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

ABSTRACT

This paper aims to analyze the socioeconomic diversity of the 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 Gross Domestic Product (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.

KEYWORDS:

• Spatial autocorrelation
• European regions
• Exploratory space-time analysis
• Multiple factor analysis

JEL CLASSIFICATION:

• C12
• O18
• R12

Acknowledgments

We thank V. Larmet for excellent research assistance. We also thank E. Elguezabal, G. Lafferté, J. Mischi, R. Sinthon, L. Védrine and the participants of the research seminar held in Saint Etienne on 14 February 2019 for useful comments.

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary material

Supplemental data for this article can be accessed here.

Notes

¹ We replace however the service employment variable by the following more disaggregated ones: emp_trad, emp_fin, and emp_adm. Moreover, we use an additional sectoral employment variable: emp_cons.

² For variables with a limited number of missing values, we made some adjustments presented in Table A1 in the appendix.

³ neet_fem (resp. neet_mal) represents the share of young female (resp. male) people (population ages 15–24) who are not in employment, education or training, as a percentage of the total number of young female (resp. male) people. neet_tot is the indicator computed without sex consideration.

⁴ NUTS stands for Nomenclature of Territorial Units for Statistics used by Eurostat. NUTS-2 refers to Basic Administrative Units and is the level at which eligibility to support from cohesion policy is determined.

⁵ We remove the remote French island Mayotte.

⁶ The method has been introduced in Escofier and Pages (1983, 1994). For an extensive and comprehensive review, see Pagès (2014) and Abdi, Williams, and Valentin (2013).

⁷ For each region, the variables are grouped by time, from 2000 to 2015, i.e. there are 16 groups. For the first group Year-2000, variables are ordered as in Table 1.

⁸ For example, to connect Greece to Italy.

⁹ The four quadrants of the Moran scatterplot report different types of spatial association between a region’s ECO-DEV and that of its neighbours. In the first quadrant are located developed regions (regions with a relatively high ECO-DEV), neighbored by similar regions (‘High-High’ or HH). Quadrant two contains regions with relatively low ECO-DEV with developed neighbours (‘Low-High’ or LH), while quadrant three contains regions with a relatively low ECO-DEV with similar neighbours (‘Low-Low’ or LL). Finally, in quadrant four are located developed regions neighbored by regions with a relatively low ECO-DEV (‘High-Low’ or HL).

¹⁰ In total, more than 96.42% of the regions in high-high clusters in 2000 remain in the same cluster in 2015. In addition, 7.32% of the regions belonging to the non-significant cluster in 2000 move in the significant high-high cluster in 2015, amplifying the spatial association of high-high regions.

¹¹ More than 87% of the regions in low-low clusters in 2000 remain in the same cluster in 2015. In addition, 11.38% of the regions belonging to the non-significant cluster in 2000 move in the low-low cluster in 2015, amplifying the spatial association of low-low regions.

¹² This statistic ($\delta$) is calculated as follows (see Rey (2001) for more details): $\delta =(k-{\sum }_i{P}_{ii})/(k-1)$, where $P_{ii}$ is the diagonal element of the LISA Markov transition matrix $P$ and $k$ the number of total classes. With no inter-class transitions, $\delta =0$, and the more the inter-class mobility, the larger $\delta$. The maximum value is $k/(k-1)$.

Additional information

Funding

This work was funded by the European Union's Horizon H2020 Research and Innovation programme under grant agreement No 726950 - IMAJINE (Integrative Mechanisms for Addressing Spatial Justice and Territorial Inequalities in Europe).