Beyond GDP: an analysis of the socio-economic diversity of European regions

Abstract

This paper aims to analyze the socioeconomic diversity of European Union (EU-28) regions from a dynamic perspective. For that purpose, we combine a series of exploratory space-time analysis approaches to multiple Factor Analysis (MFA) applied to a large range of indicators collected at the NUTS-2 level for the period 2000-2015 for the EU-28. First, we find that the first factor of MFA, interpreted as economic development (ECO-DEV), is spatially clustered and that a moderate convergence process is at work between European regions from 2000 to 2015.Second, when comparing these results with those obtained for GDP per capita, we show that the convergence pattern detected with GDP per capita is more pronounced: ECO-DEV adjusts slower over time compared to GDP per capita. Third, pictures provided by the remaining interesting factors, capturing educational attainment, population dynamics and employment, are very different.

Local Dimensions of Regional Income Inequalities in the 2010s - Geographical Proximity Based Experiences from Hungary

“…mobilities based on local neighbourhood effects result in income convergence clubs. Evidence for the creation of convergence clubs can be found at a higher sub-national level (Le Gallo, 2001;Rey, 2001;Ayouba & Le Gallo, 2019), but here you can also find results at the local level (district, settlement) related to East-Central Europe (Stankov & Dragićević, 2015;Kozera, Głowicka & Wołoszyn, 2017;Török & Benedek, 2018;Netrdová & Nosek, 2020;Ręklewski, 2022).…”

“…Le Gallo (2001) points out that areas close to higher-income regions have a greater chance of moving up (catch-up) within the income distribution, while in the case of poor neighbours, falling behind is typical. Several authors have used global and local spatial autocorrelation methods (Moran I, Getis Ord Gi*) and Markov chain analysis based on spatial categories to Egri, Z. highlight the local persistence or mobility of the spatial income situation in the EU, the US and Mexico (Le Gallo & Ertur, 2000;Fischer & Stirbock, 2006;Gutiérrez & Rey, 2013;Le Gallo & Fingleton, 2013;Smetkowski, 2014;Ayouba & Le Gallo, 2019). The results all showed marked center-periphery relations in the examined spaces.…”

Inclusive growth, public transit infrastructure investments and neighbourhood trajectories of inequality in Montreal

“…In order to examine changes in the spatial configurations of neighbourhood incomes across the city, we use directional LISAs. Originally developed by Rey et al (2011), directional LISAs have been widely adopted to study the dynamics of regional patterns of inequality (Ayouba et al, 2020; Breau et al, 2020; Fiaschi et al, 2018; He et al, 2017; Sastré Gutiérrez and Rey, 2013). Compared to most existing empirical studies of income disparities, which are typically aspatial and static (see Morrill, 1991 for an excellent discussion of this), directional LISAs allow researchers to incorporate the dynamic aspects of changes in income occurring over time as well as across space.…”

Spatial Diffusion of Civil Liberty

“…We continue the exploratory analysis with a view of the spatial dependence and its dynamics by means of the Directional LISA (Local Indicator of Spatial Autocorrelation), which captures the co-movements of countries and neighbors graphically across the Moran scatterplot. 3 This method visualizes these co-movements by means of the origin-standardized movement vector, obtained by comparing two Moran scatterplots corresponding to two different periods of time, in our case, the first and last period of analysis (Ayouba et al, 2020). 3 The Moran scatterplot consists of four quadrants reporting different types of spatial association between a country's ICL index and that of its neighbors.…”