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In the last years, there has been a growing interest in composite indices, whether they be social, socio-economic or environmental indices. In this paper, we propose a new approach to the composite indices construction which consists in computing an interval of possible values, for each statistical unit, rather than a single value. The interval is called ‘performance interval’ and it is constructed depending on the level of compensability of individual indicators. As an example of application, we considered a set of indicators of well-being in Italy and we constructed the performance intervals for the Italian Regions. Finally, we compared the midpoint of the performance intervals with the geometric mean, a classic partially compensatory aggregation function.

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.

Composite indices for measuring multidimensional phenomena have become very popular in a variety of economic, social and policy domains. The literature offers a wide range of aggregation methods, all with their pros and cons. The most used are additive methods, but they imply requirements and properties which are often not desirable or difficult to meet. For example, they assume a full substitutability among the components of the index: a deficit in one dimension can be compensated by a surplus in another. In this paper, we consider a non-compensatory composite index for spatial comparisons and its variant for spatio-temporal comparisons. A study of the aggregation function is, for the first time, presented and its main properties are formally defined. As an example of application, a set of individual indicators of well-being for OECD countries is considered and a comparison between the two approaches is provided, in order to show what they offer and how they work.

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...

In recent years, the debate on the measurement of multidimensional phenomena has caused, within the worldwide scientific Community of developed countries, a renewed interest. It is common awareness that a number of socio-economic phenomena cannot be measured by a single descriptive indicator and that, instead, they should be represented with a multiplicity of aspects or dimensions. Phenomena such as development, progress, poverty, social inequality, well-being, quality of life, etc., require, to be measured, the ‘combination’ of different dimensions, to be considered together as components of the phenomenon (Mazziotta and Pareto 2013). In fact, the complex and multidimensional nature of these phenomena requires the definition of intermediate objectives whose achievement can be observed and measured by individual indicators. The mathematical combination (or aggregation as it is termed) of a set of indicators that represent the different dimensions of a phenomenon to be measured can be obtained by applying methodologies known as composite indicators or composite indices (Saisana and Tarantola 2002; Salzman 2003; OECD 2008).

It has long been accepted that the GDP per capita cannot alone explain the well-being in a geographical area. Several have been the attempts to construct alternative, non-monetary, indices of well-being by aggregating a variety of individual indicators that represent different dimensions of well-being. The most famous, in Italy, are the Index of Regional Quality of Development and the “Il Sole 24 Ore” Quality of Life Index. An issue often not solved, from a methodological point of view, concerns the comparability of the data over time. In this work, we propose a ‘static’ and a ‘dynamic’ well-being measure based on the application of the Jevons index to the socio-economic indicators. The obtained indices are closely related and allow synthetic spatial and temporal comparisons of the level of well-being.