Abstract
Europe is in a phase of vast transition regarded both from a demographic and economic-structural point of view. Studies have shown that demographic development differs a lot when comparing urban regions with more sparsely populated peripheral regions. These diverging patterns are shown to be especially strong in the northern and eastern parts of Europe where a redistribution of people contributes to a concentration process to the metropolitan or big city areas as well as to shrinkage and depopulating of rural and peripheral areas. This paper empirically addresses these differing demographic development paths by analyzing the influence of key underlying demographic factors on population change across European regions. For the stated purpose the paper applies typologies based on both economic and demographic structure and a cross-regional regression model. The economic-structural typology developed within the ESPON/EDORA-project is used to describe and analyze economic-structural factors and a typology based on demographic characteristics that classify regions as either shrinking or expanding in terms of population is used in the empirical assessment. Findings indicate that age structure is of importance with regard to population changes and there exists an east-west divide between the growing west and declining east where the declining sectors are more frequent. It is also shown that large and densely populated regions have better preconditions for growth and fewer risks for shrinking than small and sparsely populated ones.
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Notes
- 1.
ESPON is a research programme that started 2002 and named European Spatial Planning Observatory Network. It changed name 2007 to European Observation Network for Territorial Development and Cohesion. EDORA (European Development Opportunities in Rural Areas) was an ESPON project that was running between 2008 and 2010. This article is not an EDORA project but we use the EDORA typology for the classification of differing regions within the ESPON Space, i.e. the EU plus Iceland, Liechtenstein, Norway and Switzerland but here minus Denmark and Croatia, which was excluded as a consequence of data shortage.
- 2.
If the condition number is less than 100, there is no serious problem with multicollinearity, while condition numbers between 100 and 1000 reflects moderate to strong multicollinearity.
- 3.
The multicollinearity indicated by bivariate correlations is low and the estimates are robust with regards to their signs and magnitude.
- 4.
A variable that reflects fertility rate (as discussed in the theoretical section) has been included in the preliminary analyses but removed because of endogeneity issues. Nevertheless, results are robust when this variable is included.
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Appendix: Correlation Matrices
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Johansson, M., Nilsson, P., Westlund, H. (2018). Migration and Ageing in Expanding and Shrinking European Regions. In: R. Stough, R., Kourtit, K., Nijkamp, P., Blien, U. (eds) Modelling Aging and Migration Effects on Spatial Labor Markets. Advances in Spatial Science. Springer, Cham. https://doi.org/10.1007/978-3-319-68563-2_7
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