The Italian Regional Well-Being in a Multi-expert Non-additive Perspective

Abstract

This paper designs a multidimensional index of well-being for 20 Italian regions, based on a set of 41 indicators organized in an original hierarchical structure, a decision-tree whose four main pillars are Economy, Society, Environment and Health. Our novel approach combines the objective dimension of the evaluation (a comprehensive set of statistical indicators) within a flexible non-additive aggregation model (the Choquet integral) characterized with the preferences of informed Italian stakeholders. Adopting the Choquet integral allows us to overcome the well-known limitations embedded in the linear models, by assigning a weight (capacity) to any coalitions of dimensions, and by allowing a different degree of substitutability within each decision node in the tree. The weights and the parameters for the aggregation are elicited through a computer-based nominal group technique, a method which reduces the occurrences of drastically dissenting valuations and the potential expert-selection bias. Our results show that experts’ perception of synergies and redundancies is quite heterogeneous between levels and nodes in the tree. Moreover, well-being measures are much influenced by the degree of substitutability embedded in the experts’ preferences. Overall, the Italian picture looks more heterogeneous when analysed through the Choquet integral, with respect to a linear model.

Appendices

Appendix 1: Variables Description

Within the material-resources domain, we selected raw indicators of disposable equivalent household income per capita and of non-food household-expenditure for the “income-level” sub-node; for “inequality of resources” we adopted the Gini index, while for the “deprivation” sub-domain we used the Eurostat synthetic index of deprivation and the at-risk-of-poverty rate (with threshold fixed at 50% of median national disposable income). Within the “labour market” domain, the “unemployment” sub-domain includes rates of unemployment and long-term unemployment for the whole population; “society’s weakest” include indicators on women and youth labour-market participation; the “inactive” dimension includes the share of individuals not in employment, education or training. Within the “education” domain, the sub-node “scholarization” includes data on high-school and university degrees attainments, while “school accessibility” accounts for the school-dropouts as a share of the population aged 18–24. Within the “safety” domain, “road safety” is represented by the indicator on mortality for car accidents, while the “crime-related safety” includes both violent-crime and minor-crime indices. The “generalized social capital” is measured by the share of Italian “B-type” social cooperatives,¹⁶ the share of individuals aged 14+ who participate in cultural associations, the number of blood donors per 1.000 inhabitants and the 2008 general elections turnout. The “secondary social capital” encompasses indicators on the number of volunteering organizations per 10.000 inhabitants as well as the share of individuals aged 14+ who participated in volunteering activities. Finally, “primary social capital” includes data on the frequency of friends meeting in leisure time and on the share of individuals who provided free help to others. Within the “Environment” domain, the sub-node on “green cities” is an indicator of the green areas density in urban environments; “air pollution conditions” are measured through indicators of CO₂ emissions per capita and frequency of daily urban PM10 threshold trespassing; “waste-management” measures the share of recycling on total urban wastes. Within the Health domain, the “health inequality” node is constituted by a measure of inequality in morbidity between individuals in bad and in non-bad socioeconomic status, whose values go from 0 to 100, where 0 means low inequality (this measure is detailed in “Appendix 2”).

In the last two columns, benchmarks from a policy-target, are marked with a star “*”.

Appendix 2: The Health Inequality Index

The health inequality index is an average of six own-built inequality measures, showing the extent to which poor individuals are more (or less) prone than richer ones to be subjected to a number of risk-symptoms. The data used are regional micro-data on individuals medical and socio-economic status, from the survey PASSI (Progressi delle Aziende Sanitarie per la Salute in Italia), and cover six indicators (each one expressed as dichotomous variable: symptoms occurring vs non-occurring): diabetes, depression, smoking, scarce physical activity, drinking and bad diet. Data includes information on individuals economic conditions, through a question asking whether the respondent is able to make ends meet: (1) very easily; (2) rather easily; (3) rather hardly; (4) very hardly. We first generated a dichotomous variable for economic status (high or low conditions), joining together the categories (1) and (2), (3) and (4), respectively. For each symptom and each region, we then counted the individuals who declare to suffer from it, for each of the two economic status, thus obtaining a variable I_k,g (number of individuals affected by symptom k, in good economic conditions) and a variable I_k,b (number of individuals affected by symptom k, in bad economic conditions).

For each region i, we then computed the difference between I_k,g and I_k,b, and normalized it dividing it by the maximum observed difference across Italian regions, as in the following expression:

$$ HI_{k}^{i} = 1 - \frac{{\left| {I_{k,g}^{i} - I_{k,b}^{i} } \right|}}{{Max\left| {I_{k,g}^{i} - I_{k,b}^{i} } \right|}} $$

The kth Health Inequality index spans from a minimum of 0 (highest observed differences of k-symptom’s occurrence between socio-economic statuses) and 1 (lowest observed differences). The Health Inequality index included in the hierarchical tree is the simple average of the so-built six indices.

Appendix 3: Topographical Representation of Italian Regions

Appendix 4: Scenarios Submitted for Evaluation

See Tables 9, 10, 11, 12, 13, 14, 15, 16, 17 and 18.

Table 9 Decision matrix for the “Quality of life” node

Table 10 Decision matrix for the “Sustainability” node

Table 11 Decision matrix for the “Economy” node

Table 12 Decision matrix for the “Material resources” node

Table 13 Decision matrix for the “Labour market” node

Table 14 Example of a decision matrix with 9 scenarios made of four attributes (the “Society” node)

Table 15 Decision matrix for the “Education” node

Table 16 Decision matrix for the “Safety” node

Table 17 Decision matrix for the “Social Network” node

Table 18 Decision matrix for the “Environment” node

Appendix 5: Möbius Measures Elicited from Stakeholders

Each of the boxes in Table 19 report the Möbius measure elicited from the stakeholders for each Pillar and Domain in the hierarchical tree. Since each stakeholder has its own Möbius measures, we report here the average measures across the experts that were involved in each node’s evaluation. The sign of the Möbius measures can be interpreted as detailed in Sect. 4.1.

Table 19 Möbius measures (2-additive) elicited from the NGT sessions (pillars and major nodes in capital letters)

It is interesting to notice that the great majority of the interactions between dimensions¹⁷ have a positive sign, thus conveying the existence of complementarity (or “synergy”) between them. This kind of information is coherent with the analysis of the Orness measures already described in Sect. 6: strong complementarity (i.e., low substitutability) is detected between Health and Sustainability (the well-being node), Labour Market and Material Resources (the Economy Pillar), between indicators of Unemployment and of Society’s weakest employment conditions (within the Labour market Domain), between Generalised and Primary Social Capital (Social Capital Domain), as well as between Air quality and Waste management within the Environmental Pillar, and between all the dimensions of the Health Pillar. Conversely, distinct substitutability is detected between the Economy and the Social Pillar (within the Sustainability node), between Secundary and Primary Social Capital (within the Social Capital Domain), between Road and Crime safety (Safety Domain), as well as between Waste management and Green areas (within the Environmental Pillar).

Appendix F: Results for Well-Being Domains

See Table 20.

Table 20 Well-being domains indices (Choquet integral), year 2012