The analysis of employment rates in the context of spatial connectivity of the EU regions

Main Article Content

Michaela Chocholatá
Andrea Furková


Research background: The main objective of this paper is to analyse the employment rates in the context of spatial connectivity of the EU regions. Employment rate is declared as one of the important indicators of the strategic document Europe 2020. The achievement of high levels of employment in individual regions plays therefore an important role.

Purpose of the article: The aim of the paper is to verify the possible spill-over effects within the EU regions and their territorial interconnection in the context of employment rates.

Methods: Analysis is based on tools of the Exploratory Spatial Data Analysis (ESDA) to consider spatial connectivity of the EU regions.

Findings & Value added: The results show that the statistically significant clusters of regions with high employment rates are situated mainly in the central, northern and north-western part of the EU while the clusters with low values are located mainly in Greece, Spain, Italy, Portugal, Bulgaria, Romania and some French regions.

Article Details

How to Cite
Chocholatá, M., & Furková, A. (2018). The analysis of employment rates in the context of spatial connectivity of the EU regions. Equilibrium. Quarterly Journal of Economics and Economic Policy, 13(2), 181-213.


Anselin, L. (1995). Local indicators of spatial association — LISA. Geographical Analysis, 27(2). doi: 10.1111/j.1538-4632.1995.tb00338.x.
Balcerzak, A. P. (2015). Europe 2020 strategy and structural diversity between old and new member states. Application of zero-unitarization method for dynamic analysis in the years 2004–2013. Economics & Sociology, 8(2). doi: 10.14254/2071-789X.2015/8-2/14.
Barca, F., McCann, P. & Rodríguez–Pose, A. (2012). The case for regional development intervention: place-based versus place-neutral approaches. Journal of Regional Science, 52(1).
Bivand, S. (2010). Exploratory spatial data analysis. In M. M. Fischer & A. Getis (Eds.). Handbook of applied spatial analysis. Software tools, methods and applications. Berlin Heidelberg: Springer–Verlag.
Chocholatá, M., & Furková, A. (2017). Does the location and institutional background matter in convergence modelling of the EU regions? Central European Journal of Operations Research, 25(3). doi: 10.1007/s10100-016-0447-6.
Chocholatá, M. (2018). Spatial analysis of the tertiary educational attainment in European Union. In Proceedings of the 15th International Conference Efficiency and Responsibility in Education 2018. Prague.
Cracolici, M. F., Cuffaro, M., & Nijkamp, P. (2009). A spatial analysis on Italian unemployment differences. Statistical Methods and Applications, 18(2). doi: 10.1007/s10260-007-0087-z.
European Commission (2010). Communication from the Commission Europe 2020: a strategy for smart, sustainable and inclusive growth. Retrieved from :EN:PDF (10.02.2016).
Eurostat (2015). Administrative units, statistical units, Eurostat. Retrieved from (15.02.2015).
Eurostat (2016). Statistics by theme. Retrieved from (10.02.2016).
Feldkircher, M. (2006). Regional convergence within the EU-25: a spatial econometric analysis. Retrieved from wirtschaft/Workshopbaende/2006/Workshop-No.-09.html (05.01.2018)
Fischer, M. M., & Wang, J. (2011). Spatial data analysis. Models, methods and techniques. Heidelberg Dordrecht London New York: Springer.
Franzese, R. J. & Hays, J. C. (2005). Spatial econometric modeling, with application to employment spillovers and active-labor-market policies in the European Union. Retrieved from FranzeseHays.SpatialEcon.EmploymentSpillovers.ALP.pdf (10.08.2017).
Furková, A. (2016). Spatial pattern of innovative activity in the EU regions: exploratory spatial data analysis and spatial econometric approach. In Gomes, O. & H. Martins (Eds.). Advances in applied business research: the L.A.B.S. initiative. New York: Nova Science Publishers.
GeoDa (2015). Home page. Retrieved from /downloads (15.02.2015).
Getis, A. (2010). Spatial autocorrelation. In M. M. Fischer & A. Getis (Eds.). Handbook of applied spatial analysis. Software tools, methods and applications. Berlin Heidelberg: Springer–Verlag.
Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3).
Haining, R. (2003). Spatial data analysis: theory and practice. Cambridge: Cambridge University Press.
Lottmann, F. (2012). Explaining regional unemployment differences in Germany: a spatial panel data analysis. Retrieved from (10.02.2016).
Monastiriotis, V. (2007). Patterns of spatial association and their persistence across socio-economic indicators: the case of the Greek regions. Retrieved from (25.01.2018).
Niebuhr, A. (2003). Spatial interaction and regional unemployment in Europe. European Journal of Spatial Development, 5.
Ord, J. K., & Getis, A. (1995). Local spatial autocorrelation statistics: distributional issues and an application. Geographical Analysis, 27(4). doi: 10.1111/j.1538-4632.1995.tb00912.x.
Pagliacci, F. (2014). Europe 2020 challenges and regional imbalances. A spatial analysis. Retrieved from (05.01.2017).
Pavlyuk, D. (2011). Spatial analysis of regional employment rates in Latvia. Scientific Journal of Riga Technical University, 2.
Perugini, C., & Signorelli, J. (2004). Employment performance and convergence in the European countries and regions. European Journal of Comparative Economics, 1(2).
Pietrzak, M. B. & Balcerzak, A. P. (2016). A spatial SAR model in evaluating influence of entrepreneurship and investments on unemployment in Poland. In M. Reiff & P. Gezik (Eds.). Proceedings of the international scientific conference quantitative methods in economics multiple criteria decision making XVIII. Vratna: Letra Interactive.
Smith, E. T. (2014). Spatial weight matrices. Retrieved from 0WEIGHT%20MATRICES.pdf (04.12.2014).
Viton, P. A. (2010). Notes on spatial econometric models. Retrieved from (04.12.2014).