Targeting Policies for Multidimensional Poverty and Social Fragility Relief Among Migrants in Italy, Using F-FOD Analysis

In this paper, we apply the novel Fuzzy First-Order Dominance (F-FOD) methodology to rank migrant subpopulations in Lombardy (Italy), in terms of multidimensional poverty and social fragility, for the year 2014, with the purpose to possibly provide useful support to policy-makers, in targeting relief interventions from poverty and discomfort. The F-FOD methodology allows for the direct comparison of different distributions of poverty and fragility, assessed by means of suitable ordinal multi-indicator systems, so extending to this more complex setting, the usual univariate first-order dominance criterion. It also provides complimentary “incomparability” scores, to assess to what extent the final rankings are reliable or instead forcing. It turns out that the levels of poverty and fragility of migrant subpopulations are quite different and, in particular, that the time since migrations has a key impact, on the identification of most critical cases, which typically involve recently migrated people. Evidence also emerges that the temporal poverty/fragility trajectories of migrants, distinguished by country of origin, follow different paths, suggesting how policy interventions must be properly, and differently, tuned to be effective

Higher order assortativity in complex networks

Assortativity was first introduced by Newman and has been extensively studied and applied to many real world networked systems since then. Assortativity is a graph metrics and describes the tendency of high degree nodes to be directly connected to high degree nodes and low degree nodes to low degree nodes. It can be interpreted as a first order measure of the connection between nodes, i.e. the first autocorrelation of the degree-degree vector. Even though assortativity has been used so extensively, to the author's knowledge, no attempt has been made to extend it theoretically. This is the scope of our paper. We will introduce higher order assortativity by extending the Newman index based on a suitable choice of the matrix driving the connections. Higher order assortativity will be defined for paths, shortest paths, random walks of a given time length, connecting any couple of nodes. The Newman assortativity is achieved for each of these measures when the matrix is the adjacency matrix, or, in other words, the correlation is of order 1. Our higher order assortativity indexes can be used for describing a variety of real networks, help discriminating networks having the same Newman index and may reveal new topological network features.Comment: 24 pages, 16 figure

On the decomposition by sources of the Zenga 1984 point and synthetic inequality indexes

In this work we provide a decomposition by sources of the inequality index ζ defined by Zenga (1984). The contributions of the sources are obtained with the method proposed in Zenga et al. (2012) and Zenga (2013), that allows to compare different inequality measures. This method is based on the decomposition of the inequality curves. To apply this decomposition to the index ζ and its inequality curve, we adapt the method to the cograduation table. Moreover, we consider the case of linear transformation of the sources and analyse the corresponding result

Ranking extraction in ordinal multi-indicator systems

In this paper, we present a procedure for scoring and ranking statistical units in ordinal multi-indicator systems, by integrating classical dimensionality reduction tools and novel results in Partial Order Theory. Units are ranked based on “dominance” scores, which depend upon both the structure of the partial order and the joint frequency distribution. Dominance scores are complemented with scores of incomparability among units, so to assess the ranking quality. The procedure is computationally light and is here applied to data about financial literacy in Ital

The Graphical Representation of Inequality

As of the past century, the analysis and the graphical representation of inequality play a very important role in economics. In the literature, several curves have been proposed and developed to simplify the description of inequality. The aim of this paper is a review and a comparison of the most known inequality curves, evaluating the features of each, with a particular focus on interpretation.Desde el siglo pasado el análisis y representación gráfica de la desigualdad juega un papel importante en la economía. En la literatura varias curvas han sido propuestas y desarrolladas para simplificar la descripción de la desigualdad. El objetivo de este artículo es revisar y comparar las curvas de la desigualdad más conocidas evaluando sus características y enfocándose en su interpretació

Posetic Tools in the Social Sciences: A Tutorial Exposition

In this chapter, we provide a brief outline of the main motivations why partial order theory is of key importance in the statistical analysis of socio-economic data, presenting some of the more recent tools available for practical applications. We focus in particular on four typical problems in the analysis of socio-economic data, namely: (i) the construction of rankings, (ii) the evaluation of multidimensional latent traits, like deprivation, (iii) the comparison of statistical populations scored on multi-indicator systems and (iv) the measurement of multidimensional ordinal inequality. The exposition has a didactic aim; statistical procedures are sketched avoiding technical details, but discussing simple examples and providing the relevant software code, in the R language

Application of Zenga’s distribution to a panel survey on household incomes of European Member States

In this paper Zenga’s distribution is applied to 114 household incomes distributions from a panel survey conducted by Eurostat. Previous works showed the good behaviour of the model to describe income distributions and analyzed the possibility to impose restrictions on the parametric space so that the fitted models comply with some characteristics of interest of the samples. This work is the first application of the model on a wide number of distributions, showing that it can be used to describe incomes distributions of several countries. Maximum likelihood method on grouped data and methods based on the minimization of three different goodness of fit indexes are used to estimate parameters. The restriction that imposes the equivalence between the sample mean and the expected value of the fitted model is also considered. It results that the restrictionshould be used and the changes in fitting are analyzed in order to suggestwhich estimation method use jointly to the restriction

Sustainable Development: Actual Trends on Synthetic Indicators, Non-aggregative and Configurational Approaches

[EN] Sustainable development is key for the fundamental challenges of humanity. The use of non-aggregative approaches can be attractive when trying to understand the relationships of humanity with both nature and society. Qualitative Comparative Analysis (QCA) is a method that merges the advantages of qualitative and quantitative methodologies and identifies patterns of conditions that are necessary or sufficient for explaining an outcome. Partially ordered set (poset) theory is a branch of mathematics through which tools that allow dealing with multidimensional systems of ordinal data are obtained. Assessing well-being and development requires sharing a conceptual framework on its determinants, as well as on society, and needs from the identification of the most consistent and effective methodologies to build indicators that are easily understood by society. Sustainable development is an increasingly interesting issue whose academic development can be improved thank to the use of these methodologies, in its combination of environmental and socio-economic concerns.Roig-Tierno, N.; Arcagni, A. (2021). Sustainable Development: Actual Trends on Synthetic Indicators, Non-aggregative and Configurational Approaches. Social Indicators Research. 157(1):1-7. https://doi.org/10.1007/s11205-021-02658-yS17157

Smooth Backfitting with R

The Smooth Backfitting Estimator (SBE) for additive models increases the estimation performances of the classical Backfitting Estimator. While for Backfitting many code has been proposed no usable programs for SBE are available. In this paper the packagesBF for Smooth Backfitting using the Nadaraya-Watson estimator is presented. Some simulations are provided in order to test the proposed program. The manual of package sBF, including its functions, is given at the end of the paper