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.

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.

Since some decades, inequality is attracting a growing interest within the political debate as well as in theoretical and empirical studies. Considering inequality at regional level offers useful insights for policy makers, facilitating the assessment of the effectiveness of strategies aimed at reducing regional disparities and helping in developing place-based actions. The study of regional inequality poses some relevant issues related to the spatial nature of data. In fact, dealing with georeferenced data implies the opportunity of considering the spatial interactions among regional units that are likely to play a role in shaping the inequality dynamics. Some studies highlighted the importance of incorporating spatial effects in a traditional measure of inequality such as the Gini index. These studies are based on the definition of a proximity structure, that allows one to discriminate between the spatial and the non-spatial component of inequality. Different definitions of the proximity structure are likely to influence the spatial component of inequality. Those aspects are analysed in the present paper to offer more detailed insight in the territorial dimension of inequality. The measures and their decompositions are discussed in the case of European NUTS 3 regions.

The topic of economic convergence is still crucial in the European Union (EU) as promoting regional growth and the reduction of disparities remains a key objective. In this paper we investigate development and economic growth across EU regions. Particular attention is given to σ-convergence and β-convergence. These analyses are carried out for regional units corresponding to the third level of the NUTS (Nomenclature of Territorial Units for Statistics) classification. Focusing on a refined geographical scale could offer a detailed picture of the regional growth dynamics within the EU. Additionally, we use a spatial augmented version of the conditional β-convergence model to take into account the spatial interdependencies among regions. Findings shed light on the impact of spatial interaction effects and on the need of territorial policies to achieve convergence in the EU. This aspect highlights how coordinating the regional development policies between regions is pivotal to achieve economic, as well as social and political stability within the EU.

Spatial interactions among regional units may influence the geographical distribution of economic activities. Many traditional measures of geographical concentration fail in capturing this aspect, being insensitive to permutations of the spatial position of regions. This paper proposes an approach to the measurement of geographical concentration of economic activities that accounts for spatial interactions among regions. The locational Gini is split into spatial and non-spatial components, so that a new interpretation of the index is presented. The measure is applied to evaluate the geographical concentration of different economic sectors for 1,323 NUTS 3 regions in the European Union over the period 2001–2018.

Composite indicators are often used to assess the structure of urban deprivation to promote sustainable development. However, the refined spatial scale of analysis poses problems related to data availability. In this paper, we define a spatial deprivation index in the Province of Milan, using both census data and areal interpolation. Disaggregation methods are applied to obtain variables at lower spatial level, and a geographically weighted principal component analysis is applied to measure socio‐economic deprivation at local level. Components of deprivation are investigated in their spatial structure and some policy implications deriving from the application of a spatial approach are discussed.