Abstract

We studied inequalities in the distribution of primary income, or gross wages, in 235 regions, or NUTS-2 level, of the current 27 European Union (EU) Member States, over a period of more than two decades. The opportunity to study gross wage inequality in EU regions over several decades is justified because labor markets are essentially regional in nature due to the specific characteristics of each region, such as a unique workforce composition, regional economic structure, culture, history, and institutions. In turn, statistical information at the regional level is key to accurately modeling economic policies that promote economic and social cohesion, including poverty and inequality reduction. Furthermore, focusing the study of economic inequality on gross wage inequality, rather than other sources of income, is justified for several reasons, such as: (1) inequality between EU countries is decreasing, but inequality within each country is increasing; (2) labour income is the main source of household income in the EU.
Methodologically, we rely on the decomposition of Theil's T statistic for the population, which comprises two components: the between-regions component (Tb) and the within-region component (Tw). Whereas Tb represents the sum of the contributions of each region to total wage inequality in the EU, Tw accounts for total EU regional inequality within the regions, attributed to productive sectors (differences in salaries across sectors). For this analysis, we use data on employee compensation (gross wages) and employment classified by 10 economic activity sectors (NACE Rev. 2) sourced from various Eurostat databases, covering 235 EU regions across the 27 Member States, from 2000 to 2022.
The key outputs of this study include (1) the programming of Theil's T-statistic decomposition in Stata, (2) an open-access, homogeneous, and reliable dataset of gross wage inequality indicators at regional level, (3) the development of comprehensive graphical analyses to present our results, and (4) the estimation of regression models to explore the correlation between wage inequality and socioeconomic factors, using panel data estimators.
Specifically, the results have allowed us to contrast the existence of invisible regional inequalities in the distribution of wages between regions of three types: (1) the productive specialization of regions in sectors with lower wages (for example, agriculture and tourism); (2) the headquarters or capital effect, since large companies that tend to pay higher wages are located in regions that include the country's capital; and (3) the quality of institutions, since regions with less efficient institutions converge more slowly in income levels.