STATISTICA & APPLICAZIONI
Six-monthly journal aimed at promoting research in the Methodological Statistics field
Statistica & Applicazioni is a six-monthly journal aimed at promoting research in statistical methodology and its original and innovative applications. Statistica & Applicazioni publishes research articles (and short notes) on theoretical, computational and applied statistics.
The journal is Open Access.
The journal was founded in 2003 by the following Departments belonging to different Italian Universities:
- Quantitative Methods - University of Brescia;
- Quantitative Methods for Business and Economic Sciences - University of Milano-Bicocca;
- Statistics - University of Milano-Bicocca;
- Information Technology and Mathematical Methods - University of Bergamo;
- Economics and Statistics - University of Calabria;
- «Silvio Vianelli» Mathematical and Statistical Sciences - University of Palermo;
- Statistics - Catholic University of the Sacred Heart, Milan.
At present the journal is supported by the following organizations:
- DMS StatLab - University of Brescia
- Department of Statistics and Quantitative Methods - University of Milano-Bicocca
- Department of Statistics - Catholic University of the Sacred Heart, Milan
- Department of Economics, Statistics and Finance - University of Calabria
- Department of Engineering - University of Bergamo
- Az.Agr.Case Basse of Gianfranco Soldera
- Indexed in: Scopus - Google Scholar - Current Index to Statistics - Ulrich's Periodicals Directory - SCImago Journal & Country Rank
- Available on: Torrossa - EBSCO Discovery Service
In this issue
Decomposition by sources, by subpopulations and joint decomposition by subpopulations and sources of Gini, Bonferroni and Zenga 2007 inequality indexes
by Igor Valli, Michele Zenga pages: 28 Free
Recently, the authors have illustrated the decompositions by subpopulations of the Gini (1914), Bonferroni (1930) and Zenga (2007) inequality measures. These decompositions were illustrated by a numerical example involving non-overlapping subpopulations and by a numerical example involving overlapping subpopulations. In the present paper we illustrate the decomposition by sources, the decomposition by subpopulations and the joint decomposition by subpopulations and sources of the three cited indexes. These decompositions are applied to data from the 2014 central bank of Italy sample survey on household income and wealth and are performed using the R package ineqJD.
There is a great discussion concerning the differences between the income of females and males. The purpose of the present study is to estimate the distribution of the ratio of female income over male income. The methodology to study the ratio in exam is based on the distribution of the ratio of two Dagum random variables with three parameters proposed in 2010 by Pollastri and Zambruno. We will consider the official statistics of income and we will estimate the parameters of the Dagum distribution. The distribution of the ratio in question studied in two different situations can reveal the gender inequality concerning income in different times, regions, and age classes.
This paper aims to assess the direction of changes in the distribution of per capita income as well as income inequalities in Polish households. The main trends of change were distinguished on the basis of theoretical distributions estimated for 2009-2018. However, a detailed comparative analysis of income distribution and the decomposition of income inequalities by major socio-economic groups was carried out for 2015 and 2018 (before and during the implementation of significant social programmes). In the article we used methods of modelling distribution of per capita income, including the Dagum distribution, Bhattacharyya distance, Gini coefficient and Theil index. All analyses
were performed on the basis of microdata from the Household Budget Survey of 2009-2018, carried out by Statistics Poland. The data set for each period consists in the observation of a minimum of 35 thousand households. The results of research indicate an increase in the level of per capita income in the period 2009-2018. The introduced changes in social policy have both resulted in an increase in the average level of income and a decrease in income inequality, especially in families with dependent children.
The sam’s global multiplier matrix as a ‘‘structural’’ inequality measure of personal income distribution
Aim of this paper is to introduce the ‘‘global multipliers matrix’’ as a ‘‘structural’’ inequality measure of personal income distribution. This measure is derived from the Social Accounting Matrix, considered as a linear model. The multiplier approach allows quantifying the different ways by which, an income equally earned by each Household’s group, turns into different disposable income levels through the three stages of spending, production and distribution. The resulting inequality in personal income distribution can be considered as the minimum inequality compatible with the given productive and spending structures, and hence as a result of the mechanism explicitly considered in the model. The multiplier matrix allows highlighting different features of personal income distribution and its inequality’s level. In particular: i) the extent to which each sector of activity contribute to the distribution of value added (primary income) over the different household groups; ii) how the composition of production factors’s ownership is linked to the intrahousehold inequality; iii) the impact of different fiscal and redistributive policies which translate into changes in disposable incomes of different household groups. This approach, we argue, allows for the assessment and evaluation of the effects of ‘‘new policies’’, aimed at reducing poverty and inequality ex-ante and not only ex-post. Some numerical example, referred to the Italian economy, allow quantifying how the different policies translate into different inequality levels. One meaningful result is that an exogenous injection in any account (Activities, Factors, Private Institutions) ends in benefiting the richest ones. In our example, the market and production shapes in Italy seem to have a very low power to generate income for the poorest Household groups. Inequality in the personal income distribution seems to be a structural feature of our system. Therefore it can be better assessed with the multiplier approach instead of using only traditional ‘‘synthetic’’ measures as Gini, Palma or Zenga indexes.
Modeling bivariate income and consumption distribution using copula function and reparameterized Dagum distribution
In this work, we propose to jointly analyse income and consumption distribution, using copula function to model the dependence structure, and the new formulation of Dagum distribution, proposed by Domma, Condino and Giordano (2018), to model the marginal distributions with a clear economic meaning of the marginal parameters. Indeed, this specification allow us to express the marginal distributions in terms of indicators of particular interest for the specific context and to evaluate the direct impact of some individual features on these indicators. Following the same criterion adopted for the marginals, we also reparameterize the copula function in terms of tail dependence measures, to have a tool for evaluating the direct impact of covariates on dependence. Preliminary results regarding data from Survey on Households Income and Wealth (SHIW) by bank of Italy are showed.
Across the healthcare landscape, it is now widely understood that Social Determinants Of Health (SDOH) have a major impact on health outcomes, care quality and medical costs. Individuals’ race, ethnicity, education, income level or geographical location often have more influence on their physical and mental health than clinical factors. Therefore, to improve their populations’ health local health authorities will have to make a concerted effort to better address these social determinants. A key aim of this research was to identify and investigate the principal conceptual frameworks of SDOH used in the current literature, and to contribute to the ongoing debate about practicable measures which could be used for monitoring purposes and so alert Countries, Regions and Small States to where action or further research should be focused. European, Italian and Marche Region’s data were used in analyses, with he relevant inequality measures applied to identify underprivileged populations in different territories.