Differently unequal: Zooming-in on the distributional dimensions of the crisis in euro area countries

This paper discusses how income inequality developed during the current crisis in euro area countries, as well as the role played by each income source. Based on an extended definition of income - including additional components which do not appear in the standard Eurostat definitions - we complement the information provided by the Gini index and quantile ratios by computing an alternative inequality indicator, developed by Zenga (2007), and its decomposition by income source. While broadly confirming the distributional effect of the crisis documented in previous studies, we find that in specific countries the level of inequality appears higher when alternative measures are taken into account, and that the rise of inequality since 2008 has not been as modest as the previous studies would suggest. The paper further looks at how the distribution of income has evolved during the crisis by income quantile groups (i.e. 'zooming-in'). The results point to varying contribution of labour income in 2011 compared to 2007. In addition, while the impact of individual households' characteristics shows a non-linear pattern across income quantile groups before the crisis, such dispersion has decreased in 2011.We argue that, on the basis of our analysis, not only euro area countries are "differently unequal" in that inequality has developed in a very peculiar way in different countries, but also because it needs to be tackled at a finer level of analysis

Differently unequal Zooming-in on the distributional dimensions of the crisis in euro area countries. LEQS Discussion Paper No. 86/2015 January 2015

This paper discusses how income inequality developed during the current crisis in euro area countries, as well as the role played by each income source. Based on an extended definition of income – including additional components which do not appear in the standard Eurostat definitions – we complement the information provided by the Gini index and quantile ratios by computing an alternative inequality indicator, developed by Zenga (2007), and its decomposition by income source. While broadly confirming the distributional effect of the crisis documented in previous studies, we find that in specific countries the level of inequality appears higher when alternative measures are taken into account, and that the rise of inequality since 2008 has not been as modest as previous studies would suggest. The paper further looks at how the distribution of income has evolved during the crisis by income quantile groups (i.e. ‘zooming-in’). The results point to varying contribution of labour income in 2011 compared to 2007. In addition, while the impact of individual households’ characteristics shows a non-linear pattern across income quantile groups before the crisis, such dispersion has decreased in 2011. We argue that, on the basis of our analysis, not only euro area countries are “differently unequal” in that inequality has developed in a very peculiar way in different countries, but also that it needs to be tackled at a finer level of analysis

Theoretical and quantitative issues on assessing well-being and quality of life

This PhD dissertation contains the results of a broad research carried out at the School of Social Sciences of the University of Genoa. Those contributions are related to the analysis of Well-being and Quality of Life and the indexes created to measure them, facing different issues and answering to diverse questions. Usually, especially until a decade ago, per-capita income has been adopted as a unique measure to quantify well-being, although it can represents it only partially. As per-capita income measures the total value of final goods and services produced within the borders of a country in a year, it focuses only on the economic dimension of well-being. It ignores other determinants of well-being, so the evaluation of the multidimensional nature of well-being is limited to the monetary dimension. The measurement of income, despite its incompleteness, still remains an indicator able to show the historical evolution of well-being between people and countries history. Actually, there are many forms of well-being, and before discussing the concrete policies to increase them, it is necessary to fully understand the many nuances of the concept. In order to do so, it is necessary to refer to both economic and social aspects, evaluating the interconnections between them. Only such an analysis allows to highlight an increasingly complex and detailed phenomenon. Thus, to go beyond the mere income-related aspect of well-being, it is crucial to consider well-being as a multidimensional phenomenon involving all aspects of people\u2019s lives. This is the reason why, after the financial crisis, which hit industrialized economies in the final part of the 2000s, \u201cQuality of Life\u2019\u2019 and \u2018\u2018Well-being\u2019\u2019, became very popular words and received the attention of policy makers and researchers. The first term is mainly used when one speaks at the level of individuals, whilst the second is more frequent when one speaks about communities, localities, and societies. Similarly, \u2018\u2018Well-being\u2019\u2019 refers rather to actual experience, and \u2018\u2018Quality of Life\u2019\u2019 to context and environments. However, in both cases, the terms are used with a broad range of meanings, and the ranges frequently overlap. However, this multidimensionality makes the assessment of Well-being and Quality of Life even more complex, because most of its dimensions are hard to identify and quantify and depend on subjective assessments. The aim of this work is to investigate and to find possible theoretical backgrounds and methods able to give an extensive, but at the same time organised, description of those phenomena, as well as a precise assessment. The first part (Chapter 1) of the work presents the topic under scrutiny, summarizing the main concepts and findings about multidimensional Well-being and its quantification. It goes through different frameworks, as well as the examination of a number of international well-being indicators familiar to the public audience. The subsequent three chapters instead, focus on measurement issues. Chapter 2 shows the construction of a composite multidimensional Well-being index for the European Union. It represents a comprehensive quantitative attempt to deal with the multidimensionality of the phenomenon. Chapter 3 is a step further, since, focusing on the European Union again, it tries to assess if the use of two different kind of aggregation methodologies, compensatory and non-compensatory, could create difficulties in quantify well-being. The final chapter, Chapter 4, deal with other two fundamental issues in the multidimensional well-being evaluation. Indeed, analysing data about Italian cities, the aim is to deal with the attribution of specific weights to dimensions and with intertemporal comparison

Statistics in the 150 years from Italian Unification. SIS 2011 Statistical Conference, Bologna, 8 – 10 June 2011. Book of Abstracts

Statistics in the 150 years from Italian Unification. SIS 2011 Statistical Conference, Bologna, 8 – 10 June 2011. Book of Abstract

Statistics in the 150 years from Italian Unification. SIS 2011 Statistical Conference, Bologna, 8 – 10 June 2011. Book of Abstracts

Statistics in the 150 years from Italian Unification. SIS 2011 Statistical Conference, Bologna, 8 – 10 June 2011. Book of Abstract

Comparative Analysis of Student Learning: Technical, Methodological and Result Assessing of PISA-OECD and INVALSI-Italian Systems .

PISA is the most extensive international survey promoted by the OECD in the field of education, which measures the skills of fifteen-year-old students from more than 80 participating countries every three years. INVALSI are written tests carried out every year by all Italian students in some key moments of the school cycle, to evaluate the levels of some fundamental skills in Italian, Mathematics and English. Our comparison is made up to 2018, the last year of the PISA-OECD survey, even if INVALSI was carried out for the last edition in 2022. Our analysis focuses attention on the common part of the reference populations, which are the 15-year-old students of the 2nd class of secondary schools of II degree, where both sources give a similar picture of the students

ASA 2021 Statistics and Information Systems for Policy Evaluation

This book includes 25 peer-reviewed short papers submitted to the Scientific Opening Conference titled “Statistics and Information Systems for Policy Evaluation”, aimed at promoting new statistical methods and applications for the evaluation of policies and organized by the Association for Applied Statistics (ASA) and the Department of Statistics, Computer Science, Applications DiSIA “G. Parenti” of the University of Florence, jointly with the partners AICQ (Italian Association for Quality Culture), AICQ-CN (Italian Association for Quality Culture North and Centre of Italy), AISS (Italian Academy for Six Sigma), ASSIRM (Italian Association for Marketing, Social and Opinion Research), Comune di Firenze, the SIS – Italian Statistical Society, Regione Toscana and Valmon – Evaluation & Monitoring

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available