‘dynamic’ index for time comparisons. The MPI entails an equal weighting of the indicators, and only relative time comparisons are allowed. The indices based on the WP method give more weight to low values, and allow for both absolute and relative time comparisons. An application to indicators of w...

The aim of this work is to provide a tool for the measurement of the health infrastructure endowment based on the assumption of “non-substitutability” of the indicators (a compensation among them is not allowed). The proposed approach, called “Method of Penalties by Coefficient of Variation”, normalizes the indicators by a specific criterion that deletes the unit of measurement and the variability effect and uses, as aggregation function, an adjusted mean by a penalty coefficient that is function, for each unit, of the indicators’ variability in relation to the mean. The obtained index is easily computable, interpretable and comparable over time. As an example of application, we consider a set of indicators of endowment of advanced medical equipment.

Il tema della sintesi di diversi indicatori in un indicatore rappresentativo dell’insieme dei fenomeni considerati è un tema ben noto in letteratura, soprattutto nel campo delle statistiche sociali. Spesso, tuttavia, la sintesi viene effettuata senza l’introduzione di alcuna ponderazione, dunque assumendo implicitamente che i diversi fenomeni considerati abbiano tutti lo stesso rilievo e possano quindi “entrare” nel procedimento di sintesi in termini paritetici. Questa assunzione, pur comprensibile per la difficoltà di evitare il rischio dell’arbitrarietà, potrebbe tuttavia condurre a risultati distorti, o quanto meno non completamente affidabili, dato che nella realtà difficilmente i fenomeni da sintetizzare possono essere posti su un livello paritetico. La questione, peraltro, si complica quando si è in presenza di una doppia dimensione da sintetizzare: quella settoriale (nel lavoro qui presentato, le diverse categorie infrastrutturali) e quella territoriale (qui, le province italiane). Al problema  in particolare, a quello della costruzione di un indicatore di sintesi della dotazione infrastrutturale a livello di diverse unità territoriali  sono state date soluzioni diverse, tutte più o meno basate sulla variabilità, settoriale e/o territoriale, quale elemento da assumere per la determinazione dei pesi da introdurre eventualmente nell’algoritmo di sintesi. Il lavoro che qui si presenta si pone l’obiettivo di mettere a confronto alcuni degli approcci a tal fine utilizzati e di verificare sia l’eventuale convergenza sia il grado di robustezza (e dunque di affidabilità) dei risultati ottenuti. The synthesis of different indicators expressed in only one representative measure of a set of phenomena is a subject known in literature, especially in the social statistics. Frequently the synthesis is calculated without weighting, or rather it is assumed, implicitly, that the phenomena considered have the same importance and they can be considered on the same level. This assumption reduces the risk of the arbitrariness but it could lead biased, or not completely reliable, results. However the issue is more complicated when there are two dimensions to summarize: the sectorial dimension (in this paper, the different infrastructural categories) and the territorial dimension (the Italian provinces). The problem  in particular, computing composite indicators of infrastructural endowment  is dealt with different approaches based on the sectorial and territorial variability in order to determine the weights for a set of elementary indicators. The aim of this paper is to compare some approaches, to verify the convergence and the robustness (reliability) of the results obtained.

The measurement of development or poverty as multidimensional phenomena is very difficult because there are several theoretical, methodological and empirical problems involved. The literature of composite indicators offers a wide variety of aggregation methods, all with their pros and cons. In this paper, we propose a new, alternative composite index denoted as MPI (Mazziotta-Pareto Index) which, starting from a linear aggregation, introduces penalties for the countries or geographical areas with ‘unbalanced’ values of the indicators. As an example of application of the MPI, we consider a set of indicators in order to measure the Millennium Development Goals (MDGs) and we present a comparison between HDI (Human Development Index) methodology, HPI (Human Poverty Index) methodology and MPI.