by Michele Zenga
A new inequaliy curve I(p) based on the ratio between the lower mean M(p) and the upper mean þ M+(p) is proposed. By averaging I(p) the new inequality index I is obtained. The index I satisfies the usual properties required to an inequality measure. Being U(p)=1-I(p) a ratio between two arithmetic means, the meaning of I(p) is very straightforward. The index I is related to the Gini index R and the Bonferroni index B by the relation: R <= B <= I . In section 6 the curves I(p) and the Lorenz curve L(p) for the N = 14026 personal incomes, regarding the Bank of Italy survey on the household expenditure, are reported.
by Stéphane Mussard, María Noel Pi Alperin
This article extends the paper of Dagum C. and Costa M. (2004). We further develop the study of
multidimensional poverty using fuzzy sets by introducing a mixture of decomposition analysis. The
model yields the most relevant dimensions of poverty (health, education, etc.) and the most relevant
sub-groups (areas, gender, etc.) in order to identify the main forces that contribute to the overall
amount of the state of poverty. These results are useful for decision makers that contemplate socioeconomic
policies in favour of poverty reduction. Finally, we apply this decomposition to study the
level of poverty of Argentina in 1998.
by Eugenio Brentari, Silvia Golia, Marica Manisera
The paper compares two models to construct measures from the responses on a set of categorical
variables, the Rasch Model and the Nonlinear (Categorical) Principal Component Analysis, and
can be considered as a part of the literature about the choice between stochastic and algorithmic
models. The aim is to discuss the Rasch Model and Nonlinear PCA differences and similarities, emphasizing
the information that can be drawn from the data, and to compare the resulting measures.
by Pietro Giorgio Lovaglio
The aim of the present paper is to generalize the definition of Human Capital (HC) as unidimensional
latent variable (LV) to the case of bidimensional LV composed by an ‘‘Educational dimension’’
and a ‘‘Working experience dimension’’ underlying the process of determination and accumulation
of earned Income and capital Income. In particular we propose an extended version of the statistical
definition of HC (part 2), coherent with the economic theory and major studies that have proposed
HC indicators (part 3); then, in order to overcome the limits of classical approaches, an estimation
method that provides standardized LV (part 4) and an actuarial approach that obtains HC scores
in monetary values are proposed (part 6) and applied to the estimation of the Italian household
HC in 2000 (part 5). Then, to investigate relations between HC and other relevant economic variables
for household, a structural model is specified and estimated (part 7). Final section draws major
conclusions (part 8).
by Francesca Greselin, Walter Maffenini
The sample mean difference Â is an unbiased estimator of Gini’s mean difference A. It is well
known that Â is asymptotically normally distributed (Hoeffding, 1948). In order to obtain confidence
intervals for A, Â must be standardized and hence its variance Var(Â) must be estimated. In
this paper we study the effective coverage of the confidence intervals for A, when using a specific
unbiased estimator ^ Var(Â) for the variance of Â, in a non-parametric framework. The empirical
determination of the minimum sample size required to reach a good approximation of the nominal
coverage is analyzed through a new approach. The reported results show that this threshold is critically
related to the asymmetry and the tail heaviness in the underlying distribution.