Does Culture Make a Better Citizen? Exploring the Relationship Between Cultural and Civic Participation in Italy

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JRC Statistical Audit of the Individual Deprivation Measure

The Individual Deprivation Measure (IDM) is a gender-sensitive, multidimensional measure of poverty. The measure assesses deprivation at the individual level, in relation to 15 key dimensions of life, namely Food, Water, Shelter, Health, Education, Energy/fuel, Sanitation, Relationships, Clothing, Violence, Family planning, Environment, Voice, Time- Use and Work. It offers information additional to other national surveys, providing a high level summary of deprivation through an index while enabling users to gain further understanding through the decomposition and disaggregation of the scalar, gender-sensitive, individual-level data on which it is based. European Commission’s Competence Centre on Composite Indicators and Scoreboards (COIN) at the Joint Research Centre (JRC) was invited by the International Women’s Development Agency (IWDA) to audit the IDM study concerning the Fiji (2014-17) dataset. In this dataset, composed by almost three thousands subjects, only 13 out of 15 key dimensions of the IDM are considered. The statistical audit presented herein aims to contribute to ensuring the transparency of the IDM methodology and the reliability of the results. The report touches upon data quality issues, the conceptual and statistical coherence of the framework and the impact of modelling assumptions on the results.JRC.I.1-Monitoring, Indicators & Impact Evaluatio

The JRC Statistical Audit of the Social Progress Index (SPI)

A spur of social progress is enabling people to fulfil their own potential and in doing so the capability of the society they are a part of. The Social Progress Index (SPI) is an international monitoring framework for measuring social progress without resorting to the use of economic indicators. It provides a basis to understand the relationship between economic and social progress and measures country performance on aspects of social and environmental performance. The Social Progress Index builds on three dimensions: Basic Human Needs, Foundations of Wellbeing and Opportunity. These dimensions establish the basis of the framework and are used to aggregate 51 social outcome indicators organized in 12 components into a single summary measure. The statistical audit discussed in this report was conducted by the European Commission’s Joint Research Centre, and it aims at maximizing the reliability and transparency of the Social Progress Index. The audit focuses on the statistical coherence and the impact of key modelling assumptions used in the SPI framework. The statistical audit of the SPI should enable policy analysts and researchers alike to draw more relevant and well-targeted conclusions regarding inclusive growth strategies that benefit everyone at all levels of economic development.JRC.I.1-Modelling, Indicators and Impact Evaluatio

JRC Statistical Audit of the 2020 Commitment to Reducing Inequality index

The 2020 Commitment to Reducing Inequality Index (CRII) is a multidimensional index which ranks 159 countries for their policy performance across three pillars covering public services, progressive taxation and labour rights. The statistical audit presented herein was performed by the European Commission’s Joint Research Centre and aims to contribute to ensuring the transparency of the index methodology and the reliability of the results. The report touches upon data quality issues, the conceptual and statistical coherence of the framework and the impact of modelling assumptions on the results. The analysis suggests that meaningful inferences can be drawn from the index. The CRII is reliable and with a statistically coherent framework. CRII ranks are shown to be representative of a plurality of scenarios and robust to changes in the aggregation method and pillar weights. Nonetheless the good statistical properties of the CRI index, some suggestions are made for possible refinements.JRC.I.1-Monitoring, Indicators & Impact Evaluatio

The JRC Statistical Audit of the Retail Restrictiveness Indicator

Monitoring the restrictiveness of regulations governing the retail companies may help to induce a positive dynamic leading to more open and competitive retail markets in the EU. The Commission services have developed for the first time the monitoring framework for the Retail Restrictiveness Indicator, which is made of 14 types of restrictions, two sub-pillars, two pillars and one overall index across the 28 EU Member States. This exercise inevitably entails both conceptual and practical challenges. The statistical audit discussed in this note was conducted by the European Commission’s Joint Research Centre (JRC), and it aims at maximising the reliability and transparency of the Retail Restrictiveness Indicator framework. It should enable policy analysts and researchers alike to draw more relevant, meaningful and useful conclusions from the results presented in the Staff Working Document accompanying the Commission Communication on a European retail sector fit for the 21st century. The present statistical assessment of the Retail Restrictiveness Indicator focuses on two main aspects: • The statistical coherence of the indicator framework, and; • The impact of key modelling assumptions on the overall scores and ranks. This JRC analysis complements the reported Retail Restrictiveness Indicator results for the EU Member States – namely those for the two main pillars, the Establishment restrictions and Operations restrictions - with estimated confidence intervals, in order to better appreciate the robustness of the results to key modelling choices (such as choice of the weights and the aggregation formula).JRC.I.1-Modelling, Indicators and Impact Evaluatio

