This paper investigates the relationship between economic growth and regional income inequality in a spatial econometric perspective. The role of space in the measure of inequality is discussed, and a new theoretical model that relates inequality with economic growth is introduced. The proposed model extends a spatial Mankiw–Romer–Weil specification by introducing regional income inequality as a determinant of economic growth. The measure of inequality proposed as a covariate in the model is derived by a spatial decomposition of the Gini index. An empirical analysis focused on European Union NUTS-2 regions is carried out to illustrate the model.

This paper investigates the role of spatial dependence, spatial heterogeneity and spatial scale in principal component analysis for geographically distributed data. It considers spatial heterogeneity by adopting geographically weighted principal component analysis at a fine spatial resolution. Moreover, it focuses on dependence by introducing a novel approach based on spatial filtering. These methods are applied in order to derive a composite indicator of socioeconomic deprivation in the Italian province of Rome while considering two spatial scales: municipalities and localities. The results show that considering spatial information uncovers a range of issues, including neighbourhood effects, which are useful in order to improve local policies.

Traditional inequality measures fail to capture the geographical distribution of income. The failure to consider such distribution implies that, holding income constant, different spatial patterns provide the same inequality measure. This property is referred to as anonymity and presents an interesting question about the relationship between inequality and space. Particularly, spatial dependence could play an important role in shaping the geographical distribution of income and could be usefully incorporated into inequality measures. Following this idea, this paper introduces a new measure that facilitates the assessment of the relative contribution of spatial patterns to overall inequality. The proposed index is based on the Gini correlation measure and accounts for both inequality and spatial autocorrelation. Unlike most of the spatially based income inequality measures proposed in the literature, our index introduces regional importance weighting in the analysis, thereby differentiating the regional contributions to overall inequality. Starting with the proposed measure, a spatial decomposition of the Gini index of inequality for weighted data is also derived. This decomposition permits the identification of the actual extent of regional disparities and the understanding of the interdependences among regional economies. The proposed measure is illustrated by an empirical analysis focused on Italian provinces.