Composite Indicators for Measuring Well-Being of Italian Municipalities
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SAPIENZA UNIVERSITÀ DI ROMA Department of Social and Economic Sciences PhD Course in Applied Social Science, XXXI Curriculum in Research Methods for Socio-Economic Analysis Composite Indicators for Measuring Well-being of Italian Municipalities PhD Course’s Director: Prof. Antimo Luigi Farro Tutor: Prof. Filomena Maggino Student: dr. Matteo Mazziotta Accademic Year 2017/2018 2 INTRODUCTION .................................................................................................................... 6 PART I – THEORIES AND METHODS ........................................................................... 9 1. THEORETICAL FRAMEWORK: GDP VERSUS WELL-BEING ................................ 10 1.1 GDP: definition and uses .............................................................................................................. 10 1.2 Beyond GDP: scientific and political context ............................................................................... 13 1.3 The role of Italian studies ............................................................................................................... 17 1.4 GDP is not well-being: an application to real data ....................................................................... 28 2. COMPOSITE INDICATORS: THEORIES AND METHODS ....................................... 45 2.1 Manage the complexity ................................................................................................................. 45 2.2 Formative versus Reflective model ................................................................................................ 46 2.3 How to construct a composite indicator ......................................................................................... 49 2.3.1 Mission “Replace GDP” .............................................................................................................. 51 2.3.2 The use (good and bad) of the composite indicators .................................................................... 53 2.3.3 The “perfect” composite indicator does not exist ........................................................................ 54 2.3.4 The steps characterizing the composite indicators construction .................................................. 56 2.3.4.1 The definition of the phenomenon ......................................................................................... 56 2.3.4.2 The selection of the indicators .............................................................................................. 57 2.3.4.3 The normalization ................................................................................................................. 57 2.3.4.4 The aggregation .................................................................................................................... 64 2.3.4.5 The validation ....................................................................................................................... 74 2.4. Best practices ............................................................................................................................... 77 2.5. AMPI method ................................................................................................................................ 81 2.5.1 Method and formulas ................................................................................................................... 81 2.5.2 Properties and observations ........................................................................................................ 82 2.5.3 A variant for space-temporal comparisons .................................................................................. 85 2.5.4 Theoretical aspects ...................................................................................................................... 86 2.5.4.1 The positive penalty index .................................................................................................... 87 2.5.4.2 The negative penalty index ................................................................................................... 89 2.5.5 Consideration about the method ................................................................................................... 92 PART II – APPLICATION TO ADMINISTRATIVE DATA .................................... 94 3. ADMINISTRATIVE DATA SOURCES ............................................................................ 95 3.1 Introduction ................................................................................................................................... 95 3.2 Pros and cons ................................................................................................................................. 97 3.3 ARCHIMEDE ................................................................................................................................... 98 3 3.3.1 Data base “Labour” ..................................................................................................................... 99 3.3.2 Data base “Socio-economic conditions of the households” ....................................................... 100 3.3.3 Data base “Persons and Places” ................................................................................................. 105 4. WELL-BEING OF ITALIAN MUNICIPALITIES ........................................................ 110 4.1 Introduction ................................................................................................................................ 110 4.2 Domains, Individual indicators and composite indicators ............................................................ 111 4.2.1 Population and Households .................................................................................................. 115 4.2.2 Health .................................................................................................................................... 119 4.2.3 Education .............................................................................................................................. 121 4.2.4 Labour ................................................................................................................................... 125 4.2.5 Economic Well-being ............................................................................................................ 130 4.2.6 Environment .......................................................................................................................... 134 4.2.7 Economy on the territory ....................................................................................................... 139 4.2.8 Research and innovation ....................................................................................................... 143 4.2.9 Infrastructure and mobility.................................................................................................... 147 4.3 Analysis of the results ................................................................................................................. 148 CONCLUSIONS ................................................................................................................. 167 REFERENCES ................................................................................................................... 170 ANNEX I – THE REGRESSION TREES .................................................................... 174 4 5 Introduction Well-being is a complex phenomenon. Multidimensionality is recognized in literature as its main feature. This phenomenon is in some aspects elusive and difficult to monitor, and the definition is the combination of heterogeneous components, which assume different meanings in different contexts. A universally accepted definition of well-being does not exist (yet): each country (or areas) attributes importance to dimensions that for others may not be as relevant, consistent with their culture and social dynamics. Accurate measurement of well-being is a prerequisite for the implementation of effective welfare policies, which, through targeted actions in the most critical areas, are geared to the progressive improvement of living conditions. Until some time ago, such a plurality of components was poorly valued, believing that the only income dimension could represent in an exhaustive way such a complex reality. For many years, GDP (Gross Domestic Product) has been an indisputable landmark for states all over the world, playing the key role in defining, implementing and evaluating the effects of government action. Recently, the international debate has questioned the supremacy of GDP, and initiatives have been launched which, through the involvement of a growing number of countries, aim to develop alternative ways of measuring well-being that assign the same value to its components, Economic, Social and Environmental. Since well-being, as mentioned above, is a multidimensional phenomenon then it cannot be measured by a single descriptive indicator and that it should be represented by multiple dimensions. It requires, to be measured, the “combination” of different dimensions, to be considered together as components of the phenomenon (Mazziotta and Pareto, 2013). This combination can be obtained by applying methodologies known as composite indicators (Salzman, 2003; Mazziotta and Pareto, 2011; Diamantopoulos et al., 2008). In this ever-evolving scenario, the Italian experience is represented by the BES (Equitable and Sustainable Well-Being) project that