Abstract

In Italy, the measure of the Equitable and Sustainable Well-being is provided by the Italian Institute of Statistics by means of a dashboard of basic and composite indicators. To investigate the dependence structure between the different domains of well-being, we propose the use of Non-Parametric Bayesian Networks based on the normal copula distribution, that allow to explore the conditional independence relationships between the composite indicators. The main advantage of the non-parametric models is that, as opposed to the parametric approach, they do not require any assumption on the marginal distributions of the variables. The proposed model is applied to the Equitable and Sustainable Well-being indicators measured at the provincial level and enriches the analysis of well-being by inspecting similarities and differences between Italian urban areas and territories.

Highlights

  • Before the 1990s, the Gross Domestic Product (GDP) has been the only indicator accepted as a valid measure of a country’s progress

  • The need to go beyond GDP, developing a set of indicators taking into account the environmental and social aspects related to the quality of life

  • To deal with the theoretical issue of modeling a very complex multivariate dependence structure, we propose the use of Non-Parametric Bayesian Networks (NPBNs), as introduced in literature by Hanea et al (2006) and Kurowicka and Cooke (2006)

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Summary

Introduction

Before the 1990s, the Gross Domestic Product (GDP) has been the only indicator accepted as a valid measure of a country’s progress. In the final report of the European Commission for Measuring Economic Performance and Social Progress, known as “Stiglitz report” (Stiglitz et al 2009), the Commission’s objective was clearly that of identifying new relevant indicators, different than GDP, accounting for all dimensions of well-being. 134 basic indicators were identified, grouped in 12 domains: Health, Education and training, Work and life balance, Economic well-being, Social relationships, Politics and Institutions, Security, Subjective well-being, Landscape and cultural heritage, Environment, Innovation, research and creativity and Quality of services. In the last edition, according to the BES framework, 56 basic indicators have been taken into account, grouped in 11 dimensions of well-being, the same proposed at the regional level with the exception of the Subjective well-being domain. To take into account the multidimensionality of well-being, this study focuses on modeling the dependence relationships between the different BES domains at NUTS-3 level. The last section addresses some conclusions and open research problems

Basics on Bayesian Networks
Non‐Parametric Bayesian Networks
Pair Copula Construction and Regular Vines
Learning the NPBNs
Dependence Relationships Between Local BES Indicators
The Estimated BN Based on Composite Indicators
Conditioning
Conclusions
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