Multidimensional Clustering of EU Regions: A Contribution to Orient Public Policies in Reducing Regional Disparities

Abstract

This paper applies multidimensional clustering of EU-28 regions with regard to their specialisation strategies and socioeconomic characteristics. It builds on an original dataset. Several academic studies discuss the relevant issues to be addressed by innovation and regional development policies, but so far no systematic analysis has linked the different aspects of EU regions research and innovation strategies (RIS3) and their socio-economic characteristics. This paper intends to fill this gap, with the aim to provide clues for more effective regional and innovation policies. In the data set analysed in this paper, the socioeconomic and demographic classification associates each region to one categorical variable (with 19 categories), while the classification of the RIS3 priorities clustering was performed separately on “descriptions” (21 Boolean categories) and “codes” (11 Boolean Categories) of regions’ RIS3. The cluster analysis, implemented on the results of the correspondence analysis on the three sets of categories, returns 9 groups of regions that are similar in terms of priorities and socioeconomic characteristics. Each group has different characteristics that revolve mainly around the concepts of selectivity (group’s ability to represent a category) and homogeneity (similarity in the group with respect to one category) with respect to the different classifications on which the analysis is based. Policy implications showed in this paper are discussed as a contribution to the current debate on post-2020 European Cohesion Policy, which aims at orienting public policies toward the reduction of regional disparities and to the enhance complementarities and synergies within macro-regions.

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Notes

  1. 1.

    Since 2009, four macro-regions have been implemented: EUSBSR, for the Baltic Sea Region (2009); EUSDR, for the Danube Region (2011); EUSAIR, for the Adriatic and Ionian Region (2014); EUSALP, for the Alpine Region (2015). They comprehensively involve 19 EU Member States and 8 non-EU countries, also with some territorial overlaps (European Commission 2016).

  2. 2.

    “The long-term impact of implementation of smart specialisation strategies in terms of increased innovation, job creation and improved productivity will require a number of years and will be examined as part of the ongoing and ex-post evaluation of Cohesion Policy programmes” (European Commission 2017, p. 19).

  3. 3.

    Gianelle and Guzzo (2017) present a preliminary analysis on Italy and Poland, grounded on an expert classification of RIS3 priorities.

  4. 4.

    https://www.interregeurope.eu/.

  5. 5.

    http://www.interact-eu.net/.

  6. 6.

    https://ec.europa.eu/regional_policy/it/policy/cooperation/macro-regional-strategies/.

  7. 7.

    Example of national fora is the FONA project, in Germany, on sustainable science, technology and innovation for a sustainable society (www.fona.de).

  8. 8.

    Data are available online at http://hdl.handle.net/11380/1177861, https://doi.org/10.25431/11380_1177861.

  9. 9.

    Dataset downloaded on 1st October 2018 from Eye@RIS3 platform, EC-JRC.

  10. 10.

    Available at https://www.alpine-region.eu/actions/mapping-eusalp-regions-governance-concerning-ri-sector.

  11. 11.

    Among the planes generated by the pairs of factorial axes, the one identified by the first two has the most relevant share of the overall inertia and therefore reproduces with less distortion the actual distances between the points of the cloud.

  12. 12.

    In general, in a correspondence analysis of a medium-large matrix, such as the one under analysis, the rate of inertia is always very low, then it allows the ranking of the factors but it is not very effective in guiding the selection of the number of factors to be considered for the clustering procedure. Histogram of the percentage of inertia of the first 50 factors is plotted in “Appendix 2”.

  13. 13.

    For each group, the percentage values indicate its relative weight, in terms of the number of categories.

  14. 14.

    Test-value for qualitative categorical variable is a statistical criterion associated with the comparison of two portions within the framework of a hypergeometric law. The test-value = 2.1 corresponds to a bilateral test probability α/2 of less than 2.5%.

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Acknowledgements

This work is part of the Work Package Nr: T-3 “Enhancing shared Alpine Governance project” of the Project “Implementing Alpine Governance Mechanism of the European Strategy for the Alpine Region” (AlpGov) of the Interreg Alpine Space Programme—Priority 4 (Well-Governed Alpine Space) (Grant No. MIN000510A15), SO4.1 (Increase the application of multilevel and transnational governance in the Alpine Space). A preliminary version of this paper has been presented at the workshop “Promoting open innovation in the EUSALP macro-region: experiences from the Alpine regions”, organised by Action Group 1 in the Eusalp Annual Forum, 21st November 2018, Innsbruck, Austria. The authors wish to thank the participants and Mr. Jean-Pierre Halkin, DG Regional and Urban Policy—Head of Unit D.1, for their comments.

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Appendices

Appendix 1

See Table 2.

Table 2 Coordinates of categories referred to socioeconomic classification, priority description classification and priority codes classification, on the first 4 factors. (Color figure online)

Appendix 2

See Fig. 8.

Fig. 8
figure8

Histogram of the percentage inertia of the first 50 factors

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Pavone, P., Pagliacci, F., Russo, M. et al. Multidimensional Clustering of EU Regions: A Contribution to Orient Public Policies in Reducing Regional Disparities. Soc Indic Res (2020). https://doi.org/10.1007/s11205-020-02324-9

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Keywords

  • Regional smart research and innovation strategies
  • Multi-dimensional analysis
  • Clustering
  • European regions
  • Sustainable development

JEL Classification

  • R58
  • Q5
  • C38