Spatial econometric approach to the EU regional employment process
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Spatial econometric approach to the EU regional employment process Andrea Furková1 · Michaela Chocholatá1 Accepted: 13 October 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract This paper deals with the estimation of spatial econometric models of employment rate across 259 NUTS 2 (Nomenclature of Units for Territorial Statistics) regions of the European Union in 2018 regarding different region-specific factors. Since, spatial autocorrelation and spatial heterogeneity often occur jointly, the paper is oriented at verification of two hypotheses. Hypothesis 1 related to the existence of the spatial autocorrelation, i.e., that the regional employment process is not a spatially isolated process, was confirmed. Based on the estimation of Spatial Durbin Model, direct, indirect and total spatial impacts were quantified and verified. The results proved the significant impact of neighbouring regions for GDP and compensation of employees variables in explaining regional employment rate. Significant influence of factors like educational attainment level and population density seems to be limited only to the particular region. Hypothesis 2 reflected the existence of the spatial heterogeneity. Based on the geographically weighted regression the assumption of spatial variability of the model parameters was also verified. The regional employment in the EU seems to be affected by both spatial effects and the presented approaches thus represent two different insights into the complex spatial character of the modelled process. Keywords Regional employment rate · Spatial interactions · Spatial Durbin model (SDM) · Geographically weighted regression (GWR)
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Andrea Furková [email protected] Michaela Chocholatá [email protected]
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Department of Operations Research and Econometrics, University of Economics in Bratislava, Dolnozemská cesta 1, 852 35 Bratislava, Slovakia
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A. Furková, M. Chocholatá
1 Introduction The issue of employment has been attracting both the politicians and the researchers for decades. One of the EU’s (European Union) priorities declared in the strategic document Europa 2020 (European Commission 2010) is to promote a high-employment economy and to achieve a target of 75% employment rate of the population aged 20–64 by 2020. These ideas are also followed by a new EU cohesion policy framework for 2021–2027 (European Commission 2018). To face the problem of demographic change as well as to improve the position in the global competition, the EU needs to increase the labour force participation in order to deliver the economic, social and territorial cohesion (European Commission 2010). Plenty of studies have been published to analyse the regional (un)employment rates across various groups of EU regions considering different region-specific factors and using different methodological and empirical scope. The level of employment in a region is determined not only by individual region-specific factors, but its location in space plays a crucial role as well. Consideration of t
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