Roberto Zelli

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.

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.

Income distribution in Italy: A nonparametric analysis

Using kernel density estimation, this paper describes the real income distribution and how it evolved over time in Italy. Data are cross-sectional samples from the population of Italian households during the period 1987–1998. A non parametric test is applied to asses whether the observed changes in the distribution are statistically significant, while the presence of more than one mode in the distributions is investigated by a bootstrap test. Empirical results show that the Italian income distribution significantly changed over time, accompanied by a decreasing inequality pattern until 1991 and a subsequent rise. No marked income gains were perceived, while the real “losers” of the decade seem to be households in the middle-upper income range.

Mixture models and convergence clubs

In this paper we argue that modelling 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 have more power than bump hunting tests and includes a natural method of assessing the cross-component immobility necessary to imply a correspondence between components and convergence clubs. Applying the mixture approach to cross-country per capita income data for the period 1960 to 2000 we find evidence of three component densities in each of the nine years that we examine. 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. We 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. This increased concentration as well as that of the poor countries around their component mean produces a rise in polarization in the distribution over the sample period.

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.

The Make-Up of a Regression Coefficient: An Application to Gender

In this paper we illustrate the potential use of an old/new methodology which combines the use of concentration curves in order to investigate the components that make up a regression coefficient. The illustration is based on examining gender differences in the effect of age on labor market participation in Italy. Women participation rate is substantially lower than men, but their age profile is similar. The most striking difference is in terms of hours of work: while Italian men increase their work effort until the age of 35, Italian women reduce it until the age of 39. These results do not differ substantially when we split the working population into employed and self-employed. Earnings increase with age for both men and women, however the local regression coefficient is negative for Italian women in the age of 38–42. This evidence is accentuated when we focus on the employees.

Does Regional Cost-of-Living Reshuffle Italian Income Distribution?

This paper examines how spatial price differentials affect income distribution in Italy. The distribution of household income is “reshuffled” after controlling for the purchasing power of households residents in different regions, but only when housing price variations are included in the PPP index. Poor households living in Southern Italy alleviate their relative condition, but concentration of poverty still holds in the Southern part of the country.

SEASONALITY, PRODUCTIVITY SHOCKS, AND SECTORAL COMOVEMENTS IN A REAL BUSINESS CYCLE MODEL FOR ITALY

This paper investigates the degree of comovements in quarterly Italian time series of sectoral output. A recently developed multivariate technique for the empirical analysis of long-run, cyclical and seasonal comovements is used in the context of a multisectoral real-business-cycle model augmented with persistent seasonal shocks in productivity. Our empirical results emphasize the role of input–output relations in the propagation mechanism and indicate that sectoral outputs have a relatively low number of common stochastic trends, in conflict with the hypothesis of independent productivity shocks. In contrast, stochastic seasonals seem to move idiosyncratically. Furthermore, our findings suggest that the theoretical model should be extended to allow for deterministic seasonal shifts in preferences.

Measuring the Middle Class in Kazakhstan : A Subjective Approach

This paper proposes a model-based approach to estimate income boundaries for identifying the middle class in Kazakhstan over 2003-15. The approach exploits the subjective evaluation of Kazakhstan households about their social status, relating self-declared social class membership to income. Income data come from the Kazakhstan Household Budget Survey, which also includes a specific module on quality of life and perceived social status. As social status is intrinsically an ordinal response, the paper estimates a proportional odds model with income as the key explanatory variable. Although other factors influence the self-perception of being in the middle class, income is by far the most important determinant. Benchmarking on 2013, the estimated middle class lower bound is