Joint Research Centre Statistical Audit of the 2018 Global Attractiveness Index

Attractiveness is a prerequisite and a symptom for competitiveness and it is valued both because it implies a nation’s ability to attract talent, capital and assets (know-how, technologies, and other), and because more in general it stimulates the whole process of economic and social development. The European House - Ambrosetti has developed an international monitoring framework – the Global Attractiveness Index (GAI) – that measures a country’s attractiveness as determining element of its ability to be competitive and to grow. The GAI builds on four attributes of attractiveness: Openness, Innovation, Efficiency, and Endowment. These pillars are used to organise and aggregate 21 Key Performance Indicators (KPIs) into a single summary measure for 144 countries that altogether cover approximately 93% of the world’s population and 99% of Gross Domestic Product (in US$) worldwide. This framework inevitably entails both conceptual and practical challenges. The statistical audit discussed in this note was conducted by the European Commission’s Joint Research Centre, and it aims at maximising the reliability and transparency of the Global Attractiveness Index. It should enable policy analysts and researchers alike to draw more relevant, meaningful and useful conclusions on good practices and challenges that countries face in today’s competitive game to business and job creation.JRC.I.1-Modelling, Indicators and Impact Evaluatio

Partial Order Theory for Synthetic Indicators

Given a big set of data with several variables, the aim is the evaluation of each unit with a method that produces a synthetic measure describing a complex and non-observable concept; this goal is achieved respecting the characteristics of the variables, specially the measurement scale. The information gathered with partially ordered sets (poset) reflects this aim, because posets depends only on the order relations among the observations, and allows to handle ordinal and dichotomous variables fairly. In this setting, the vector of variables observed on a unit is handled as a unique entity called profile and not as a group of different variables that need to be aggregated. Starting from recent developments in poset theory, this thesis is organized in three parts. The first proposes to obtain a unique indicator combining the values given by the severity measures for evaluation, derived from the fuzzy identification method. The second contribution is the HOGS (Height Of Groups by Sampling) procedure, which is aimed to estimate the average rank of groups of units of a big population. HOGS is a step forward the statistical estimation of the average rank of a profile; furthermore it allows the estimation of the effect of external variables on the synthetic measure. The last results are two new R functions: the first computes the approximated average rank for large data sets overcoming the usual sample sizes considered by the software usually used until now, the second implements the information given by the frequency of profiles in the computation of approximated average rank, making its use more profitable for social sciences.Data una grande popolazione osservata su diverse variabili, ci si pone l'obiettivo di valutare le singole unità con un metodo che sia in grado di produrre una informazione sintetica per la descrizione di un concetto complesso e non osservabile; in questa tesi si vuole raggiungere questo scopo rispettando le caratteristiche dei dati, specialmente la scala di misura di questi. Gli insiemi parzialmente ordinati (poset) si adattano a questo scopo; questo tipo di insiemi sono costruiti unicamente sulle relazioni d'ordine tra le osservazioni e quindi consentoto di trattare le variabili ordinali e dicotomiche in modo adeguato alle loro caratteristiche. Nella letteratura dei poset, il vettore di variabili osservate su una unità è chiamato profilo e trattato come un oggetto unico senza procedure di aggregazione. Questa tesi si connette ai più recenti sviluppi nella teoria dei poset ed è organizzata in tre parti principali. La prima propone una sintesi dell'informazione fornita dalle misure di severity, derivate dal metodo di fuzzy identification. Il secondo e principale contributo è la procedura HOGS (Height OF Groups by Sampling), che ha lo scopo di stimare l'average rank di gruppi di unità da grandi popolazioni. HOGS permette di avvicinarsi alla stima statistica dell'average rrank dei singoli profili ed inoltre fornisce un metodo per studiare l'effetto di variabili esterne sulla misura sintetica. L'ultima parte contiene le funzioni che sono state sviluppate in R: la prima calcola l'average rank approssimato per grandi moli di dati, la seconda implementa l'informazione data dalle frequenze dei signoli profili nella popolazione osservata, rendendo questo metodo più spendibile nelle scienze sociali

