Maria Grazia Pittau

MIXTURE MODELS, CONVERGENCE CLUBS, AND POLARIZATION

We argue that modeling the cross-country distribution of per capita income as a mixture distribution provides a natural framework for the detection of convergence clubs. The framework yields tests for the number of component distributions that are likely to be more informative than “bump hunting” tests and includes a method of assessing the cross-component immobility necessary to imply a correspondence between components and convergence clubs. Applying this approach to Penn World Data for the period 1960 to 2000 we find evidence of three component densities. We find little cross-component mobility and so interpret the multiple mixture components as representing convergence clubs. We document a pronounced tendency for the strength of the bonds between countries and clubs to increase and show that the well-known “hollowing out” of the middle of the distribution is largely attributable to the increased concentration of the rich countries around their component means.

Poverty and inequality in European regions

The European Union Statistics on Income and Living Conditions (EU-SILC) is the main source of information about poverty and economic inequality in the member states of the European Union. The sample sizes of its annual national surveys are sufficient for reliable estimation at the national level but not for inferences at the sub-national level, failing to respond to a rising demand from policy-makers and local authorities. We provide a comprehensive map of median income, inequality (Gini coefficient and Lorenz curve) and poverty (poverty rates) based on the equivalised household income in the countries in which the EU-SILC is conducted. We study the distribution of income of households (pro-rated to its members), not merely its median (or mean), because we regard its dispersion and frequency of lower extremes (relative poverty) as important characteristics. The estimation for the regions with small sample sizes is improved by the small-area methods. The uncertainty of complex nonlinear statistics is assessed by bootstrap. Household-level sampling weights are taken into account in both the estimates and the associated bootstrap standard errors.

Stability of household income in European countries in the 1990s

The patterns of change in the annual household income in the countries of the European Community during the years 1994-1999 are explored. The income is modelled by mixtures of multivariate log-normal distributions, and the mixture components are interpreted as representing one subpopulation with steady increments and others with various levels of volatility. The method is extended to models for a combination of log-normal and categorical variables. An index of income stability is defined for the countries. Throughout, we emphasise graphical summaries of the results.

Hierarchical Modelling of Disparities in Preferences for Redistribution

We evaluate the magnitude of the disparities in the demand for redistribution across European countries and American states during the 2000s. Modelling the demand for redistribution in a multilevel framework, we identify the determinants that contribute the most in predicting support for redistribution. We observe that individual characteristics and contextual variables are associated with demand for redistribution in the same way in Europe and in the US, whereas others exert different influences on the probability of supporting redistribution. We find important differences from some well-established evidence obtained from data collected for the 1980s and the 1990s.

An optimal design for air pollution monitoring network

A new method is presented to determine the optimum number and location of monitoring stations in an air pollution network. The selection of the monitoring stations is based on the spatial representativity of the sites. In particular, the spatial coefficient of the correlation between the pollutant concentrations in different receptors is considered with regard to different meteorological scenarios. The methodology is applied in the province of Venice for two different pollutants: SO2 and NOx. Copyright

Evidence of Convergence Clubs Using Mixture Models

Cross-country economic convergence has been increasingly investigated by finite mixture models. Multiple components in a mixture reflect groups of countries that converge locally. Testing for the number of components is crucial for detecting “convergence clubs.” To assess the number of components of the mixture, we propose a sequential procedure that compares the shape of the hypothesized mixture distribution with the true unknown density, consistently estimated through a kernel estimator. The novelty of our approach is its capability to select the number of components along with a satisfactory fitting of the model. Simulation studies and an empirical application to per capita income distribution across countries testify for the good performance of our approach. A three-clubs convergence seems to emerge.

Diversity in Europe: how European citizens support redistributive policies

Our paper empirically evaluates the magnitude of the disparities across European countries and regions in the demand for redistribution in the 2000s. We identify which are the individual characteristics and the contextual variables, at country and also at regional level, that contribute the most in predicting the observed different support for redistribution. Demand for redistribution is modelled in a multilevel framework that provides a natural and suitable model for accounting different levels of variation, at individual level, at regional level and at country level, simultaneously.

Consensus Classification for A Set of Multiple Time Series

In multiple time series analysis, when there are a very large number of series, a classification into homogeneous clusters might be useful to reduce the problem’s complexity and eliminate possible redundancies (Zani, 1983). Furthermore, when we have different classifications, one for each statistical unit (e. g. spatial units), a consensus classification allows one to obtain a classification which summarizes the given classifications. The present paper focuses on the problem of identifying consensus classifications in a set of multiple time series (panel data), using a consensus method (Vichi, 1993, 1994). First, a distance among time series is defined and a hierarchical classification among time series, for each temporal lag and for each unit, is performed. Then, a consensus classification among different units for the same temporal lag is carried out. Finally, a hierarchical classification among the different consensus classifications, with the same temporal lag, is carried out.