Formal modeling of local population growth has usually tended to focus on identifying patterns that are presumed to hold universally. However, as Glaeser, Ponzetto, and Tobio highlighted, these laws are reliable for long-term dynamics; but in some moments or for some places, the balance between the different factors may change, giving rise to different specific behaviors. In this article, we study local population growth in Spain with no intention of searching for universal patterns. Rather, we are interested in identifying how relevant the temporal and spatial heterogeneity may be, that is, to assess the even and uneven effects that population growth determinants can exert across time and space. The geographically weighted regression (GWR) approach applied in this article for two different decades, 1991–2001 and 2001–2011, captures the spatial heterogeneity. Results on the spatially differentiated population growth factors are compared with the global ordinary least squares (OLS) estimators for both decades. Essential factors in urban and regional economics such as size (initial population) or distance (either to the big cities or to the coast) can have different effects on population growth across both space and time, corresponding to the global estimated effects for some areas but diverging from these in others. Using GWR estimation procedures, we can identify changes in the sign or in the intensity of a factor’s effect across space, such that some factors could enhance population growth in one place but reduce it in another. Only after all spatially differentiated local effects have been analyzed and taken into consideration can appropriate national or regional policies be designed à la carte to promote, retain, or deter population growth.

Spain 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.

In 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.

The spatial concentration of immigrants across and within European countries is highly heterogeneous, tending to reinforce the internal spatial disparities within EU Member States and regions. Although European regional data show that the highest levels of foreign‐born population concentration correspond to those NUTS2 regions that contain a large city or metropolitan area, there are other place‐based determinants that might explain their attractiveness to immigrants. Using a comprehensive database at NUTS2 (regional) and LAU2 (local) levels for three large European countries in terms of immigrant population (Italy, Spain, and France), comparable results show how the relevance of these determinants depends on the country under analysis and the spatial unit chosen. This provides challenges for the design of a common future European policy addressing the unresolved demographic issues. Understanding the main regional and local factors of attraction to foreign‐born population within countries is crucial to explain the present spatial concentration patterns and anticipate future migration flows, especially in a context where immigrants are the fastest‐growing population group in those European countries. Nevertheless, there might be a trade‐off between the foreign‐born population alleviating the territorial ageing and depopulation issues and the search for spatial justice.

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