Suchergebnisse

Despite seven decades of development of the European Union project, on 23 June 2016, the United Kingdom, Europe and the rest of the world were surprised when the Leave campaign won the Brexit referendum, offering an extraordinary case study for researchers. We spatially disaggregate the vote share data, which allows us to explore where anti-European sentiment took root in the UK and why. In this paper, a spatial dependence model is applied to clarify and quantify the relevance of the different dimensions - demographic, cultural/educational and economic - that play a role in explaining the rise of support for the Leave campaign. The analysis is conducted at the local level, using local authorities (LAs) as the spatial unit of analysis due to the combination of official datasets with newly generated data in the context of an EU H2020 project. A new indicator capturing the affluence of each local area relative to its close neighbours is proposed and included in the model. In general, we observe that most of the main conclusions obtained by large regions or at the national level also hold at the local scale. However, it is particularly interesting that inequalities by LAs are clearly significant, indicating a marked influence on voters' decisions that have thus far been unaccounted for. This result provides further support for the existence of, to use Andrés Rodriguez-Pose's terminology, an even more intense "revenge of the places that do not matter" at the local scale.

AbstractIn the 2000s, and with natural population growth rates close to zero, Spain experienced an inflow of almost 5 million immigrants. These new Spanish residents did not tend to locate evenly across the territory and contributed to putting pressure on the already large spatial population imbalances between cities and rural areas, between the coast and the interior and between the central and peripheral areas. Covering the whole Spanish territory and using the 804 Spanish local labour markets as units of analysis, the objective of this paper is to analyse the local determinants of the attractiveness of a place for immigrants. The estimation of geographically weighted regressions (GWR) shows the necessity of including spatial heterogeneity in the analysis, as the effect of the traditional factors explaining immigrant concentration processes—such as job‐related characteristics or the networks established by previous immigrants—exert different and even contrary effects across the Spanish territory. Although global estimations would reject the significance of agglomeration economies or the relative location of a place, the adoption of the GWR approach shows that these regional economic factors are key to understanding the geographical concentration of immigrants in Spain but show different—even contrary—responses across space. Spatial heterogeneity supports the idea that any national/regional policy to enhance (or contain) immigrant concentration should be designed a la carte and be implemented at the local level.

AbstractSpain is an ageing country, and the present demographic burden is not homogeneously distributed across space. Will the aged population be evenly distributed in the future, or will disparities broaden over time? Identifying the spatial patterns of the aged population concentration and the existence of a demographic burden convergence/divergence process is a pertinent question in Spain after the regional devolution the country has undergone in the last decades and the coexistence of different regimes. In this paper, we use a non‐parametric approach (geographically weighted regressions) to identify the determinants of the ageing dynamics, checking for the existence of a convergence/divergence ageing process after controlling for the socio‐economic characteristics of the Spanish municipalities. Although global estimations support the convergence hypotheses posed, GWR results show a significant variability of the effects depending on the area considered, which calls for a careful treatment of the results both for analysis and policy purposes.