Partial Order Theory for Synthetic Indicators

Given a big set of data with several variables, the aim is the evaluation of each unit with a method that produces a synthetic measure describing a complex and non-observable concept; this goal is achieved respecting the characteristics of the variables, specially the measurement scale. The information gathered with partially ordered sets (poset) reflects this aim, because posets depends only on the order relations among the observations, and allows to handle ordinal and dichotomous variables fairly. In this setting, the vector of variables observed on a unit is handled as a unique entity called profile and not as a group of different variables that need to be aggregated. Starting from recent developments in poset theory, this thesis is organized in three parts. The first proposes to obtain a unique indicator combining the values given by the severity measures for evaluation, derived from the fuzzy identification method. The second contribution is the HOGS (Height Of Groups by Sampling) procedure, which is aimed to estimate the average rank of groups of units of a big population. HOGS is a step forward the statistical estimation of the average rank of a profile; furthermore it allows the estimation of the effect of external variables on the synthetic measure. The last results are two new R functions: the first computes the approximated average rank for large data sets overcoming the usual sample sizes considered by the software usually used until now, the second implements the information given by the frequency of profiles in the computation of approximated average rank, making its use more profitable for social sciences.Data una grande popolazione osservata su diverse variabili, ci si pone l'obiettivo di valutare le singole unità con un metodo che sia in grado di produrre una informazione sintetica per la descrizione di un concetto complesso e non osservabile; in questa tesi si vuole raggiungere questo scopo rispettando le caratteristiche dei dati, specialmente la scala di misura di questi. Gli insiemi parzialmente ordinati (poset) si adattano a questo scopo; questo tipo di insiemi sono costruiti unicamente sulle relazioni d'ordine tra le osservazioni e quindi consentoto di trattare le variabili ordinali e dicotomiche in modo adeguato alle loro caratteristiche. Nella letteratura dei poset, il vettore di variabili osservate su una unità è chiamato profilo e trattato come un oggetto unico senza procedure di aggregazione. Questa tesi si connette ai più recenti sviluppi nella teoria dei poset ed è organizzata in tre parti principali. La prima propone una sintesi dell'informazione fornita dalle misure di severity, derivate dal metodo di fuzzy identification. Il secondo e principale contributo è la procedura HOGS (Height OF Groups by Sampling), che ha lo scopo di stimare l'average rank di gruppi di unità da grandi popolazioni. HOGS permette di avvicinarsi alla stima statistica dell'average rrank dei singoli profili ed inoltre fornisce un metodo per studiare l'effetto di variabili esterne sulla misura sintetica. L'ultima parte contiene le funzioni che sono state sviluppate in R: la prima calcola l'average rank approssimato per grandi moli di dati, la seconda implementa l'informazione data dalle frequenze dei signoli profili nella popolazione osservata, rendendo questo metodo più spendibile nelle scienze sociali

Use of poset theory with big datasets: a new proposal applied to the analysis of life satisfaction in Italy

The aim of this work is to propose a tool for measuring a complex concept, and to apply it to big sets of data measured on ordinal and/or dichotomous scales. An important field of application are subjective data, that are often based on opinions or personal evaluations. Many national and international surveys employ this kind of data, measured among thousands of individuals. Thanks to the use of the average rank (AR) as a synthetic measure of a complex concept, we believe that poset theory could be a very useful approach for dealing with ordinal data avoiding the use of scaling procedures. Because classic poset approaches are at their best when applied to few data at a time, our idea is based on a procedure for sampling units from a big population using a simple criterion to summarize the resulting values appropriately. Applying the central limit theorem enables a comparison of the results obtained from different groups using statistical tests on the means. We used our Height Of Groups by Sampling (HOGS) method to compare the average rank among groups that are defined by one or more socio-demographic variables influencing the level of the complex concept we wish to measure. The application of the HOGS procedure to life satisfaction in Italy generated convincing results, revealing significant differences between regions, genders and levels of formal education. We compared the results given by HOGS with the non linear principal component analysis and obtained an easy readable output with convincing precision and accuracy; we are confident that the HOGS procedure can be applied to many other concepts investigated in the social science